2018 - tata institute of fundamental research

School ofTechnology

andComputer

ScienceReview

June

2018

Contents

Preface 5

1 STCS: Origins, growth and plans for the future 7

2 Members of the Department 13

3 Algorithms 65

4 Applied Probability 75

5 Computational Complexity 91

6 Formal Methods 117

7 Information Theory and Communications 129

8 Teaching 143

9 Publications from the last ten years 151

10 Workshops Organised 189

11 Former Members of the School 193

12 Grants and Projects 201

Preface

This report on the School of Technology and Computer Science (STCS) was prepared for the review andevaluation of the school’s activities to be conducted by the following committee of eminent scientists.

• Prof. Gérard Huet, INRIA, France

• Prof. Ravi Kannan, Microsoft Research India (Committee Chair)

• Prof. PR Kumar, Texas A&M University

• Prof. Sriram Rajamani, Microsoft Research India

• Prof. Leonard Schulman, Caltech

On behalf of the school and the institute, I welcome the members of the committee, who will visitthe school in the period 5–6 June 2018.

The report focuses mainly on the activities of the school over the last ten years. The introductionsummarises the early computational activities at TIFR, outlining the leading role played by TIFR in de-velopment of computer science in India. These early developments provide the context for the foundingof the school in 1997.

The school has since grown steadily and is today recognised internationally as a leading centre forresearch in areas such as algorithms, communications, complexity theory, financial mathematics, formalmethods, information theory and machine learning.

STCS today has a dynamic and relatively young faculty working in diverse and upcoming areas ofquantitative research in computer and systems sciences. Our common quantitative and conceptual focusunifies us. The school is unique in India in providing research scholars a well-rounded and intensetraining in discrete as well as continuous mathematics increasingly crucial for conducting impactfulresearch.

I thank all the members of the school for contributing to this report. I am particularly grateful toJohn Barretto, Prerona Chatterjee, Arkadev Chattopadhyay, Supriya Pottipati, Jaikumar Radhakrish-nan, Ramprasad Saptharishi, Piyush Srivastava and Anamay Tengse for their efforts in collecting theinformation and preparing this report.

May 30, 2018 Sandeep K JunejaProfessor and Dean

School of Technology and Computer ScienceTata Institute of Fundamental Research

The School of Technology and Computer Science (STCS)Origins, growth, and plans for the future

Early computational efforts at TIFR

TIFRAC

TIFRAC being named by PrimeMinister Jawaharlal Nehru, 1962

CRT external display in TIFRAC

The importance of computing to scientific research was recognisedearly at TIFR. The John von Neumann report on the EDVAC (1945)was closely studied by members at TIFR, and the institute embarkedon a project to build a computer in 1955 under the guidance of Prof. RNarasimhan. By 1959 a fully functional digital computer was ready;the computer was later named TIFR Automatic Calculator (TIFRAC)when the new building of the institute was inaugurated. It was usedextensively for early scientific computations, both by members ofTIFR and scientists from other institutions. Much innovation wasundertaken in various design aspects, such as arithmetic and control,magnetic memory, text and graphic display, power supply and soft-ware. Although its initial design was not far behind world standardsin 1957, the computer was soon left behind as a first generation ma-chine because computer technology surged ahead internationally. In1962, TIFR decided to import the CDC 3600 machine manufacturedby Control Data Corporation. With this TIFR became a national com-putation centre and led the efforts to provide software education andtraining.

In the 1970s, the Electronics Commission was set up by the Gov-ernment of India, and through its initiative the National Centre forSoftware Development and Computer Technology (NCSDCT) wasset up at TIFR. This led to the creation of expertise in-house forSoftware as well as Hardware. Research in graphics, compilers, filesystems, distributed programming, hardware controllers, etc., wascarried out under this project. The hardware expertise also enabledTIFR to help start the Computer Maintenance Corporation (CMC)Ltd. in 1975. Early experiments in networking digital computerswere also undertaken at TIFR. Collaboration between NCSDCT, theSpace Application Centre (SAC) in Ahmedabad and Telecommuni-cation Centre (TRC) in Delhi resulted in an experimental satellitebased communication network COMNEX which started operationsin 1982. These efforts ultimately culminated in setting up of Indianeducational and research network ernet. In 1985, NCSDCT trans-formed itself into an autonomous institution called National Centre

stcs: origins, growth and plans for the future

for Software Technology (NCST) headed by Prof. S Ramani, draw-ing its staff largely from TIFR. One of the lasting contributions fromNCSDCT to the emerging efforts at Computer Science Research wasthe setting up in 1981 of the annual conference on Foundations ofSoftware Technology and Theoretical Computer Science (FST&TCS);the conference has run annually uninterrupted to this day.

Proceedings of the first FST&TCS

TIFR had by the mid-1970s come to be recognised as a centre forplanning and executing projects of national importance. Several pio-neering projects were undertaken under the leadership of Prof. PVSRao and Prof. MV Pitke. These activities continued well into the1990s.

Air Defence Ground Environment Systems (ADGES, 1971-85): Multipleorganisations, ECIL, Indian Air Force, Department of Electronics, De-partment of Atomic Energy, were involved in this project, with TIFRas the central hub. This rugged system was produced by ECIL andwas deployed by the Indian Air Force for intrusion detection alongthe borders.

CDOT exchanges and CDAC supercomputers: In the mid-1970s, a teamat TIFR designed and implemented an advanced telephone switchingnetwork for military applications under its project for Army RadioEngineering Network (AREN). Based on this experience, membersfrom TIFR joined a Centre for Development of Telematics (C-DOT)set up in 1984 by Government of India for public telephony. Thedevelopment of exchanges by C-DOT is considered one of the mostsuccessful technology development efforts in the country. This ush-ered in the telecom revolution in India. India’s supercomputing ef-In short, the move from analog to digital

technology for several applications in thecountry would not have taken place with-out the leadership that TIFR provided.

– FC Kohli, first CEO, Tata Consul-tancy Services (TCS)

forts similarly tapped into expertise available at TIFR in the develop-ment of supercomputers by the Centre for Development of AdvancedComputing (C-DAC) in the early 1990s.

Speech Research and Language Processing: Early work (1966-71) onspeech synthesis, recognition and perception established TIFR as acentre for significant work in this interdisciplinary area. TIFR be-came a nodal centre for speech research under UNDP’s and DoE’sproject on Knowledge Based Computer Systems (1985-99). This ef-fort included the VOICE project, whose objective was to develop aninput-output system for a computer with a facility for visual andvoice feedback. Related work on script synthesis and recognition forIndian languages was carried out with considerable success. Inves-tigations in knowledge based systems and models of child languageacquisition were carried out. Language processing efforts continueat TIFR, with recent focus on topics such phonetics and phonemicsof Assamese, Bodo, Marathi, Manipuri and Punjabi, speech basedaccess to markets and prosody for Indian languages and bio-signals.

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growth of conceptual research

Growth of conceptual research

In the 1970s and 80s TIFR was one of the main centres on comput-ing technologies, with prominent international connections. Under aUnited Nations grant, leading experts from around the world wereinvited to run schools and give talks on recent advances. Startingfrom the 1960s, TIFR hosted a stream of distinguished visitors suchas

Krzysztof Apt, ENL Beale, Gérard Berry, Meurig Beynon, Peter Bune-man, Alex Chandra, Zhou Chaochen, Philippe Flajolet, Nissim Francez,Christiane Frougny, David Gries, Juris Hartmanis, Tony Hoare, JohnHopcroft, Gerard Huet, Anita Jones, Aravind Joshi, Deepak Kapur,Zvi Kohavi, Zohar Manna, Jim Morris, KT Narayana, Roger Need-ham, Maurice Nivat, Rohit Parikh, Michael Rabin, IV Ramakrishnan,Mireille Regnier, Lawrence Rowe, Jerry Saltzer, Hanan Samet, BillScherlis, Neelam Soundararajan, Michael Stonebraker, PS Thiagarajanand William Wulf.

A generation of students found research problems from these inter-actions.

Simultaneously, led by Profs. Mathai Joseph and RK Shyamasun-dar, considerable effort was invested in developing and extendingstate-of-the-art computer systems and systems software. These ef-forts led to significant contributions in the areas of VLSI algorithms,synthesis of distributed programs, logic, computational geometry,and concurrency. The semantics of logic programming, real-time andconcurrent programming languages was a significant focus, with rig-orous investigations in negation-as-failure and the Max-Par seman-tics models. Notable highly cited results from this period include thederivation of necessary and sufficient conditions for feasible schedul-ing by priority assignment and algorithms for visibility polygons,which were obtained in the late 1980s.

Book published on the occasion ofHomi Bhabha’s birth centenary, Oxford

University Press, 2011

In the late 1980s, TIFR had a modest faculty strength of fourpermanent members working in Theoretical Computer Science, butthere was a steady stream of motivated graduate students. Throughtheir combined efforts, since the late 1980s, TIFR started being recog-nised for its research that appeared regularly in well-recognised forain the areas of algorithms, computational geometry, computationalcomplexity theory, concurrency, timed automata and logics, type-theory etc. By the mid-1990s, the focus of the combined computerscience activity shifted from project driven research to fundamentalconceptual research, with roughly equal emphasis on formal meth-ods, algorithms and complexity. When an institute-wide review wasconducted by a committee (known in TIFR as the Porter commit-tee), the activities of the Computer Science Groups were also eval-uated. Based on the recommendation of the committee, and otherexigencies, it was decided move the Computer Science related activ-ity, which until then was part of the School of Physics, to a separateschool.

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stcs: origins, growth and plans for the future

The new school

1997 2002 2007 2012 2017

0

2

4

6

8

10

12

Faculty strength by area (1997 - 2017)

CSSystemsSpeech and Signal Processing

In its meetings in August and September 1997, the TIFR Councilof Management considered several proposals for reorganising theactivities of the Institute into Schools. The Council decided that theactivities related to Computer Science at TIFR were to be carriedout in a new school. The formation of the School of Technology andComputer Science (STCS) recognised the solid research base requiredfor emerging technologies while it also provided greater room for theexpansion of its current theoretical activities.

Soon after the formation of the school, distinguished senior mem-bers joined its faculty. In 1997, Prof. Narendra Karamarkar movedto India from the US with the aim of establishing a centre for com-putational mathematics. A Computational Mathematics Lab (CML)was established; it initially operated in Pune under STCS, but therewere no strong links between the two. The lab was closed whenProf. Karmarkar left the institute in 2006.

1997 2002 2007 2012 20170

5

10

15

20

25

Number of research scholars (1997 - 2017)

In 1999, Prof. Vivek Borkar joined the School. Since his arrival sev-eral new activities related to optimisation and control theory, stochas-tic learning, and applied probability were taken up. These activitieshave grown and diversified as new faculty members and graduatestudents joined the programs. The formation of the school helpedfocus attention on academic research activities, with a strong grad-uate program. The faculty, which in 1997 was 10 members (six incomputer science and four in speech and signal processing), todayhas 15 members in computer and system sciences put together.

Current status

Among institutions in India, STCS is unique, for it gathers togetherresearchers in a diverse set of areas, creating potential for activitieswith common conceptual and quantitative focus. Today, the school isknown for its expertise and high quality contributions in areas suchas algorithms, complexity theory, formal methods, financial mathe-matics, information theory, communications and machine learning.Complementing the diversity in the faculty, our graduate programattracts students with diverse academic backgrounds. The set ofcourses in the graduate program reflects this diversity, providing ex-posure and offering flexibility to our graduate students to develop abroad background across fields.

Current strength of the school

Faculty 15

Post-docs 5

Research scholars 20

Scientific officers 3

Administrative and technical staff 4

Associate/Adjunct faculty 4

As a consequence of the recent inductions, the school has a youngfaculty: about half the members are under forty. However, greaterexperience is available through the appointment of distinguishedadjunct faculty members from around the world. In addition, theschool hosts talks by visitors and organizes workshops round theyear, which often allow us to bring in expertise in areas not alreadyrepresented in the school.

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plans for the future

Plans for the future

The school envisions growth and consolidation in the currently ac-tive areas, with the aim of becoming internationally known for ex-citing research in conceptual and theoretical areas of Computer andSystem Sciences. In addition, we intend to cultivate young talentin emerging areas such as quantum computing, machine learning,computational neuroscience, large data science, etc., and other tradi-tional areas where we lack strength, such as stochastic control. In thenear term of 3-5 years, we would like to increase our faculty strengthfrom the current 15 to around 20.

The school will seek grants and establish endowments in order toprovide a dynamic research environment with regular internationalvisitors, faculty, post-docs and students. Bringing together the exper-tise offered by the visitors and members of the institute, the schoolwill encourage formation of centres targeting important problemsby exploiting the synergies in quantitative tools across areas. Theschool will actively seek engagement and support from the industryto bring problems of immediate interest into focus.

To enhance the quality of incoming students and to increase ourimpact, we would like to restructure our current program to accom-modate substantially more students in an integrated Master’s andPhD program that focuses on common quantitative tools used invaried emerging applications in computers and information sciencesas well as physical and social sciences and engineering. This wouldbe a unique program in the country. An important flexibility of thisprogram would be to allow an explicit option to students to leaveafter a Master’s degree; thus encouraging talented students to joinwithout making an initial long term commitment.

We also plan to explore closer cooperation with EE and CS De-partments in IIT Bombay using a video link to allow students fromone institute to enrol in advanced courses taught at the other campus.Members of the faculty have offered courses in the past in which stu-dents from both institutes were enrolled, but this involved studentsmaking an inconvenient trip between the institutes. We plan to putvideo lectures on the internet. This was done on an experimental ba-sis for talks from two workshops conducted by the school. Lecturesfrom Bombay Information Theory Seminar (BITS) conducted in Jan-uary 2018 have already attracted over 1800 views. We hope to do thison a regular basis for talks/workshops held at the school.

Reach out to the industry in emerging areas

We plan to enhance academic linkages with IITs, IISc and industrythrough shared technology-enabled activities and collaborations.

To prepare students for high quality research our school conductsintense doctoral level courses. Through videos etc we plan to gen-erate knowledge resources and make them accessible to the generalpublic.

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Members of the Department

Faculty Members

Umang Bhaskar

Brief Employment History

• School of Technology and Computer Science, Tata Institute of Fun-damental Research, Mumbai, India. Reader (2015 – present).

• Department of Combinatorics and Optimization, University of Wa-terloo, USA. Post-doctoral Researcher (2014 – 2015).

• Center for Mathematics and Information, California Institute ofTechnology, USA. Post-doctoral Researcher (2012 – 2014).

• Tata Consultancy Services, India. Software Engineer (2003 – 2005).

Brief Education History

• BTech, NIT Allahabad, 2003.

• MTech, IIT Bombay, 2007.

• PhD, Dartmouth College, 2012.

Research Focus

Umang’s research focuses on algorithmic game theory, which stud-ies computation in game-theoretic models of behaviour. Game the-ory is a classical tool to analyse decentralized systems with multipleagents, such as road traffic, auctions, and elections. Fundamentalproblems in algorithmic game theory include computing the stableoutcomes - called equilibria - in games, or computing incentives foragents to converge to better outcomes. Research in algorithmic gametheory has had multiple applications, including voting mechanisms,advertising, product pricing, and college assigments to students.

Most significant work

• Truthful and Near-Optimal Mechanisms for Welfare Maximiza-tion in Multi-Winner Elections. To appear in AAAI 2018.

members of the department

• A Stackelberg Strategy for Routing Flow over Time. GEB 2015 andSODA 2011.

• Achieving Target Equilibria in Network Routing Games withoutKnowing the Latency Functions. FOCS 2014.

• Network Improvement for Equilibrium Routing. IPCO 2014.

• Online Mixed Packing and Covering. SODA 2013 and GEB (ac-cepted, appeared online).

• Equilibria of atomic flow games are not unique. SODA 2009 andMOR 2011.

Invited Talks

• Bounds on the Welfare of Truthful Voting Mechanisms, IGIDR, Mum-bai, April 2018.

• Using Tolls and Signals to Obtain Good Equilibria in Routing Games,IEOR department, IIT Bombay, Febraury 2018.

• Optimal Signaling in Bayesian Games, ISI Delhi, October 2016; ERU,ISI Kolkata, March 2016; and IISc Bangalore, January 2016.

• Network Improvement for Equilibrium Routing, International Con-clave on Foundations of Decisions and Game Theory, IGIDR, Mum-bai, March 2016.

Honours, Recognition and Services

• PC member for ACM EC 2018, 2017, 2015 and 2013, WINE 2017,SAGT 2016, and FSTTCS 2016.

• Reviewer for Games and Economic Behavior, Operations Research,European Journal of Operations Research, Theory of ComputingSciences, and Mathematical Programming.

Mentoring and Teaching

• PhD Students: Phani Raj Lolakapuri (current), Gunjan Kumar (cur-rent).

• Courses: Algorithmic Game Theory, Linear Progamming and Ap-proximation Algorithms, Computing Equilibria in Games and Mar-kets (reading course), Algorithms and Data Structures, Game The-ory (reading course; co-taught with Sandeep Juneja), Combinato-rial Optimization (co-taught with Kavitha Telikepalli).

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arkadev chattopadhyay

Arkadev Chattopadhyay



• Simons Institute for the Theory of Computing, Berkeley, USA. Vis-iting Scientist (2016 – 2018).

• Institute Henri Poincaré, Paris, France. CNRS Researcher (Febru-ary – March 2016).

• Simons Institute for the Theory of Computing, Berkeley, USA. Vis-iting Scientist (March – April 2015).

• Department of Computer Science, University of Toronto, Canada.Post-doctoral Member (2009 – 2012).

• School of Mathematics at the Institute for Advanced Study, Prince-ton, USA. Member (2008 – 2009).

• School of Computer Science at McGill University, Montreal, Canada.Research Assistant (2002 – 2004).

• PCS Innovations, Canada. Systems Lead Architect (2000 – 2002).

• Usha Communications Technology, Kolkata, India. Senior Man-ager (1995 – 1999).

• Department of Electronics and Electrical Communication Engi-neering, IIT Kharagpur, India. Research Assistant (1994 – 1995).


• BTech, Electronics & Electrical Communication Engineering, IITKharagpur, 1994.

• MSc, Computer Science, McGill University, 2004.

• PhD, Computer Science, McGill University, 2009.

Research Focus

Arkadev has so far focussed on obtaining concrete lower bounds inBoolean circuits, communication complexity and data-structures. Heis perhaps best known for his work on the ‘Number on Forehead’model of multiparty communication, a model with connections tomany other areas of computer science. In the last few years, Arkadevhas been working on understanding the effects of network topologyon multiparty point-to-point communication, lifting theorems thatlift lower bounds on the number of queries to communication lowerbounds and lower bounds for Boolean threshold circuits.


• Chattopadhyay, A., Koucký, M., Loff, B. and Mukhopadhyay, S.(2017) Simulation Beats Richness: New Data-Structure Lower Bounds.

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to appear in the 50th ACM Symposium on Theory of Computing (STOC),2018.

• Chattopadhyay, A. and Mande, N. (2017) Weights at the BottomMatter When the Top is Heavy. Electronic Colloquium on Computa-tional Complexity, TR17-083.

• Chattopadhyay, A., Radhakrishnan, J., Rudra, A. (2014) TopologyMatters in Communication. 55th IEEE Annual Symposium on Foun-dations of Computer Science (FOCS), Philadelphia, USA. 631-640.

• Chattopadhyay, A., Edmonds, J., Ellen, F. and Pitassi, T. (2016)Upper and Lower Bounds on the Power of Advice. SIAM Journalof Computing, Vol 45(4): 1412–1432.

• Chattopadhyay, A. and Wigderson, A. (2009) Linear systems overcomposite moduli. 50th IEEE FOCS, Atlanta, USA. 43-52

• Ada, A. and Chattopadhyay, A. (2008) Multiparty Communica-tion Complexity of Disjointness. Electronic Colloquium on Computa-tional Complexity, TR08-002.

• Chattopadhyay, A. (2007) Discrepancy and the Power of BottomFan-in in Depth-three Circuits. 48th IEEE FOCS, Providence, RI,USA.

Invited Talks

• Weights at the Bottom Matter When the Top is Heavy, Oxford Univer-sity Algorithms and Complexity Seminar, October 12, 2017, UK.

• Topology Matters in Communication, Nexus of Information and Com-putation Theories, February, 2016, Institut Henri Poincaré, Paris.

• Computational Complexity, Santa-Fe Winter School on Complex Sys-tems, IISER Mohali, Dec 7-21, 2015.

• Multi-party Communication Complexity, Summer School on LowerBounds, Charles University, Prague, Czech Republic, June 28-July1, 2015.

• Linear Systems over Composite Moduli, The 3rd Eastern Great LakesTheory of Computation Workshop, October 9-10, 2010, Buffalo,USA.


• Appointed Program Committee (PC) Chair of Foundations of Soft-ware Technology & Theoretical Computer Science (FSTTCS), 2019,Track-A.

• PC Member of ICALP 2017, FSTTCS 2016, STACS 2016, FOCS2015.

• Ramanujan Fellowship (2013), Department of Science and Tech-nology, India.

• PhD Thesis ranked among the top 4 competing for the DoctoralPrize (2010) of the Natural Sciences and Engineering ResearchCouncil (NSERC), Canada, in the Engineering and Computing Sci-

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arkadev chattopadhyay

ences category.


• Post-docs: Marc Vinyals (current)

• PhD Students: Sagnik Mukhopadhyay (2017), Nikhil Mande (2018;expected), Suhail Sherif (current).

• Courses: Boolean circuit complexity, Fourier analysis, Communica-tion Complexity, Computational Complexity, Theory of Computa-tion, Automata Theory using Algebra and Logic (Reading course),Concrete Lower Bounds.

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Ashutosh K. Gupta


• Department of Computer Science, IIT Bombay, Associate Profes-sor (2018 – present)

• School of Technology and Computer Science, Tata Institute ofFindamental Research, Mumbai. Reader (2014 – 2018; currentlyon leave).

• IST Austria, Postdoctoral Researcher (2011 – 2014).


• BTech, Electrical Engineering, IIT Kanpur, 2004.

• MS, Computer Science, EPFL, 2007.

• PhD, Computer science, TU Munich 2011.

Research focus

Ashutosh Gupta’s research interests are software verification, con-straint solving, program synthesis, and systems biology. He has con-tributed in verification of various classes of programs.


• “Model Checking Gene Regulatory Networks” (Best paper award),Tools and Algorithms for the Construction and Analysis of Sys-tems (TACAS) 2015, with Mirco Giacobbe, Calin Guet, ThomasHenzinger, Tiago Paixao, and Tatjana Petrov

• “Succinct Representation of Concurrent Trace Sets”, Principles ofProgramming Languages (POPL) 2015, with Thomas Henzinger,Arjun Radhakrishna, Roopsha Samanta, and Thorsten Tarrach

• “Predicate abstraction and refinement for verifying multi-threadedprograms”, Principles of Programming Languages (POPL) 2011,with Corneliu Popeea and Andrey Rybalchenko

• “From tests to proofs” (Best paper award), Tools and Algorithmsfor the Construction and Analysis of Systems (TACAS) 2009, withRupak Majumdar and Andrey Rybalchenko

• “Proving non-termination”, Principles of Programming Languages(POPL) 2008, with Tom Henzinger, Rupak Majumdar, Andrey Ry-balchenko, and Ru-Gang Xu

Editorial and PC work

PC member: SASB 2018, SAS 2018, ICEC 2016, FSTTCS 2015, CMSB2015, VMCAI 2013, CMSB 2013(PC member and Editor), WING 2013

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ashutosh k. gupta


Courses: Mathematical Logic, Program verification: theory and prac-tice

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Prahladh Harsha


• School of Technology and Computer Science, Tata Institute of Fun-damental Research, Mumbai. Reader (2009 – 2014), Associate Pro-fessor (2015 – present).

• Department of Applied Mathematics and Computer Science, Weiz-mann Institute of Science, Rehovot, Israel. Visiting Associate Pro-fessor (February – July, 2017).

• Department of Computer Science & DIMACS, Rutgers University,USA. Murray Visiting Professor (2016 – 2017).

• Simons Institute for the Theory of Computing, USA. Visiting Sci-entist (2013 – 2013).

• Institute of Mathematical Sciences (IMSc), Chennai, India. VisitingFaculty (2010 – 2013).

• Department of Computer Science, University of Texas at Austin,USA. Research Fellow (2008 – 2009).

• Technion, Israel Institute of Technology, Haifa, Israel. Aly Kauf-man Visiting Scientist (2008 – 2009).

• Toyota Technological Institute, Chicago, USA. Research AssistantProfessor (2004 – 2008).

• Microsoft Research, Silicon Valley, Mountain View, USA. Post-doctoral Researcher (January – September, 2005).


• BTech, Computer Science and Engineering, IIT Madras, 1998.

• MS, Computer Science, MIT, 2000.

• PhD, Computer Science, MIT, 2004.

Research Focus

Prahladh Harsha’s research interests are in the area of theoreticalcomputer science, with special emphasis on computational complex-ity. He is best known for his work in the area of probabilisticallycheckable proofs (PCPs), in particular in understanding the role ofcomposition (aka recursion) in the construction of probabilisticallycheckable proofs and in the construction of short and efficient PCPsand locally testable codes.


• Eli Ben-Sasson, Prahladh Harsha, and Sofya Raskhodnikova. “Some3CNF properties are hard to test.” SIAM J. Comput., 35(1):1–21,2005. (Preliminary version in 35th STOC, 2003).

• Eli Ben-Sasson, Oded Goldreich, Prahladh Harsha, Madhu Sudan,and Salil Vadhan. Robust PCPs of proximity, shorter PCPs and ap-

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prahladh harsha

plications to coding. SIAM J. Comput., 36(4):889–974, 2006. (spe-cial issue on Randomness and Computation; Preliminary versionin 36th STOC, 2004).

• Eli Ben-Sasson, Oded Goldreich, Prahladh Harsha, Madhu Su-dan, and Salil Vadhan. “Short PCPs verifiable in polylogarithmictime.” In Proc. 20th IEEE Conf. on Comput. Complexity, pages 120–134, 2005.

• Prahladh Harsha, Rahul Jain, David McAllester, and JaikumarRadhakrishnan. “The communication complexity of correlation.”IEEE Trans. Inform. Theory, 56(1):438–449, 2010. (Preliminary ver-sion in 22nd IEEE Conference on Computational Complexity, 2007).

• Prahladh Harsha, Adam Klivans, and Raghu Meka. “An invari-ance principle for polytopes” J. ACM, 59(6):29, 2012. (Preliminaryversion in 42nd STOC, 2010).

• Irit Dinur and Prahladh Harsha. “Composition of low-error 2-query PCPs using decodable PCPs”. SIAM J. Comput., 42(6):2452–2486, 2013. (special issue for FOCS 2009; Preliminary version in51st FOCS, 2009).

• Steve Chien, Prahladh Harsha, Alistair Sinclair, and Srikanth Srini-vasan. “Almost settling the hardness of noncommutative determi-nant” In Proc. 43rd ACM Symp. Theory of Computing (STOC), pages499–508, 2011.

• Venkat Guruswami, Prahladh Harsha, Johan Håstad, Srikanth Srini-vasan, and Girish Varma. “Super-polylogarithmic hypergraphcoloring hardness via low-degree long codes.” SIAM J. Comput.,46(1):132-–159, 2017. (Preliminary Version in 46th STOC, 2014).

Awards and Honours

• Swarnajayanti Fellowship Award 2015-16 in Mathematical Sciences(Department of Science and Technology, Government of India).

• NASI-SCOPUS Young Scientist Award 2011 for Mathematics.

• Associate of the Indian Academy of Sciences (for the period: 2011–2014).


• Associate Editor, SIAM Journal on Computing (2017 – present)

• Editor for Proc. 35th FSTTCS 2015, vol 45 of LIPIcs, Schloss Dagstuhl.

• Guest Editor for the CCC 2016 Special Issue in Theory of Com-puting (ToC) journal.

• Co-chair of program committee of FSTTCS 2015.

• Member of program committee: RANDOM 2009, APPROX 2011,FSTTCS 2011, RANDOM 2013, FSTTCS 2013, CALDAM 2015, FSTTCS2015, CCC 2016, FOCS 2016.

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• PhD students: Girish Varma (2016), Swagato Sanyal (2017), RakeshVenkat (2017), Tulasimohan Molli (current), Siddharth Bhandari(current).

• Courses: Analysis of Boolean functions, Coding Theory, Commu-nication Complexity, Expander graphs, Computational Complex-ity, Probabilistically Checkable Proofs, Probability and Computa-tion.

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sandeep k. juneja

Sandeep K. Juneja


• School of Technology and Computer Science, Tata Institute of Fun-damental Research, Mumbai, India. Reader (2002 – 2004), As-sociate Professor (2004 – 2011), Professor (2011 – present), Dean(2017 – present).

• Operations Research Group, Department of Mechanical Engineer-ing, IIT Delhi, Delhi, India. Assistant Professor (1997–2002), Asso-ciate Professor (2002).

• Andersen Consulting, Delhi, India. Senior Consultant (1995 –1996).

• American Credit Indemnity (Company of Dun & Bradstreet), Bal-timore. Director, Quantitative Analysis.

• Selected visiting positions:Stanford, Dept. MS & E. January – April, 2000.Columbia, IEOR. Summers 1997, 1998, 1999, 2004, 2005, 2007.Columbia Business School. Summers 2004, 2005, 2007.Indian School of Business. March – December, 2006.ICERM Brown University. October – November, 2012.Heriot Watt University. April – May, 2011.Research wing of RBI. September – December, 2015.Bank of America, Head Quantitative Analysis, India. January –

August, 2008.


• BTech, Mechanical Engineering, IIT Delhi, 1989.

• Masters in Statistics, Stanford, 1992.

• PhD, Operations Research, Stanford, 1993.

Research Focus

Rare event simulation. Efficient simulation algorithms for financialderivatives pricing and portfolio risk measurement. Equilibriumstrategic arrival behaviour at queues. Pure exploration using largedeviations theory and multi-armed bandit techniques. Perfect sam-pling for spatial stochastic processes, Robust model estimation meth-ods in finance.


• Glynn and Juneja. 2018. Selecting the best system, large devi-ations, and multi-armed bandits. http://arxiv.org/abs/1507.

04564.

• K. R. A. Murthy, S. Juneja and J. Blanchet. 2014. State-independentImportance Sampling for Random Walks with Regularly VaryingIncrements. Stochastic Systems, 2, 4, 321-374.

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http://arxiv.org/abs/1507.04564



• R. Jain, S. Juneja and N. Shimkin. 2010. The Concert QueuingProblem: To Wait or To Be Late. Discrete Events Dynamic Systems,21, 103-138.

• M. Gordy and S. Juneja. 2010. Nested Simulation in Portfolio RiskMeasurement. Management Science 56, 10, 1833-1848

• Bassamboo, S. Juneja and A. Zeevi. 2008. Portfolio Credit Riskwith Extremal Dependence. Operations Research, 56, 3, 593-606.

• P. Glasserman and S. Juneja. 2008. Uniformly Efficient ImportanceSampling for the Tail Distribution of Sums of Random Variables.Mathematics of Operations Research, 33 (1) 36-50.

• S. Juneja, P. Shahabuddin. 2002. Simulating Heavy Tailed Pro-cesses using Delayed Hazard Rate Twisting, ACM TOMACS, 12,2, 94-118.

Invited Talks

• Dynamic portfolio risk measurement. At Finance and Stochastics Day2016, Imperial College, UK, 13 October 2016.

• Rest in Lounge or wait in queue. Workshop on Congestion Games.Institute for Mathematical Sciences, National University of Singa-pore. December 16, 2015

• Ordinal optimization - Empirical large deviations rate estimators, andmulti-armed bandit methods. Workshop Applied Probability Fron-tiers: Computational and Modelling Challenges. At Banff Inter-national Research Station, Canada. June 1, 2015.

• Rare event simulation of heavy tailed random walks - A new approach.Stochastic Networks Conference at CWI Amsterdam, June 23-27,2014.

• Multi-Armed Bandits and Nested Simulation for Financial Portfolio RiskMeasurement. Keynote speaker at 4th IIM Ahmedabad Interna-tional Conference on Advanced Data Analysis, Business Analyticsand Intelligence. April 11, 2015.


• Best paper award at ICST Sixth International Conference on Per-formance Evaluation Methodologies and Tools for paper The Con-cert Queueing Game with Random Arrivals Volume.

• Ranked amongst the most productive researchers in managementfrom India (for period 1990-2009) in a study by Aditya Birla Centreat the London Business School.

• Best paper award at the ICST Fourth International Conference onPerformance Evaluation Methodologies and Tools for paper TheConcert/Cafeteria Queuing Problem: A Game of Arrivals.

• Recipient of Yahoo Academic Research Grant for the year 2009-10.

• Associate Editor, Stochastic Systems (2017 – present), Mathematicsof Operations Research (2008 – 16), Management Science (2003 -

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sandeep k. juneja

09), ACM TOMACS (2008 -10).

• Member, National Advisory Board, Economic Sciences at IIT Kan-pur (2016 – present).

• Member, Academic Council, Indira Gandhi Institute for Develop-mental Research, Mumbai (2016 – present).


• Post-docs: Tejas Bodas (2016), Vineeth Chintala (2017), SubhashiniKrishnaswamy (current).

• PhD Students: S Dey (2013), M A R Karthyek (2015), A Agarwal(2015), T Raheja (2016), S B Moka (2017), Anand Deo (current).

• Courses: Advanced probability, Mathematical finance, Real Analy-sis, Optimization, Game Theory (co-taught with Umang Bhaskar),Stochastic Calculus

25


Kavitha Telikepalli


• School of Technology and Computer Science, Tata Institute of Fun-damental Research, Mumbai, India. Associate Professor (2011 –present), Reader (2010 – 2011).

• Computer Science and Automation Department, Indian Instituteof Science, Bangalore, India. Assistant Professor (2005 – 2010).

• Algorithms and Complexity Group, Max-Planck-Institut für Infor-matik, Germany. Post-doctoral Researcher (2002 – 2004).

• River Run Software Group, Noida, India. Software Engineer (1995

– 1996).


• BTech, Computer Science and Engineering, IIT Madras, 1995.

• PhD, Computer Science, TIFR Mumbai, 2002.

Research Focus

Kavitha’s research work has primarily focused on efficient graph al-gorithms. In the last few years, she has worked on several problemsin the the domain of matching problems. Here the input is a graphwhere each vertex ranks edges incident upon itself in some order ofpreference and the general problem is to match vertices in a man-ner that keeps the vertices “as happy as possible”. She has workedin this domain using a notion called popularity that is more relaxedthan the notion of stability. She has also worked on efficient algo-rithms for several other problems in graphs: these include approx-imate distance oracles, spanners, Gomory-Hu trees, and minimumcycle bases.


• Popularity, Mixed Matchings, and Self-duality. In the proceedings ofthe 28th Symposium on Discrete Algorithms (SODA): pages 2294-2310, 2017. C.-C. Huang and T. Kavitha.

• New Algorithms for Maximum Weight Matching and a DecompositionTheorem. Mathematics of Operations Research, Vol. 42, No. 2:pages 411-426, 2017. C.-C. Huang and T. Kavitha.

• A size-popularity tradeoff in the stable marriage problem. SIAM Journalon Computing, Volume 43(1): pages 52-71, 2014. T. Kavitha.

• Faster Algorithms for All-Pairs Approximate Shortest Paths in Undi-rected Graphs. SIAM Journal on Computing: Vol. 39, No. 7:pages 2865-2896, 2010. S. Baswana and T. Kavitha.

• Faster Algorithms for Minimum Cycle Basis in Directed Graphs. SIAMJournal on Computing, Vol. 38 No. 4: pages 1430-1447, 2008. R.Hariharan, T. Kavitha, K. Mehlhorn.

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kavitha telikepalli


• Associate Editor, ACM Transactions on Algorithms (from 2016 on-wards)

• Associate Editor, Journal of Combinatorial Optimization (2012–2017)

Program Committee membership: ICALP 2017, CSR 2017, SODA 2016,IWOCA 2016, FUN 2016, WAOA 2015, APPROX 2014, FSTTCS 2014,SODA 2013, ESA 2013, ALENEX 2013, WAOA 2013, SWAT 2012,FSTTCS 2012, COCOON 2012, COCOON 2011, SODA 2010, FSTTCS2010, STACS 2009, WALCOM 2009, FSTTCS 2008, FSTTCS 2006.

27


Hariharan Narayanan



• Departments of Statistics and Mathematics, University of Wash-ington, USA. Assistant Professor (2012 – 2016).

• Department of Electrical Engineering, Princeton University, USA.Post-doctoral Associate (2011 – 2012).

• Laboratory for Information and Decision Systems, MassachusettsInstitute of Technology, USA. Post-doctoral Associate (2009 – 2011).


• Dual degree (BTech + MTech), Electrical Engineering, IIT Bombay,2003.

• MS, Computer Science, The University of Chicago, 2006.

• PhD, Computer Science, The University of Chicago, 2009.

Research Focus

Hariharan Narayanan’s research is focussed in two areas: Random-ized Interior Point Methods for Sampling and Optimization, andManifold learning. Randomized interior point methods are algo-rithms that bring together ideas from interior point methods andMarkov chains. Manifold learning consists of algorithms and anal-yses based on the hypothesis that high dimensional data lie in thevicinity of a low dimensional manifold.


• Testing the Manifold Hypothesis, C. Fefferman, S. Mitter and H.Narayanan, Online February 9, 2016, Journal of the AmericanMathematical Society, Volume 29 (2016), 983-1049.

• Randomized interior point methods for sampling and optimization, H.Narayanan, Annals of Applied Probability, Volume 26, Number 1,February 2016, pp 597-641.

• Heat flow and a faster algorithm to compute the surface area of a convexbody, M. Belkin and H. Narayanan and P. Niyogi, Random Struc-tures and Algorithms, Volume 43, Issue 4, December 2013, pages407-428.

• Random walks on polytopes and an affine interior point method for Lin-ear Programming, R. Kannan and H. Narayanan, Mathematics ofOperations Research. Volume 37, Issue 1, (On line) January 9,2012, pages 1-20.

• Geometric Interpretation of Half-Plane Capacity, S. Lalley, G. Lawler,H. Narayanan, Electron. Commun. Probab. Volume 14 (2009),

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hariharan narayanan

paper no. 55, 566-571.

• Minimizing average latency in oblivious routing. P. Harsha, T. Hayes,H. Narayanan, H. Racke and J. Radhakrishnan ACM-SIAM Sym-posium on Discrete Algorithms (SODA), January 2008

• On the complexity of computing Kostka numbers and Littlewood-Richardsoncoefficients, H. Narayanan, Journal of Algebraic Combinatorics, vol-ume 24, issue 3, November 2006, Volume 24, Issue 3, November2006, pages 347-354.

Invited Talks

• Seminar, ICTS Bangalore, Statistical Physics Methods in MachineLearning, December 2017.

• Department Colloquium, Electrical Engineering, IIT Bombay, March2017.

• LIDS Seminar, MIT, September 2016.

• International Conference on Continuous Optimization (ICCOPT),Tokyo, August 2016.

• IMA workshop on Power of Randomness in Computation, GaT-ech, March 2015.

• Seventh Workshop on Whitney interpolation, College of Williamand Mary, August 2014.

• Arkansas Spring Lecture Series on Interpolation and extension,April 2013.


• Silver Medal at the International Mathematical Olympiad held inTaipei, Taiwan in 1998.

• Refereed for Annals of Statistics, International Journal of Com-puter Vision, Random Structures and Algorithms, MathematicalProgramming A, International Conference on Algorithmic Learn-ing Theory(ALT), Conference on Neural Information ProcessingSystems (NIPS), Conference on Learning Theory (COLT), Sympo-sium on Theory of Computing (STOC), Symposium on Founda-tions of Computer Science (FOCS).


• PhD Students: Kitty Mohammed (Univ. of Washington), AdamGustafson (Univ. of Washington), Somnath Chakraborty (current)

• Courses at TIFR: Topics in high dimensional geometry

• Courses at the University of Washington: Calculus I, DifferentialEquations, Topics in high dimensional Geometry, Resampling In-ference, Stochastic Modelling.

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Other Services

• NSF Grant on Fitting Manifolds to Noisy data, Sep 2016 to Aug2019.

• Ramanujan Fellowship, July 2017 to June 2021.

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paritosh k pandya

Paritosh K Pandya


• School of Technology and Computer Science, Tata Institute of Fun-damental Research, Mumbai, India. Professor (2011– present), As-sociate Professor (1999 – 2011), Reader (1995 – 1999), Fellow (1992

– 1995), Research Associate (1988 – 1992).

• Department of Computer Science, Indian Institute of TechnologyBombay, India. Adjunct Professor (2015 – 2018), Adjunct AssociateProfessor (2002 – 2015).

• International Institute for Software Technology of the United Na-tions University, Macau. Visiting Researcher (1993 – 1994; on leavefrom TIFR).

• Oxford University Computing Laboratory, United Kingdom. Re-search Officer (1989 – 1991; on leave from TIFR).


• BE, Electronics, M S University, Baroda, 1980.

• MTech, Computer Science, IIT Kanpur, 1982.

• PhD, Computer Science, Bombay University/TIFR Mumbai, 1988.

Research Focus

Logics, automata and formal verification. Specifically, Theory of real-time logics and automata. Requirement Modelling, Model checkingand Controller synthesis from interval temporal logic QDDC.


• Andreas Krebs, Kamal Lodaya, Paritosh Pandya, and HowardStraubing. “Two-variable logic with a between relation.” in Pro-ceedings of the 31st ACM/IEEE Symposium on Logic in Computer Sci-ence (LICS 2016), AMC/IEEE, 2016. pp. 106–115.

• S.N. Krishna, K. Madnani,and P.K. Pandya. “ Making metric tem-poral logic rational.” in 42nd International Symposium on Mathemat-ical Foundations of Computer Science, (MFCS 2017), August 21-25,2017 - Aalborg, Denmark. LIPIcs 83, Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2017. pp.77:1–77:14.

• P.K. Pandya, S.S. Shah. “On Expressive Powers of Timed Log-ics: Comparing Boundedness, Non-punctuality and DeterministicFreezing.” in Proc. 22nd International Conference on ConcurrencyTheory (CONCUR 2011) Aachen, Germany, LNCS 6901, Springer,2011.

• P.K. Pandya, S.S. Shah. “The unary fragments of metric intervaltemporal logic: bounded versus lower bound constraints.” in Proc.10th International Symposium on Automated Technology for Verificationand Analysis (ATVA 2012), Trivendrum, LNCS 7561, Springer, 2011.

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• P.K. Pandya, S.N. Krishna and K. Loya. “On Sampling Abstrac-tion of Continuous Time Logic with Durations.” in Proc. 13thInternational Conference on Tools and Algorithms for the Constructionand Analysis of Systems (TACAS 2007), Braga, Portugal, 2007, LNCS4424, Springer, (2007).

• G. Chakravarty and P.K. Pandya. “Digitizing Interval DurationLogic.” in Proc. 15th International Conference on Computer AidedVerification (CAV 2003), Colorado, Boulder, July 2003 (2003) pp 167-179.

• M. Joseph and P. K. Pandya. “Finding response times in a realtime system.” in The Computer Journal, 29(5), October 1986.

Invited Talks

• Logic Colloquium (LC 2018), Special session on Temporal and FuzzyLogics, Udine, Italy, 23-28 July 2018.

• 9th Intl. workshop on Methods for Modalities (M4M 2017), Kanpur,January, 2017.

• Post-conference workshop on Algorithmic Verification of Real-timeSystems, FSTTCS 2015, Bangalore, 16-19 December, 2016.

• BCS-FACS workshop on Provably Correct Systems (ProCoS 2015),London, 9-10 March, 2015.

• Special session on Logic in Computer Science at 14th Asian LogicConference, IIT Bombay, 5-8 January, 2015. (Other speakers were:Moshe Vardi, Chrystal Baier, Prakash Panangaden).


• PhD Students: K. Narayan Kumar (1996), P. Vijay Suman (2009), Si-moni Shah (2013; Awarded Sasken Best Thesis Award), Amol Wakankar(current; BARC; jointly guided with A.K. Bhattacharjee), KhushrajMadnani (current; IIT Bombay; jointly guided with S.N. Krishna).

• Courses: Automata/Theory of Computation, Introduction to Math-ematical Logic, Automata Theory using Algebra and Logic (read-ing course), Advanced Automata, Foundations of Program Verifi-cation, Concurrency and Process Algebra (reading Course), Foun-dations of Program Verification, Model Checking: Theory andPractice, Foundations of Propositional and Predicate Logic, Sci-ence of Computer Programming.

Services

Program Committees: FSTTCS [’18 (co-chair), ’17, ’10, ’03 (co-chair),’01, ’00, ’97, ’93], LATA [’18 (co-chair), ’09], PEC ’16, TIME [’16, ’11,’10, ’07], ICTAC [’16, ’14, ’13, ’10, ’05, ’04], POPL ’15 (local chair),ICLA ’15, SETTA [’15, ’14], TASE [’15, ’14], RP ’14, ICDCN ’13, HSCC[’12, ’10], FORMATS [’09, ’08, ’07, ’03], FM [’09, ’08, ’06], AVOCS [’09,’08, ’06, ’05], CATS ’09 AFM ’07, ASWC ’06, SEFM [’06 (co-chair), ’05,

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paritosh k pandya

’04], SLAP [’05, ’04, ’03], FME ’01

Editorial Board: Formal Aspects of Computing (’96 – ’10)

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Vinod M. Prabhakaran



• School of Computer and Communication Sciences, Ecole Poly-technique Fédérale de Lausanne (EPFL), Switzerland. Post-doctoralresearcher (February – August, 2011).

• Coordinated Sciences Laboratory, University of Illinois at Urbana-Champaign, USA. Post-doctoral researcher (2008 – 2011).


• BTech, Electronics and Communication Engineering, University ofKerala, August 1999.

• ME, Signal Processing, IISc, Bangalore, 2001.

• PhD, Electrical Engineering and Computer Science, University ofCalifornia, Berkeley, 2007.

Research Focus

Vinod Prabhakaran’s research interests are primarily in InformationTheory. His work has addressed questions related to communica-tion and computation in networks. Some of his recent works in-clude a study of communication requirements for secure computa-tion and optimal designs for communication in several different set-tings which involve the presence of adversaries.


• V. Narayanan and V. Prabhakaran, “Secure Computation in In-complete Networks", submitted, 2018.

• M. Bakshi and V. Prabhakaran, “Plausible Deniability over Broad-cast Channels”, IEEE Transactions on Information Theory, accepted,April 2018.

• D. Data, V. Prabhakaran, and M. Prabhakaran, “Communicationand Randomness Lower Bounds for Secure Computation",IEEETransactions on Information Theory, vol. 62, no. 7, pp. 3901-3929,July 2016. (Conference version in CRYPTO 2014).

• C. Fragouli, V. Prabhakaran, L. Czap, and S. Diggavi, “WirelessNetwork Security: Building on Erasures", Proceedings of the IEEE,vol. 103, no. 10, pp. 1826–1840, October 2015.

• V. Prabhakaran and M. Prabhakaran, “Assisted Common Infor-mation with an Application to Secure Two-Party Sampling", IEEETransactions on Information Theory, vol. 60, no. 6, pp. 3413-3434,June 2014.

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vinod m. prabhakaran

• V. Prabhakaran and P. Viswanath, “Interference Channel with SourceCooperation” and “Interference Channel with Destination Coop-eration", IEEE Transactions on Information Theory, vol. 57, no. 1, pp.156-186 and 187–209, January 2011.

• V. Prabhakaran, D. Tse, and K. Ramchandran, “Rate Region of theQuadratic Gaussian CEO Problem,” in Proc. IEEE Symposium onInformation Theory (ISIT), Chicago, 2004, pp. 119.

Invited Talks

• Tension: understanding cryptographically interesting correlations, Work-shop on Mathematics of Information Theoretic Cryptography, In-stitute for Mathematical Sciences, National University of Singa-pore, Sep 26-30, 2016.

• Rényi information complexity, Information Theory and ApplicationsWorkshop, University of California, San Diego, Jan 31 – Feb 5,2016.

• Lower Bounds for Interactive Function Computation, at IEEE Informa-tion Theory Workshop (ITW), Jeju, Oct 11-15, 2015.

• Information Inequalities for Networks and Applications to Secure Com-putation, Allerton Conference on Communication, Control, andComputing, Illinois, Sept 29 – Oct 2, 2015.

• Tension Bounds for Interactive Systems, IEEE Information TheoryWorkshop (ITW), Jerusalem, Apr 26 – May 1, 2015.

• Secure Function Computation and Information Inequalities for Networks,"Between Shannon and Hamming: Network Information Theoryand Combinatorics" workshop at Banff International Research Sta-tion (BIRS), Banff, Mar 1-6, 2015.

• On the Oblivious Transfer Capacity Region of the Binary Erasure Broad-cast Channel, IEEE Information Theory Workshop (ITW), Hobart,Nov 2-5, 2014.


• IEEE Transactions on Information Theory - Associate Editor forShannon Theory since August 2016.

• Adjunct faculty at Department of Electrical Engineering, IIT Bom-bay since April 2014.

• Ramanujan Fellowship from Department of Science and Technol-ogy, Government of India, 2011-2016.


• Post-docs: Jithin Ravi (2017).

• PhD Students: Manoj Mishra (2016; IIT Bombay), Deepesh Data(2017), Gowtham Raghunath Kurri (current), Varun Narayanan(current).

35


• PhD Students mentored: László Czap (EPFL), Amitalok Budkuley(IIT Bombay), Viswanathan Ramachrandran (IIT Bombay).

• Courses: An Introduction to Probability, Information Theory, Digi-tal Communication, Advanced Information Theory (TIFR and IITBombay), Network Information Theory (TIFR and at IIT Bombay),Convex Optimization, Coding Theory, Privacy (reading course).

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jaikumar radhakrishan

Jaikumar Radhakrishan


• School of Technology and Computer Science, Tata Institute ofFundamental Research, Mumbai, India. Senior Professor (2017

– present), Professor (2007 – 2016), Associate Professor (2000 –2007), Reader (1997 – 2000), Fellow (1992 – 1997), Visiting Fellow(1991 – 1992).

• Toyota Technological Institute, Chicago, USA. Visiting Professor(2004 – 2006)

• The Hebrew University, Jerusalem. Post-doctoral visitor (1996 –1997).

• Japan Advanced Institute of Science and Technology, Kanazawa,Japan. Visiting Associate Professor (1992 – 1993).

• CMC Ltd, Calcutta, India. Associate Systems Engineer (1985 –1986).


• BTech, Computer Science and Engineering, IIT Kharagpur, 1985.

• PhD, Computer Science, Rutgers University, 1991.

Research focus

Algorithms, Combinatorics, Complexity, Randomness, Quantum Com-putation and Information


• Magnús M. Halldórsson, Jaikumar Radhakrishnan: Greed is Good:Approximating Independent Sets in Sparse and Bounded-Degree Graphs.Algorithmica 18(1): 145-163 (1997)

• Jaikumar Radhakrishnan: An Entropy Proof of Bregman’s Theorem.J. Comb. Theory, Ser. A 77(1): 161-164 (1997)

• Jaikumar Radhakrishnan, Aravind Srinivasan: Improved boundsand algorithms for hypergraph 2-coloring. Random Struct. Algo-rithms 16(1): 4-32 (2000)

• Jaikumar Radhakrishnan, Amnon Ta-Shma: Bounds for Dispersers,Extractors, and Depth-Two Superconcentrators. SIAM J. Discrete Math.13(1): 2-24 (2000)

• Harry Buhrman, Peter Bro Miltersen, Jaikumar Radhakrishnan,Srinivasan Venkatesh: Are Bitvectors Optimal? SIAM J. Comput.31(6): 1723-1744 (2002)

• Rahul Jain, Jaikumar Radhakrishnan, Pranab Sen: A property ofquantum relative entropy with an application to privacy in quantumcommunication. J. ACM 56(6): 33:1-33:32 (2009)

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• Eli Ben-Sasson, Swastik Kopparty, Jaikumar Radhakrishnan: Sub-space polynomials and limits to list decoding of Reed-Solomon codes.IEEE Trans. Information Theory 56(1): 113-120 (2010)

• Prahladh Harsha, Rahul Jain, David A. McAllester, Jaikumar Rad-hakrishnan: The communication complexity of correlation. IEEE Trans.Information Theory 56(1): 438-449 (2010)

• Mohit Garg, Jaikumar Radhakrishnan: Set membership with a fewbit probes. SODA 2015: 776-784

• Marco Dalai, Venkatesan Guruswami, Jaikumar Radhakrishnan:An improved bound on the zero-error list-decoding capacity of the 4/3channel. ISIT 2017: 1658-1662

Invited Talks

• The bounded-round quantum communication complexity of set disjoint-ness at the workshop on Quantum Information Processing (QIP),Waterloo, 2004

• The list decoding radius of Reed-Solomon codes at the 18th SrinivasaRamanujan Memorial Award Lecture, Indian Mathematical Soci-ety, Pune, 2008

• Better bounds for perfect hashing into a 4-element set at From Infor-mation Theory to Combinatorics, Workshop in Honour of JánosKörner’s research world, Rome, 2017

Honours, recognition and services

• Machtey Award for the best student paper by the FOCS (1991)program committee for the paper “Better bounds for thresholdformulas” (jointly with two other recipients).

• Associate of the Indian Academy of Sciences for the period 1994 –1999.

• Fellow of the Indian Academy of Sciences, 2007 onward.

• Shanti Swarup Bhatnagar Prize in Mathematical Sciences, 2008.

• Fellow of the Indian National Science Academy, 2014 onward.

• Member, Nevanlinna Prize Committee (2014)


• PhD Students: K V Subrahmanyam (1994), S Venkatesh (2000),Pranab Sen (2001), Sivaramakrishnan Sivasubramaniam (2005), RahulJain (2005), Chinmoy Dutta (2009), Saswata Shannigrahi (2011),Naqueeb Warsi (2015), Mohit Garg (2016), Kshitij Gajjar (current).

• Courses: Algorithms, Automata and Computability, Combinato-rial Optimization, Communication Complexity, Information The-ory, Mathematics Foundations for Computer Science, Probability,Randomness and Computation, Quantum Computation and In-formation.

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n. raja

N. Raja


• School of Technology and Computer Science, Tata Institute of Fun-damental Research, Mumbai, India. Reader (2006 – present), Fel-low (2001 – 2006), Visiting Fellow (1998 – 2001).


• ME, IISc, Bangalore.

• PhD, TIFR Mumbai.

Research Focus

My research work has been focused in the areas of logic in computerscience and on the foundations of mathematics. I have been workingon formal verification of computer software and on the formaliza-tion of mathematics. My work is based on automated reasoningtechniques to rigorously verify the correct behaviour of computersystems and also for formalizing theorems from mathematics andtheir proofs. My work has also examined proofs of basic mathe-matical theorems to see if they can be reformulated using a simplerrepertoire of logical primitives and reasoning mechanisms.


• Vicious Queues and Vicious Circles Raja Natarajan; Asia PacificMathematics Journal, Vol. 3, No. 1.

• Social Processes, Program Verification and All That: Andrea As-perti, Herman Geuvers and Raja Natarajan; Mathematical Struc-tures in Computer Science, Vol. 19, No. 5.

• Yet Another Proof of Cantor’s Theorem: Raja Natarajan; Dimen-sions of Logical Concepts, Coleção CLE, Campinas, Vol. 54.

• A Negation-Free Proof of Cantor’s Theorem: N. Raja; Notre DameJournal of Formal Logic, Vol. 46, No. 2.

• Type Systems for Concurrent Programming Calculi: N. Raja andR.K. Shyamasundar; Informatica, Vol. 27, No. 4.

• Combinatory Formulations of Concurrent Languages: N. Raja andR.K. Shyamasundar; ACM Transactions on Programming Languagesand Systems (TOPLAS), Vol. 19, No. 6.

• The Quine-Bernays Combinatory Calculus: N. Raja and R.K. Shya-masundar; International Journal of Foundations of Computer Science,Vol. 6, No. 4.

Invited Talks

• Diagrammatic Reasoning for Boolean Equations, Conf. on Computa-tional Logic: Bridging the Gap between Human and Automated

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Reasoning, Dresden, Germany (2017).

• Modularity and Proof Assistants, Current Issues in Interactive Theo-rem Proving, Toulouse, France (2016).

• Modal Logics for Internet Protocols, Conf. on Assertoric and ModalLogics, Santiago, Chile (2016).

• Judgment Aggregation and Stability of Programs, Conf. on Logics forSocial Behaviour, Lorentz Center, Leiden, The Netherlands (2014).

• Software Tools for Formal Proofs, Mathematics for Scientific Pro-gramming, Oberwolfach, Germany (2013).

• From Paradox to Proof, Conf. on Logic and Computation, The Aus-tralian National University, Canberra, Australia (2009).

• From Yablo’s Paradox to Cantor’s Theorem, Sémantique et Réalisabil-ité Seminar, Université Paris Diderot – Paris 7, France (2009).


• Member, Editorial Board, Logica Universalis, Journal published bySpringer-Birkhauser.

• Member, Programme Committee, Thirteenth International Confer-ence on Risks and Security of Internet and Systems, Arcachon, France(2018).

• Member, Programme Committee, Non-Classical Logics, Special Trackof the 30th International Conference of the Florida Artificial Intel-ligence Research Society, (FLAIRS), Florida, U.S.A. (2017).

• Member, Programme Committee, Symposium on Dependable Soft-ware Engineering, Beijing, China (2016).

• Member, Programme Committee, Fourth ACM-SIGPLAN Confer-ence on Certified Programs and Proofs, (CPP), Mumbai, India (2015).

• Member, Programme Committee, Second Workshop on Smarter Planetand Big Data Analytics, International Conference on DistributedComputing and Networking (ICDCN), (2014).

• Editor, Distributed Computing and Internet Technology, LectureNotes in Computer Science, Vol. 8337, Springer-Verlag (2014).

• Editor, Special Issue on Interactive Theorem Proving Sadhana, En-gineering Journal of the Indian Academy of Sciences.


• PhD Students: Benny George Kenkireth (2011), Abhishek Kr Singh(current).

• Post-docs: Benoit Razet (2010–2012).

• Courses at TIFR: Mathematical Logic, Concurrency Theory, Dis-tributed Algorithms, Semantics of Computation, Recursive Func-tion Theory, Term Rewriting Systems, Foundational Models forComputing, Topics in Interactive Proof Checking.

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n. raja

• Courses taken elsewhere: Theoretical Computer Science (IISER Trivan-drum), Interactive Theorem Proving (ISI Kolkata), The AxiomaticTechnique in Logic and Mathematics (IISER Pune), MathematicalLogic (Jadavpur University, Kolkata), Proof Systems for Proposi-tional Logics (Kerala School of Mathematics), Logic and FormalReasoning in Computer Science (KIIT University, Bhubaneswar).

Other services

• Conferences Organized

– Organizing Committee Member, Sixth World Congress and Schoolon Universal Logic, Vichy, France, 2018.

– Organizing Committee Member, Sixteenth International Confer-ence on Verification, Model Checking, and Abstract Interpretation,(VMCAI), Mumbai, India, 2015.

– Organizing Committee Member, Fifth World Congress on Para-consistency, ISI Kolkata, India, 2014.

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Ramprasad Saptharishi



• Tel Aviv University, Israel. Post-doctoral researcher (2014 – 2016).

• Microsoft Research India. Research Fellow (2013 – 2014).


• BSc Hons, Mathematics and Computer Science, Chennai Mathe-matical Institute, 2007.

• MSc, Computer Science, Chennai Mathematical Institute, 2009.

• PhD, Computer Science, Chennai Mathematical Institute, 2013.

Research Focus

Ramprasad Saptharishi’s research interests are primarily in algebraiccomplexity theory. Much of his recent work has been in studyingmultivariate polynomials in the context of algebraic circuit lowerbounds, polynomial identity tests, and the structure of Reed-Mullercodes.


• “Approaching the chasm at depth four”; Ankit Gupta, Pritish Ka-math, Neeraj Kayal, Ramprasad Saptharishi; Journal of the ACM,2014; conference version in CCC 2013.

• “Arithmetic circuits: A chasm at depth three”; Ankit Gupta, Pri-tish Kamath, Neeraj Kayal, Ramprasad Saptharishi; SIAM Journalof Computing, 2016; conference version in FOCS.

• “An exponential lower bound for homogeneous depth-5 circuitsover finite fields”; Mrinal Kumar, Ramprasad Saptharishi; CCC2017.

• “Efficiently decoding Reed-Muller codes from random errors”;Ramprasad Saptharishi, Amir Shpilka, Ben Lee Volk; IEEE Trans-actions in Information Theory 2017; conference version in STOC2016.

• “Functional lower bounds for arithmetic circuits and connectionsto boolean circuit complexity”; Michael Forbes, Mrinal Kumar,Ramprasad Saptharishi; CCC 2016.

• “Jacobian hits circuits: Hitting-sets, lower bounds for depth-Doccur-k formulas & depth-3 transcendence degree-k circuits”; Manin-dra Agrawal, Chandan Saha, Ramprasad Saptharishi, Nitin Sax-ena; SIAM Journal of Computing 2016; conference version in STOC2012.

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• “A super-polynomial lower bound for regular arithmetic formu-las”; Neeraj Kayal, Chandan Saha, Ramprasad Saptharishi; STOC2014.

Invited Talks


Awards and fellowships

• Ramanujan Fellowship of the Department of Science and Technol-ogy, Govt. of India. 2017

• Winner of the ACM India Doctoral Dissertation Award, 2013.

• Co-winner of Best Paper award in CCC 2013.

Program Committee(s) FSTTCS 2015


• PhD Students: Anamay Tengse (current), Prerona Chatterjee (cur-rent)

• Courses: Algebra and Computation, Polynomial Methods in Com-binatorics (co-taught with Jaikumar Radhakrishnan), AlgebraicComplexity Theory, Ideals, varieties and algorithms (reading course).

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Pranab Sen



• McGill University, Canada. Visiting Professor (2010 – 2011).

• NEC Laboratories America, Princeton, USA. Research Staff Mem-ber (2005 – 2006).

• Department of Combinatorics and Optimization, University of Wa-terloo, Canada. Postdoctoral Researcher (2003 – 2004).

• LRI, Universite Paris-Sud, France. Postdoctoral Researcher (2001

– 2002).


• PhD, Computer Sciece, TIFR Mumbai, 2001.

• BTech, Computer Science, IIT Bombay, 1994.

Research Focus

Pranab’s broad research area is quantum computation and quantuminformation theory. Quantum computation harnesses the extra in-formation processing capabilities offered by quantum bits and quan-tum operations in order to perform computational tasks. Pranab hasworked on diverse topics in quantum computation e.g. hidden sub-group problem and quantum communication complexity. He hasalso worked on a few topics in quantum cryptography e.g. zeroknowledge against quantum attacks and quantum information lock-ing. Presently he is working in quantum information theory with afocus on one shot results for quantum multiterminal channels.


• Radhakrishnan, J., Sen, P., Warsi, N., One-Shot Marton Inner Boundfor Classical-Quantum Broadcast Channel, IEEE Trans. InformationTheory 62(5), pp. 2836-2848, 2016.

• Friedl, K., Ivanyos, G., Magniez, M., Santha, M., Sen, P., HiddenTranslation and Translating Coset in Quantum Computing, SIAM J.Comput. 43(1), pp. 1-24, 2014.

• Fawzi, O., Hayden, P., Sen, P., From Low-Distortion Norm Embed-dings to Explicit Uncertainty Relations and Efficient Information Lock-ing, J. ACM 60(6), pp. 44:1-44:61, 2013.

• Sen, P., Achieving the Han-Kobayashi inner bound for the quantuminterference channel, IEEE Symp. Information Theory, pp. 736 -740, 2012.

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pranab sen

• Hallgren, S., Moore, C., Rotteler, M., Russell, A., Sen, P., Limita-tions of quantum coset states for graph isomorphism, J. ACM 57(6), pp.34:1-34:33, 2010.

• Jain, R., Radhakrishnan, J., Sen, P., A property of quantum relativeentropy with an application to privacy in quantum communication, J.ACM 56(6), pp. 33:1-33:32.

• Hallren, S. Kolla, A., Sen, P., Zhang, S., Making Classical HonestVerifier Zero Knowledge Protocols Secure against Quantum Attacks, Int.Coll. Aut. Lang. Prog. (ICALP), pp. 592-603, 2008.

Invited Talks

• Graph isomorphism, the hidden subgroup problem and identifying quan-tum states, Work. on Quant. Inf. Proc., Paris, 2006.

• Johnson-Lindenstrauss lemma and unitary t-designs, Workshop on Quant.Inf.: Codes, Geometry and Random Structures, CRM, Montreal,2011.


Awards and Fellowships

• Programme Committee Member for QIP 2017, AQIS 2017, FSTTCS2015, AQIS 2014, TQC 2013, TQC 2011, QIP 2008, FSTTCS 2007

conferences.

• The paper From Low-Distortion Norm Embeddings to Explicit Uncer-tainty Relations and Efficient Information Locking, was presented as aplenary talk at the QIP 2011 Worskhop.

• The paper Making classical honest verifier zero knowledge protocolssecure against quantum attacks, authored by S. Hallgren, A. Kolla,P. Sen and S. Zhang won the best paper award at the ICALP 2008

conference, Track C on Cryptography.


• Ph.D. Students: Naqueeb Warsi (2014 – 2015), Sayantan Chakraborty(current), Aditya Nema (current).

• Courses: Quantum Computation and Information, Probability The-ory, Computational Complexity, Information Theory, Mathemati-cal Structures.

Other Services

• Referee for STOC, FOCS, SODA, CCC, ICALP, STACS, TAMC,ISIT, TQC, QIP, AQIS conferences, SICOMP, JCSS, TCS, IEEE Trans.Info. Theory, QIP journals.

• Local Organiser for QIP 2008 conference.

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Piyush Srivastava



• Center for the Mathematics of Information, Computing and Math-ematical Sciences department, California Institute of Technology,USA. Post-doctoral scholar (2014 – 2016).


• BTech, Computer Science and Engineering, IIT Kanpur, 2009.

• PhD, Computer Science, University of California, Berkeley, 2014.

Research Focus

Piyush Srivastava’s research is largely on probabilistic structures incomputation. His current interests include the study of samplingproblems, often those related to probabilistic graphical models. Alarge part of his recent work has focused on understanding the con-nections between phase transitions in statistical physics, and algo-rithms and computational complexity.


• N. J. A. Harvey, P. Srivastava, and J. Vondrák. Computing theindependence polynomial: from the tree threshold down to theroots. Extended abstract in Proceedings of ACM-SIAM SODA, 2018.

• Q. Berthet, P. Rigollet, and P. Srivastava. Exact recovery in theIsing blockmodel. To appear in the Annals of Statistics.

• J. Liu, A. Sinclair, P. Srivastava. The Ising Partition Function: Zerosand Deterministic Approximation. Extended abstract in Proceed-ings of IEEE FOCS, 2017.

• I. Panageas, P. Srivastava, N. K. Vishnoi. Evolutionary dynamicsin finite populations mix rapidly. Extended abstract in Proceedingsof ACM-SIAM SODA, 2016.

• L. J. Schulman, A. Sinclair, P. Srivastava. Symbolic integrationand the complexity of computing averages. Extended abstract inProceedings of IEEE FOCS, 2015.

• A. Sinclair, P. Srivastava, D. Štefankovic, Y. Yin. Spatial mixingand the connective constant: Optimal bounds. Probability Theory &Related Fields, 168 (1-2) (July 2016), pp. 153-197. Based on extendedabstracts in Proceedings of ACM-SIAM SODA, 2015 and Proceedingsof IEEE FOCS, 2013.

• A. Sinclair, P. Srivastava. Lee-Yang theorems and the complexity ofcomputing averages. Communications in Mathematical Physics, 329(3) (Aug. 2014), pp. 827–858. Extended abstract in Proceedings of

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piyush srivastava

ACM STOC, 2013. Presented as an invited tutorial at the IEEE FOCS2013 Workshop on “Zeros of Polynomials and their Applications”.

Invited Talks

• Exact recovery in the Ising blockmodel, Dagstuhl seminar on “Com-putational Counting”, August, 2017.

• Weitz’s algorithm and the Lovász local lemma, “Counting complexityand Phase transitions” – reunion workshop at the Simons Institutefor the Theory of Computing, University of California, Berkeley,June 2017.

• The complexity of computing averages, “The classification programof counting complexity” workshop at the Simons Institute for theTheory of Computing, University of California, Berkeley, March2016.

• Mixing time of stochastic evolutionary dynamics, Dagstuhl seminaron “Evolution and Computing”, January 2016.

• Correlation decay, phase transitions, and counting, “Information The-ory and Applications” meeting at UC San Diego, February 2015.

• Phase transitions, zeros of polynomials and the computational complex-ity of problems in statistical physics, “Western States Mathemati-cal Physics Meeting”, California Institute of Technology, February2015; and “IEEE Symposium on Foundations of Computer Science(FOCS) Workshop on Zeros of polynomials and their applications”,Berkeley, October 2013.


Research support and fellowships

• Ramanujan Fellowship of the Department of Science and Technol-ogy, Govt. of India.

• Postdoctoral fellowship of the Center for the Mathematics of In-formation at Caltech, 2014-2016.

• Berkeley Fellowship for Graduate Study, 2009.

Service

• Journal Reviewing: Annals of Applied Probability, Theory of Comput-ing, Information and Computation.

• Conference proceedings reviewing: ACM STOC, ACM-SIAM SODA,APPROX-RANDOM, FSTTCS, ICALP, ICTS, IEEE FOCS, STACS.


• Courses: Numerical algorithms, Analysis of Markov chains

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Rahul Vaze



• Beceem Communications Pvt. Ltd., Bangalore, India. Design En-gineer (2004 – 2006).


• ME Telecommunications, Electrical Communication Engineering,IISc, Bangalore, 2004.

• PhD, Electrical and Computer Engineering, University of Texas atAustin, 2009.

Research Focus

Rahul’s research is largely on non-Ergodic theory of communication,stochastic geometry based analysis of wireless networks and combi-natorial online algorithms.


• R. Vaze and S. Iyer, “Capacity of Cellular Wireless Networks”, inProc. WiOpt, May 2017, Paris.

• S. Iyer, R. Vaze, “Achieving Non-Zero Information Velocity inWireless Networks”, Annals of Applied Probability. Volume 27,Number 1 (2017), 48-64.

• A. Pananjady, V. Bagaria, and R, Vaze, “Optimally Approximatingthe Coverage Lifetime of Wireless Sensor Networks”, in IEEE/ACMTransactions on Networking, vol. 25, no. 1, pp. 98-111, Feb. 2017.

• S. Satpathi, R. Nagda and R. Vaze, “Optimal Offline and Compet-itive Online Strategies for Transmitter-Receiver Energy Harvest-ing”, in IEEE Transactions on Information Theory, vol. 62, no. 8,pp. 4674-4695, Aug. 2016.

• Bagaria, A. Pananjady, and R. Vaze, “The Online Disjoint Set CoverProblem and its Applications”, in Proc. IEEE INFOCOM Apr.2015, Hong Kong.

• Tianyang Bai, Vaze, and R. Heath, R.W., “Analysis of Blockage Ef-fects on Urban Cellular Networks”, IEEE Transactions on WirelessCommunications, vol. 13 , no. 9, Sept. 2014 , pp. 5070 -5083.

• R. Vaze and R. W. Heath Jr., “Transmission Capacity of Ad-hocNetworks with Multiple Antennas using Transmit Stream Adap-tation and interference Cancelation”, IEEE Transactions on Infor-mation Theory, vol. 58, Feb. 2012, pp. 780-792.

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rahul vaze

Invited Talks

• Capacity of Cellular Wireless Networks, IIT Bombay, February 2018;and IISc Bangalore, September 2017.

• Online energy efficient packet scheduling for a common deadline, IIScBangalore, September 2016.

• Graph Matchings and Wireless Communication, ASET Talk, TIFR,March 2016.

• Achieving Non-Zero Information Velocity in Wireless Networks, IISc-DRDO Workshop on Models and Protocols for Mobile Ad-HocNetworks, IISc Bangalore, Oct 2015; and Simons Conference onNetworks and Stochastic Geometry, The University of Texas atAustin, May 2015.

• Achieving Non-Zero Information Velocity in Wireless Networks, Tele-com Paris Tech, July 2015.

• Optimal WiFi Sensing Via Dynamic Programming, Alcatel Lucent BellLabs, Paris, July 2015.

• Tutorial on Combinatorial Optimization in Wireless Networks, NationalConference on Communications, IIT Bombay, February 2015.

• Being Greedy is Good : Approximating the FDMA Capacity, CUHK,Hong Kong, November 2014.



• TPC Co-Chair for IEEE Globecom 2014, Ad Hoc Networks Track.

• TPC Co-Chair for IEEE SPCOM 2016.

• Served as the Editor of the IEEE Journal on Selected Areas of Com-munications starting from 2015-2016.

• Currently serving as the Editor of the IEEE Trans. on GreenComm. and Networking since 2016.

• TPC Member: IEEE SPCOM 2012, 2014, IEEE ICC 2011, 2012, 2013,IEEE WCNC, 2013, 2014, WiOpt 2014, 2015, 2016, NCC 2012, 2013,2014, 2015, 2016, 2017, 2018, IEEE MobiHoc 2013.

Awards

• Won the National Academy of Science India’s Young ScientistAward for the year 2015.

• Won the Ramanath Cowsik Medal from TIFR in 2014 for best pa-per in last 5 years for people under the age of 35 from TIFR.

• Won the best paper award in the Networks track of the NationalConference on Communications 2014 held at IIT Kanpur for thepaper “Maximizing Utility Among Selfish Users in Social Groups”co-authored with Ashwin Pananjady and Vivek Bagaria.

• Won the Indian National Academy of Engineering’s Young Engi-neer Award for the year 2013.

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• Won the Indian National Science Academy’s Young Scientist Awardfor the year 2013.

• Won the Eurasip Best Paper Award for the best journal paper pub-lished in Eurasip Journal on Wireless Communication and Net-working for the year 2010.


• Post-docs: Sharayu Moharir (2014-15)

• PhD Students: Shishir Pandey

• Courses: Multiple antenna communication, Information Theory,Machine Learning, Stochastic Geometry, Wireless Communications,Online Algorithms, Learning Theory, Communications, Multi-userInformation Theory.

Other Services

• Uncoordinated, secure and energy aware access in distributedwireless networks, DeiTy, Govt. of India, 2014-17. Co-PI’s Prof.Bikash Dey, Dr. Sibi Pillai, IIT Bombay, Dr. Vinod Prabhakaran,TIFR-Mumbai, Dr. Sripati Acharya, NIT-Surathkal, Dr. SumeetKundu, NIT-Durgapur.

• Indian National Science Academy’s Young Scientist Award Grant:Design of Efficient Spatial Wireless Networks using Stochastic Ge-ometric tools. 2014-18.

• CEFIPRA Indo-French joint program on D2D Communications forLTE-Advanced Cellular Networks. 2015-18, co-PI’s Prof. NeeleshMehta, Prof. Chandra Murthy, Indian Institute of Science, Prof.Ketan Rajawat, IIT Kanpur, Marceau Coupechoux, Telecom ParisTech, Amira Alloum, Nokia Siemens, Cedric Adijh Inria.

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adjunct and associate faculty

Adjunct and Associate Faculty

Associate Professor: Riddhipratim Basu

Riddhipratim Basu is a Reader at the International Centre for Theo-retical Sciences (ICTS) of the Tata Institute of Fundamental Research(TIFR).. Before joining ICTS in 2017, he was the Szegö Assistant Pro-fessor of Mathematics at Stanford University. He is a recipient of theRamanujan Fellowship, the Simons Junior Faculty Fellowship andthe Loève Fellowship. His research interests are in probability the-ory and applications, with special focus in problems coming fromstatistical physics and theoretical computer science. Recent interestsinclude first and last passage percolation, geometry of random inter-faces, interacting particle systems, among other topics.

Adjunct Professor: Vivek Shripad Borkar

Vivek Shripad Borkar is an Institute Chair Professor at the IIT Bom-bay. He is known for introducing analytical paradigm in stochasticoptimal control processes and is an elected fellow of all the threemajor Indian science academies viz. the Indian Academy of Sci-ences, Indian National Science Academy and the National Academyof Sciences, India. He also holds elected fellowships of The WorldAcademy of Sciences, Institute of Electrical and Electronics Engi-neers, Indian National Academy of Engineering and the AmericanMathematical Society. He received the TWAS Prize of the WorldAcademy of Sciences in 2009.

Adjunct Professor: Devavrat Shah

Devavrat Shah is a Professor with the department of Electrical En-gineering and Computer Science at Massachusetts Institute of Tech-nology. His current research interests are at the interface of Statisti-cal Inference and Social Data Processing. His work has been recog-nised through prize paper awards in Machine Learning, OperationsResearch and Computer Science, as well as career prizes including2010 Erlang prize from the INFORMS Applied Probability Societyand 2008 ACM Sigmetrics Rising Star Award. He is a distinguishedyoung alumni of his alma mater IIT Bombay.

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Graduate Students

Shubhada Agrawal

Education

• Integrated MTech (Mathematics and Computing, IIT Delhi)

• At TIFR since August, 2017

Research interests/recent work: Applied probability and finance.

Siddharth Bhandari

Education

• BTech (Mechanical Engineering, IIT Kharagpur)

• MSc (Computer Science, CMI, Chennai)


Research interests/recent work: Complexity theory and Coding the-ory. Trying to understand when codes are efficiently locally testableand decodable. Showed that the zero-error list-decoding capacity ofthe q/(q − 1) channel is exponentially small unless the list size isΩ(q lg q).

Arghya Chakraborty

Education

• BMath Hons (Mathematics, ISI Bangalore)


Research interests/recent work: Automata Theory, Algorithms, Com-plexity theory.

Sayantan Chakraborty

Education

• BTech (Electronics and Communication Engineering, IEM, Kolkata)

• MTech (Signal Processing and Communication, IIT Kanpur)


Research interests/recent work: Quantum information theory, espe-cially one-shot protocols in quantum information. Currently study-ing the convex-split lemma and its applications to one shot protocols.

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graduate students

Somnath Chakraborty

Education

• BSc (University of Calcutta)

• MSc (Mathematics, TIFR Bangalore)

• MA (Mathematics, Indiana University, USA)


Research interests/recent work: Manifold Learning. Currently study-ing abundance of epsilon-dense subsets of compact Riemannian man-ifolds, especially symmetric spaces. This relates to questions aboutexistence of spectral gap of some naturally arising (Markov) opera-tor, noncommutative diophantine approximation.

Prerona Chatterjee

Education

• BSc Hons (Mathematics, St Xavier’s College, Kolkata)

• MSc (Mathematics and Computing, IIT Guwahati)


Research interests/recent work: Complexity theory, especially algebraiccomplexity theory. Currently studying the advantages and draw-backs of the algebraic-independence based approaches for polyno-mial identity testing; also interested in the algebraic independencetesting question.

Anand A. Deo

Education

• BE (Electronics Engineering, DJ Sanghvi College of Engineering,Mumbai)


Research interests/recent work: Probability, machine learning, espe-cially applications in finance. Currently working on developing prov-ably efficient simulation and calibration techniques for credit risk(the risk that a financial firm defaults on a loan).

Kshitij Gajjar

Education

• BTech (Computer Science and Engineering, Manipal Institute ofTechnology)


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Research interests/recent work: Algorithms, graph theory, combina-torics. Proved optimal bounds for the problem of finding smalldistance-preserving subgraphs of interval graphs. Currently work-ing on the parametric shortest path problem for planar graphs.

Gunjan Kumar

Education

• BTech (Computer Science and Engineering, IIT Guwahati)


Research interests/recent work: Property testing, combinatorics. Cur-rently working on property testing of valuation functions.

Gowtham R Kurri

Education

• BTech (Electronics and Communication Engineering, IIIT Hyder-abad)


Research interests/recent work: Information theory, machine learning.Obtained characterisation for the rate of communication required fortwo parties to generate correlated sequences when they share in-dependent randomness with a coordinator. Obtained single-letterexpressions for feasibility and optimal rates of communication forinteractive computation of randomised functions by two users.

Phani Raj Lolakapuri

Education

• BS-MS (Mathematics major, Physics minor, IISER Trivandrum)


Research interests/recent work: Algorithmic Game Theory. Studiedthe computation of equilibria in problems on traffic networks; ob-tained an efficient algorithm for computing equilibria in certain net-works; showed NP-hardness for others. Currently studying prob-lems on social networks to determine the optimal opinion a playermight form given the opinion of others.

Nikhil Mande

Education

• BMath Hons (ISI Bangalore)

• MSc (Applications of Mathematics, CMI, Chennai)

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graduate students


Research interests/recent work: Communication complexity, booleancircuit complexity. Have worked on the communication complex-ity of XOR functions under various randomised models of commu-nication, with applications to boolean circuit complexity. Recentlyshowed that general depth-two threshold circuits are more power-ful than those where the weights of the bottom threshold gates arebounded.

Tulasimohan Molli

Education

• BSc Hons (Mathematics and Computer Science, CMI, Chennai)

• MSc (Computer Science, CMI, Chennai)


Research interests/recent work: Complexity theory, especially CircuitComplexity and Communication Complexity. Currently studyingvarious polynomial representations of boolean functions which areefficiently computable by circuits. This has applications in provinglower bounds and derandomisation of algorithms in various compu-tational models.

Varun Narayanan

Education

• BTech (Electronics and Communication, NIT Calicut)

• MTech (Control and Computing, IIT Bombay)


Research interests/recent work: Information theory, especially its ap-plications in Secure Multiparty Computing. Recently worked on se-cure computation in incomplete networks and a notion of privacy inindex coding. Currently working on information-theoretic boundsfor secure multiparty computing.

Neha

Education

• BE (Electrical Engineering, Delhi College of Engineering)

• MSc (Applications of Mathematics, CMI, Chennai)


Research interests/recent work: Information Theory and its applica-tions.

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Aditya Nema

• BE (Electronics and Telecommunication Engineering, SGSITS, In-dore)

• At TIFR since 2013

Research interests/recent work: Classical and quantum information the-ory. Studied the super-additive capacity of random quantum chan-nels obtained from a unitary t-design; studied the gate fidelity forunitary 4-design. Currently interested in concentration of measureinequality for decoupling theorems and rate distortion function forone-shot classical and quantum message compression.

Vidya Sagar Sharma

Education

• BTech (Computer Science, NIT Patna)

• MTech (Theoretical Computer Science, IIT Guwahati)


Research interests/recent work: Algorithms, automata, combinatorialoptimisation.

Suhail Sherif

Education

• BTech (Computer Science and Engineering, IIT Guwahati)


Research interests/recent work: Communication complexity and querycomplexity. Studying, among other things, the extent to which ran-domness can help in various models of computation.

Abhishek Kr Singh

Education

• BTech (Computer Science and Engineering, West Bengal Univer-sity of Technology, Kolkata)

• MTech (Media and Sound Engineering, IIT Kharagpur)


Research interests/recent work: Mathematical logic, type theory andcomputability theory. Working on the formalisation of Mathematicson a theorem prover, by formally verifying results on finite math-ematical structures using the Coq Proof Assistant. By creating a li-brary of facts on finite partially ordered sets, formally verified impor-

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graduate students

tant results from combinatorics such as Dilworth’s theorem, Mirsky’stheorem, Hall’s marriage theorem and the Erdos-Szekeres theorem.

Anamay G Tengse

Education

• BE (Computer Engineering, Goa Engineering College)

• MTech (Computer Science and Engineering, IIT Bombay)


Research interests/recent work: Complexity theory, especially algebraiccomplexity theory. Currently studying the polynomial identity test-ing question, in particular for circuit classes related to read-once al-gebraic branching programs (ROABP). Recently studied a generali-sation of ROABPs and extended the state-of-the-art hitting sets forROABPs to the generalised setting.

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Postdoctoral Fellows

Palash Dey


• Master of Engineering from IISc, 2013.

• PhD from IISc, 2017.

Research Area Computational Social Choice.


• “Manipulative Elicitation – A New Attack on Elections with In-complete Preferences”. To appear in AAAI 2018. (single author).

• “Query Complexity of Tournament Solutions”. In Proc. AAAI2017. (single author)

Subhashini Krishnasamy


• BTech degree in Electronics and Communication Engineering fromJawaharlal Nehru Technological University, Hyderabad, 2009.

• ME degree in Telecommunications from Indian Institute of Sci-ence, Bengaluru, 2011.

• PhD from the Department of Electrical and Computer Engineeringat The University of Texas at Austin, USA, May 2017.

Research Area Online learning and control algorithms.


• S. Krishnasamy, R. Sen, R. Johari, S. Shakkottai, Regret of Queue-ing Bandits, Conference on Neural Information Processing Systems(NIPS), December 2016.

• S. Krishnasamy, R. Sen, S. Oh, S. Shakkottai,Detecting SponsoredRecommendations, ACM International Conference on Measurementand Modeling of Computer Systems (SIGMETRICS), June 2015.

Raj Mohan Matteplackel


• BTech, Department of Computer Science, (Govt.) College of Engi-neering, Trivandrum (CET), 1999-2003.

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postdoctoral fellows

• ME, Department of Computer Science and Automation, IndianInstitute of Science, 2003-2005.

• PhD, Department of Computer Science and Automation, IndianInstitute of Science, 2005-2012.

Previous Post-doc Experience Post Doctoral Fellow, Laboratoire Spé-cification et Vérification, ENS Cachan, Paris, France, 2013 – 2015.

Research Area Automata theory, Formal methods, Verification, Au-tomated controller synthesis, Temporal logics.


• Raj Mohan Matteplackel, Paritosh K. Pandya, and Amol Wakankar,Formalizing Timing Diagram Requirements for Model Checkingand Synthesis, SEFM 2017.

• Amol Wakankar, Paritosh K. Pandya, and Raj Mohan Matteplackel,DCSYNTH: Guided Reactive Synthesis with Soft Requirementsfor Robust Controller and Shield Synthesis, Submitted to TACAS2018.

Sumedh Tirodkar


• BTech in Computer Engineering from COEP, Pune, 2007

• MTech from CSE, IIT Bombay, 2009.

• PhD from CSE, IIT Bombay under guidance of Prof. Sundar Vish-wanathan, 2017.

Research Area approximation algorithms, streaming algorithms, andonline algorithms.


• Maximum Matching in Two, Three, and a Few More Passes OverGraph Streams in APPROX 2017, with Sagar Kale.

• On Randomised Algorithms for Matching in the Online Preemp-tive Model in ESA 2015, with Ashish Chiplunkar and Sundar Vish-wanathan.

Marc Vinyals


• Undergraduate studies in mathematics at Universitat Politecnicade Catalunya, Barcelona.

• PhD in Computer Science at KTH Royal Institute of Technology,Stockholm, supervised by Jakob Nordstrom.

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Research Area Computational complexity, and proof complexity inparticular.


• “How limited interaction hinders real communication (and whatit means for proof and circuit complexity)” in FOCS 2016, withS. F. de Rezende and J. Nordström, in which we prove size-spacetrade-offs for cutting planes and a separation between monotone-ACi and monotone-NCi+1.

• “Hardness of Approximation in PSPACE and Separation Resultsfor Pebble Games” in FOCS 2015, with Siu Man Chan, MassimoLauria and Jakob Nordström in which we prove that pebble gamesare PSPACE-hard to approximate within an additive constant.

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scientific staff

Scientific Staff

Nandini Bondale

Education History

• BSc, Chemistry-Physics, University of Bombay 1979

• MSc, Biology-Physics, University of Bombay 1981

• MA, Linguistics, University fo Bombay 1991

• PhD, Computational Linguistics, University of Mumbai 1998

Employment History

• Scientific Officer, TIFR, ’91 – current

Nature of work Worked in the area of Speech and Natural LanguageProcessing. Have contributed in the area of m-Health (Mobile Healthtechnology). Currently pursuing her interest in ‘Technology for Health’by studying Biofield Scans and its evaluation. In the area of speech,focusing on the ‘Prosody for Indian Languages’.

Selected Publications

• N. Bondale and A. Deo, “The ‘Unseen Body’: Biofield Scanningfor Detection and Prediction of Health Issues” Proceedings of the‘5th International Conference Science and Scientist - 2017’, Kath-mandu, Nepal, 2017

• S. Barhate, S. Kshirsagar, N. Sanghvi, K. Sabu, P. Rao and N. Bon-dale, “Prosodic features of Marathi news reading style” Proceed-ings of ‘TENCON 2016, IEEE Region 10 Conference’, Singapore,2016, pp. 2215-2218

• N. Bondale, V. Surve, M. Nadkarni, O. Parkhi, P. Joshi, A. Pandey,“Issues in developing pronunciation lexicon for Marathi”, Pro-ceedings of the Oriental COCOSDA held jointly with 2013 Con-ference on Asian Spoken Language Research and Evaluation, 2013

International Conference

• N. Bondale, S. Kimbahune, A. Pande, “mHEALTH-PHC: An ICTTool for Primary Healthcare in India”, IEEE Technology and Soci-ety Magazine, Vol 32, Num 3, Fall 2013.

• N. Bondale, S. Kimbahune and A. Pande, ‘Rural Community Healthin India: Problems & Solutions’ in the book ‘Mobile Health (mHealth)Multidisciplinary Verticals’, CRC Press, November 2014

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Nitin S Gawandi

Education History

• BE Electrical Engineering, Mumbai

• Diploma in Industrial Electronics, Mumbai

Employment History

• Laboratory Assistant, Babasaheb Gawade Institute of Technology,Mumbai( May ’01 – June ’03)

• TIFR, Laboratory Assistant (2003 – 2009), Scientific Officer (2009 –present).

Nature of work Responsibilities include systems and network ad-ministration in STCS and ISDG, server management in STCS, keep-ing record of departmental capital equipment inventory.

Ravikumar Naik

Education History

• BE (Computer Science and Engineering), 2000

• MTech (Computer Network Engineering), 2005

Employment History

• Scientific Officer, STCS, TIFR (2006 – present)

Nature of work IT Manager in STCS. Responsibilities include design,implementation and maintenance of new technology, evaluating thefunctionality of systems and ensuring reliability, purchasing appro-priate hardware and software, implementing and managing systemsecurity and integrity, institute usage policy and software licensinglaws.

Administrative Staff

John Barretto

Education History

• SSC (1975)

• First Year Arts (1977)

Employment History

• Lower Division Clerk (LDC) with the Directorate of Purchase andStores, Department of Atomic Energy, 1978 – 1983

• TIFR, Technical Typist (1983 – 1990), Superintendent (1990 – 1995),Assistant Administrative Officer (1995 – 2006), Administrative Of-ficer (2006 – 2017)

• Consultant, STCS, TIFR, (2017 – present)

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administrative staff

Nature of work Secretarial support for STCS, Dean TCSF, ISDG andComputer and Systems Sciences Subject Board.

Pravin N Bhuwad

Education History

• SSC (1979)

• FYJC in Arts (1980)

• Certificate course in radio and audio servicing (1986)

• Certificate course in television servicing (1987)

Employment History:

• TIFR, Laboratory Assistant (1989 – 1999), Technical Assistant (1999

– present)

Nature of work Responsibilities include repairing and servicing ofelectronics and electrical equipments, designing and assembling elec-tronics circuits, cash purchasing of STCS electronics material andhandling miscellaneous needs of faculty and students in STCS.

Waman K Gawade

Education History

• SSC (1991)

Employment History

• TIFR, Gardener (2001 – 2008), Work Assistant (2008 – present)

Nature of work Responsibilities include most administrative workrelated to STCS such as helping out in seminars/workshops, cashpurchase, collecting postal mails from mailing section, lecture an-nouncements on notice boards, photocopying of materials, shifitingof materials, etc.

Supriya Pottipati

Education History

• BSc, Computers, S V University, 2007

• MBA, HR, Madras University, 2012

Employment History

• HR Executive, SKS Microfinance Limited, (2007 – 2012)

• Assistant Manager HR, Panoramic Holidays Ltd, (2013 – 2015)

• HR Admin Manager, Lloyd Healthcare Pvt Ltd, (2015 – 2017)

• Administrative Assistant, TIFR, (2017 – present)

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Nature of work Secretarial support for STCS, Dean TCSF, ISDG andComputer and Systems Sciences Subject Board.

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Algorithms

Algorithms are the logical building blocks of many modern systems,not just in computing, but in diverse fields such as economics, oper-ations research, and transportation. They provide the tools to buildcomplex systems which, in a rigorous mathematical sense, satisfycriteria such as speed, optimality, space-efficiency, and distributedcontrol. Many important algorithms and data structures are imple-mented as toolboxes, and are used widely in scientific and generalapplications.

Linear programming is viewed as a revolu-tionary development giving man the abilityto state general objectives and to find, bymeans of the simplex method, optimal pol-icy decisions for a broad class of practicaldecision problems of great complexity.

- George Dantzig(“Reminiscences about the origins of

linear programming” 1983)

The impact of algorithms is perhaps best illustrated by two exam-ples — the simplex algorithm for linear programming, and the Gale-Shapley algorithm for stable matching. Linear programs are usedfor finding an optimal solution that satisfies some linear constraints.Linearity, however, is frequently a good first-order approximationto more complicated functions, and hence linear programming, andthe simplex algorithm for solving these, are widely used to modeland find solutions to problems in many different fields includingthose mentioned above. From the first use of linear programmingfor planning during the second World War to this day, designingfaster methods for solving linear programs continues to be a majorresearch direction in algorithms.

The second problem, that of obtaining a stable matching, is facedfrequently by systems that assign people, e.g., students to schools,or residents to hospitals, where people have preferences over theirassignments. The Gale-Shapley algorithm describes how to obtaina matching that is stable, i.e., no two people can exchange their as-signments and be better off. The 2012 Nobel prize in economics wasawarded to Shapley and Roth for their work in this area. A versionof this algorithm is now used in admissions to engineering schoolsin India.

The rapid growth in data, and the use of machines in decision-making processes, both mean that algorithms will grow rapidly inimportance, though the focus will be more diverse. Traditionally, re-search in algorithms is driven by worst-case analysis, fixed inputs,and algorithms that run in time polynomial in the input size, as op-posed to exponential. Building on these results, modern algorithmsthat deal with data will be required to run much faster, often in timelinear or even sublinear in the size of the input. The input may notbe fixed a priori, but may arrive and change over time, such as in-

algorithms

put from an array of sensors, or immediate data regarding financialtransactions. Algorithms for such varying inputs are called online,dynamic, or streaming algorithms, depending on the precise con-straints and nature of data arrival, and are recently the subject ofintense research and emphasis. Algorithms for decision-making im-ply that algorithms which can learn to identify or separate inputsare important. This is one of the goals of machine learning. Finally,algorithms increasingly are used in systems which are not centrallycontrolled, but where multiple agents interact. For example, soft-ware platforms run millions of auctions every day, where the bid-ding is done by automated agents. Social networks are formed bylinks formed by individual agents, and are analysed and used bycompanies for marketing. Algorithmic game theory studies the useof algorithms in these systems, where the concern is not just speed oroptimality, but also ensuring that the output or outcome generatedis stable with regard to the agents.

A perfect matching in a graph

The algorithms group in STCS currently focus on graph theoryand algorithmic game theory. Graphs are a natural way to rep-resent many problems involving interactions between components,and hence form a basic tool to develop further algorithms. For ex-ample, the stable matching problem above is a graph problem. Ourmembers have recently studied generalisations of the stable match-ing problem, namely popular matchings. As in the stable matchingproblem, every vertex has a preference over its neighbours, such asstudents ranking schools and vice-versa, or applicants ranking train-ing posts. Stability is a classical goal for the assignment, but is alsovery strict — for instance, a stable matching could leave half theagents unassigned. A matching is popular if it is weakly preferred bya majority to any other matching, i.e., a popular matching is a weakCondorcet winner in the set of matchings. Every stable matching is apopular matching, in fact, it is a popular matching of minimum size.A maximum size popular matching is a promising candidate for aglobally stable matching that achieves a greater social good than astable matching, since its size is larger. We have studied severalproblems in popular matchings and designed efficient algorithms forsome of them. We now understand the limits of tractability in thisproblem and some of its generalisations. Other graph algorithmswe have worked on include problems such as computing approxi-mate distances and spanners efficiently, computing minimum cyclebases in undirected and directed graphs efficiently, and variants ofthe min-cut problem in undirected graphs.

A

B

C

D

W

X

Y

Z

W > Y > X > Z

Y > Z > W > X

Z > X > Y > W

Y > X > W > Z

B > A > C > D

D > A > B > C

A > C > B > D

B > C > A > D

A stable matching

In algorithmic game theory, our research is on congestion in trans-portation networks, and preference revelation. The game-theoreticmodelling of traffic in road networks dates back to 1920s, but thesemodels are used for many other networked systems, including inter-net traffic and load balancing across multiple servers. These modelsare used by researchers to plan for pricing, capacity augmentation,and other methods of congestion control. Typically the outcome ofinterest is the equilibrium, which is a stable outcome with respect

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research highlights: umang bhaskar

to the agents in the system, such as drivers in a road network. Ourwork studies various aspects of equilibria in these models, includingefficient algorithms for computing equilibria, and theoretical boundson the optimality of equilibria. Fast algorithms for computing equi-libria are of great importance as they can be used for simulating theeffect of various measures to reduce congestion. A second strand ofresearch studies games from the perspective of observing behaviourof the agents in the system, rather than the actual costs. As a con-crete example, in setting prices for objects the actual utility of buyersmay not be obtainable, but a seller can observe the quantities boughtat various prices. We study algorithms for learning the utilities ofagents from these observations, as well as optimising objectives whenthe utilities cannot be learned thus.

Besides these problems, another area we are currently working onis voting mechanisms that accurately capture the utilities that vot-ers have for the candidates. This is a classical problem, but hasrecently gained prominence with the rise of participatory democ-racy, where members of a local population directly vote on issuesthat affect them, such as allocation of municipal budgets. Truthfulmechanisms were earlier characterised by Gibbard and Satterthwaite,which often lead to strong lower bounds on the utility of any truth-ful mechanism. We are interested in devising mechanisms that cancircumvent these lower bounds, either by relaxing truthfulness, or byconsidering restricted instances of the problem.

Several of the optimisation and game-theoretic problems that wedescribe have a wealth of applications, and we are interested to seeif our algorithms can be used in these applications.

Looking forward, we believe there is a lot of scope for growth forthe group at STCS. We would like to add competence both in ar-eas where the members are currently active, as well as the researchdirections mentioned earlier. Currently our research areas mainlyspan graph algorithms and algorithmic game theory: we hope togrow particularly in the areas of approximation algorithms, compu-tational geometry, dynamic, online, and streaming algorithms, andalgorithms for large data sets.

We also note that the theoretical focus of STCS offers a uniqueopportunity for interactions among faculty. Many members in STCSoutside the algorithms group have either worked on or are interestedin algorithms and their applications, including online and approxi-mation algorithms, randomised algorithms, learning, and game the-ory. While we have worked on joint research projects, there is a lotof scope for increased interaction. In addition to adding new mem-bers, these interactions present another avenue for growth for thealgorithms group.

Research Highlights: Umang Bhaskar

Algorithmic game theory aims to understand the computational is-sues that arise when multiple agents interact, and agents are selfish,

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i.e., they optimise their own objective. For example, in road traffic,agents are drivers, and each driver tries to minimise the time shetakes to reach her destination in the presence of other drivers. In

Road and data networks are twoexamples where routing games are

extensively used to both analyse andinfluence traffic

an auction, a seller and multiple buyers interact. The seller seeksto maximise her profit, while the buyers seek to minimise the priceat which they obtain the item. Game theory typically studies sta-ble outcomes in these games, most frequently the Nash equilibrium.An outcome is an equilibrium if no agent can improve her utility inthe game by playing differently. As systems grow larger and morecomplex, the assumption of centralised control in many of these sys-tems becomes more unsustainable, and the relevance of game the-ory, where agents act individually, as a model for decentralised be-haviour increases. I am interested in game theory as a natural toolto understand interactions between many utility-maximising agents,and in particular algorithmic and computational issues that arise ingames. I describe below my contributions in two areas in algorithmicgame theory: routing games, and the revealed preference approachto analysing games.

Routing Games. In many situations, e.g., road networks and datanetworks, multiple agents need to travel from a source to a desti-nation in a network. Each agent must decide on a route from thesource to the destination, with the objective of minimising her owndelay. Routing games are used to understand the impact of this de-cision on traffic and delays in such networks. The model of routinggames is rich enough to describe a number of problems in very dif-ferent fields: routing games are used to model traffic flows on roads,transportation of freight, evacuation scenarios, data networks, andload balancing on servers. Because of their rich structure and variedapplications, routing games are among the earliest and most widelystudied problems in algorithmic game theory.

The central solution concept for analysing routing games is theNash equilibrium, and the fundamental questions in routing gamesrelate to properties of equilibria: does an equilibrium exist? Do mul-tiple equilibria exist? How much worse is system performance –e.g., the total delay of the agents – at equilibrium as compared toan optimal, centrally controlled outcome? The last question asks forupper bounds on the Price of Anarchy, a central theme of research inalgorithmic game theory.

The classical Braess’ paradox: theaddition of a low-cost edge in a

network (shown dashed) can result inan increase in cost to all of the players.

Typically, routing games model traffic by static flows, assumingthat each agent uses all the edges in the route she chooses instantlyand simultaneously. Such models ignore a basic and often crucialcharacteristic of traffic: its transient nature. In my work, I studyrouting games with the more realistic assumption of transient, time-varying traffic [4, 3]. The equilibrium in this model is significantlymore complex than in a model with static flows. Despite this, I givea simple strategy for the owner of a network to ensure that the Priceof Anarchy, is bounded from above by a small constant.

An important question in routing games is to design networks so

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research highlights: umang bhaskar

that the total delay of the players at equilibrium is maximised. Thisis a central question in transportation research, introduced in 1979

as the Continuous Network Design Problem (CNDP). Despite its im-portance and significant research efforts, previous algorithms for thisproblem either did not give any approximation guarantees, or fo-cused on exact algorithms that require exponential time. I give thefirst approximation guarantees for polynomial-time algorithms forthis problem [9]. I give both approximation algorithms and hardnessguarantees that are tight for a number of different network topolo-gies, including general networks and series-parallel networks.

The results mentioned above assume that each agent controls in-finitesimal traffic in the network, and hence the impact of the routechosen by a single agent is marginal. This assumption makes rout-ing games simpler to analyse, and is a good assumption in, e.g., roadtraffic. In particular, the equilibrium is known to be unique with thisassumption. Without this assumption, when players control signif-icant traffic, analysing the game becomes much harder, and manyfundamental questions remain open in this setting. A fundamen-tal problem in algorithmic game theory is constructing algorithmsto compute equilibria. However there is little work on computingequilibria in games without the assumption of infinitesimal traffic,called atomic splittable routing games. My work gives the first al-gorithms for computing equilibria in these games in simple parallel-edge graphs [11]. These are the first algorithms for these games that

e1 e2

e3

e4 e5

e6

t

s

An Atomically Splittable RoutingGame with multiple equilibria

allow general convex delay functions on the edges. We also give evi-dence that in general graphs, equilibrium computation may be hard.In another work, I have shown that in these games, in contrast torouting games with infinitesimal players, the uniqueness of equilib-ria may no longer hold, and have given the first examples of suchgames with multiple equilibria [5, 6]. In particular, I show that mul-tiple equilibria can exist even in simple series-parallel graphs. Thisallows us to give a complete characterisation of graph topologies inwhich the equilibrium is unique.

A natural way for atomic splittable routing games to arise is ifinfinitesimal players form coalitions, and within a coalition, eachplayer routes its flow to minimise the total delay of the coalition.Each coalition then behaves as a single atomic splittable player. Whateffect does this have on the total cost? I show that in series-parallelnetworks where all players have access to the same routes, the totalcost in the presence of coalitions is always at most that without coali-tions [7]; thus forming coalitions in this setting can only improve thesystem performance at equilibrium.

Revealed Preference Approach for Analysing Games. Most work in al-gorithmic game theory assumes that the costs (or utilities) of agentsare known to the system designer. It then proceeds with the questionat hand, such as computing equilibria in the game, or determiningincentives so that players attain a better outcome. This is clearly astrong assumption. Players often have incentives to misreport their

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costs. Even when proxies for the costs are known, such as travel timein traffic networks or bids in auctions, these cannot be equated to thecosts of players.

If we assume that costs are unknown, how can we proceed withanalysing a game? One solution is to focus on player behaviour, anduse that to reveal player costs and preferences. This is formalised ineconomics by the classical revealed preference approach. Rather thanfix utilities for the players, this approach takes as input the behaviourof the agents, and determines if there exist utilities that explain thisbehaviour as an equilibrium. Such utilities are said to rationalise theobserved player behaviour.

A simple and fundamental question is to use player behaviour tovalidate common assumptions about player costs and utilities. Forexample, submodularity or concavity, corresponding to diminishingmarginal returns, is a common assumption. Having observed playerbehaviour, can we validate this assumption? Concretely, given playervalues at certain points, does there exist a corresponding functionthat satisfies the assumption about utilities? For this basic question,we show that the problem is NP-hard for many common assump-tions on player utilities [8]. Thus, validating these assumptions maybe computationally difficult.

A common technique in routing games to improve traffic flowis to use tolls to influence player behaviour. However, all previousalgorithms to compute tolls required knowledge of the player costs.We show that appropriate tolls can be obtained without learning theplayer costs [10]. We only need to be able to observe the traffic flowin response to costs.

As stated earlier, a central question in algorithmic game theoryis the computational complexity of equilibrium. In many games, ifwe are given the costs of the players as input, it is known that theequilibrium is hard to compute – it is unlikely that an algorithmthat runs in polynomial time will be able to compute an equilibrium.This is considered a significant criticism of equilibrium as a solutionconcept, since computing an equilibrium is a prerequisite for reach-ing such an outcome. However, such results consider the costs ofagents in the game to be literal, and given as inputs to the problemof computing equilibrium. Economists on the other hand, insteadof considering the costs to be literal, ask if there exist costs that ex-plain observed user behaviour. Thus, instead of costs, the observedvariables in a game are the behaviour of the players. The problem isthen to determine if there exist utilities that explain this behaviouras equilibrium, and for which there exist polynomial-time algorithmsto compute an equilibrium. This problem puts the issue of compu-tational complexity within the framework of the revealed preferenceapproach.

I have addressed this problem in two-player games called bima-trix games. It is known that in this case, computing an equilibriumis PPAD-hard. In spite of this, considering the revealed preferenceapproach, I have shown that large classes of observed agent be-

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haviour can be explained by games where the equilibrium can becomputed by polynomial-time algorithms [1]. Our work also char-acterises player behaviour that can be rationalised, and thus con-tributes to pure economic theory as well. I also consider the casewhere players are restricted to pure strategies [2]. While comput-ing a Nash equilibrium can be done in polynomial-time in this case,I have shown results about the structural complexity of the playerutilities that rationalise the observed behaviour.

References[1] Siddharth Barman, Umang Bhaskar, Federico Echenique, and Adam Wierman.

The empirical implications of rank in bimatrix games. In ACM Conference onElectronic Commerce, EC ’13, Philadelphia, PA, USA, June 16-20, 2013, pages 55–72,2013.

[2] Siddharth Barman, Umang Bhaskar, Federico Echenique, and Adam Wierman.On the existence of low-rank explanations for mixed strategy behavior. In Weband Internet Economics - 10th International Conference, WINE 2014, Beijing, China,December 14-17, 2014. Proceedings, pages 447–452, 2014.

[3] Umang Bhaskar, Lisa Fleischer, and Elliot Anshelevich. A stackelberg strategyfor routing flow over time. In Proceedings of the Twenty-Second Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2011, San Francisco, California,USA, January 23-25, 2011, pages 192–201, 2011.

[4] Umang Bhaskar, Lisa Fleischer, and Elliot Anshelevich. A stackelberg strategyfor routing flow over time. Games and Economic Behavior, 92:232–247, 2015.

[5] Umang Bhaskar, Lisa Fleischer, Darrell Hoy, and Chien-Chung Huang. Equilibriaof atomic flow games are not unique. In Proceedings of the Twentieth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2009, New York, NY, USA, January4-6, 2009, pages 748–757, 2009.

[6] Umang Bhaskar, Lisa Fleischer, Darrell Hoy, and Chien-Chung Huang. On theuniqueness of equilibrium in atomic splittable routing games. Math. Oper. Res.,40(3):634–654, 2015.

[7] Umang Bhaskar, Lisa Fleischer, and Chien-Chung Huang. The price of collusionin series-parallel networks. In Integer Programming and Combinatorial Optimization,14th International Conference, IPCO 2010, Lausanne, Switzerland, June 9-11, 2010.Proceedings, pages 313–326, 2010.

[8] Umang Bhaskar and Gunjan Kumar. On the complexity of extending partialfunctions. Technical report, 2017.

[9] Umang Bhaskar, Katrina Ligett, and Leonard J. Schulman. Network improve-ment for equilibrium routing. In Integer Programming and Combinatorial Optimiza-tion - 17th International Conference, IPCO 2014, Bonn, Germany, June 23-25, 2014.Proceedings, pages 138–149, 2014.

[10] Umang Bhaskar, Katrina Ligett, Leonard J. Schulman, and Chaitanya Swamy.Achieving target equilibria in network routing games without knowing the la-tency functions. In 55th IEEE Annual Symposium on Foundations of Computer Sci-ence, FOCS 2014, Philadelphia, PA, USA, October 18-21, 2014, pages 31–40, 2014.

[11] Umang Bhaskar and Phani Raj Lolakapuri. Equilibrium computation in atomicsplittable routing games with convex cost functions. Technical report, 2017.

Research Highlights: Kavitha Telikepalli

My research work has primarily focused on efficient graph algo-rithms. My recent focus has mainly been on a sub-area of graphproblems, which is the domain of matching problems. In several ofthese problems, the input is a bipartite graph G = (A ∪ B, E) whereeach vertex ranks edges incident upon itself in some order of prefer-ence and the general problem is to match vertices in an optimal man-ner. Stability is the usual notion of optimality in such an instance;

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here we consider a more relaxed notion called popularity, which wasintroduced by Gärdenfors in 1975.

Popular matchings Popularity is based on voting by vertices on theset of feasible matchings. The preferences of a vertex over its neigh-bours are extended to preferences over matchings by postulating thatevery vertex orders matchings in the order of its partners in thesematchings. A matching is popular if it never loses a head-to-headelection against any matching where each vertex casts a vote. Thuspopular matchings are (weak) Condorcet winners in the correspondingvoting instance.

A

B

C

X

Y

Z

X > Y > Z

X > Y > Z

X > Y > Z

M1 M2 M3 M1

An instance with no left-popularmatching

Gärdenfors showed that when there are no ties in preference lists,a stable matching is popular. However it is easy to show that astable matching is a min-size popular matching. We showed thata max-size popular matching can be computed in linear time. Thisalgorithm can be considered as a 2-level generalisation of the Gale-Shapley algorithm. A maximum size popular matching need notbe a max-size matching in G. If a max-size matching is one of therequirements of a problem, then the desired matching is a max-sizematching M∗ that is popular within the set of max-size matchingsin G. We show that such a matching M∗ always exists and can becomputed in G = (A ∪ B, E) in O(mn0) time, where m = |E| andn0 = min(|A|, |B|).

Though the above matching M∗ is popular in the set of max-sizematchings, in the entire set of matchings in G, its unpopularity factorcould be as high as n0 − 1. On the other hand, a maximum sizepopular matching could be of size only 2

3 |M∗|. In between these twoextremes, we showed that there is an entire spectrum of matchings:for any integer 2 ≤ k ≤ n0, there is a matching Mk in G of sizeat least k

k+1 |M∗| whose unpopularity factor is at most k − 1; alsosuch a matching Mk can be computed in O(km) time by a k-levelgeneralisation of our max-size popular matching algorithm.

Popular mixed matchings In collaboration with Chien-Chung Huang,we recently considered a more general problem: suppose there is areal-valued edge weight function in G = (A ∪ B, E) with strict pref-erence lists. The goal is to compute a max-weight popular matching,however the complexity of this problem is currently unknown. Butwe know how to compute a max-weight popular mixed matching inpolynomial time. A mixed matching is a “lottery” over matchingsand thus it is more complicated to implement than a (pure) match-ing.

We studied the polytope PG of fractional popular matchings andshowed that this polytope is half-integral. This implies that thereis always a max-weight popular mixed matching Π such that Π =

(M0, 12 ), (M1, 1

2 ) where M0 and M1 are matchings in G. As Π canbe computed in polynomial time, our result implies that in order toimplement a max-weight popular mixed matching in G, we need justa single random bit.

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research highlights: kavitha telikepalli

We also showed that our result carries over to the roommates prob-lem, where the graph G need not be bipartite. The polytope ofpopular fractional matchings is still half-integral here and so we cancompute a max-weight popular half-integral matching in G in poly-nomial time. To complement this result, we also showed that theproblem of computing a max-weight popular (integral) matching ina roommates instance is NP-hard.

Maximum weight matchings We (in collaboration with Chien-ChungHuang) also considered the problem of efficiently computing maxi-mum weight matchings in general graphs. The input here is a gen-eral graph G = (V, E) and integral edge weights given by w : E →1, 2, · · ·W. We showed that the max-weight matching problem canbe solved by reducing it to W different maximum cardinality match-ing problems. Thus we solved the problem in time proportional tothe product of W and the time needed for finding a maximum car-dinality matching. Hence any future improvement in the runningtime of the maximum cardinality matching algorithm implies a fasterrunning time for our algorithm. One of the advantages of our algo-rithm is that it computes an integral optimal dual solution. This inturn gives a new proof to the fact that Edmonds’ matching polytopeis totally integral. Although the total dual integrality of Edmonds’matching polytope is well-known, ours is a constructive proof of thisfact.

Approximate Distances Another topic I worked on was on faster al-gorithms for constructing “approximate distance oracles”. These arecompact data structures using which an approximate distance esti-mate for any pair of vertices can be retrieved efficiently. Thorup andZwick in 2001 were the first to show such space-efficient data struc-tures for any undirected graph G = (V, E) with non-negative edgeweights, with |V| = n and |E| = m.

We improved the construction time of the Thorup-Zwick algo-rithm to show that for any integer k > 2, an approximate distance or-acle of size O(kn1+1/k) can be constructed in time O(min(n2, kmn1/k))

and for k = 2, an O(n3/2) sized data structure can be built in O(n2)

time that answers 3-stretch queries in O(log n) time. We then ex-tended this technique to design faster algorithms to build n× n ta-bles that store all-pairs stretch t distances for t = 2, 7/3 and also gavean algorithm with expected running time O(n2) to compute all pairsalmost stretch 2 distances. This is joint work with Surender Baswana.

A graph and a spanner of stretch 2

Spanners A related question here is to find efficient algorithms forconstructing sparse subgraphs, or spanners, of a given graph withthe property that for any pair of vertices s and t, the distance in thespanner between s and t is at most a constant factor away from the s-t distance in the given graph. We worked on the problem of showingsparse spanners in unweighted graphs where the distortion in dis-

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tance is given by a pair (α, β). An (α, β) spanner of an unweightedgraph G is a subgraph H that distorts distances in G up to a multi-plicative factor of α and an additive factor of β. We showed that forany integer k, a (k, k− 1) spanner of size O(kn1+1/k) exists and it canbe constructed in linear time. We also showed a (1, 6) spanner of sizeO(n4/3). This is joint work with Surender Baswana, Kurt Mehlhorn,and Seth Pettie.

We also worked on the case where we seek an even sparser sub-graph H under the condition that distances only between a givensubset P of pairs have to be well-approximated. Distances in H be-tween pairs outside P need not be well-approximated. We (some ofthis work is in collaboration with Nithin Varma) showed construc-tions of such sparse subgraphs for the case where distances in H areapproximated within additive stretches of 2, 4, and 6 when givenany subset P ⊆ V × V. We obtained even sparser constructions forthe case when we are given a subset S ⊂ V and our set P = S× V.We also derived better results when the set P = S× T (for additivestretch 4) and also when the set P is implicitly described as the setof pairs whose distance in G is at least D, for a given parameter D.

,A graph and a cycle basis over F2(which is not a cycle basis over Q).

Minimum Cycle Basis The minimum cycle basis problem is an alge-braic problem on graphs: this problem involves computing a mini-mum weight basis for the cycle space of the graph. When the graphis undirected, the cycle vectors are 0-1 incidence vectors in 0, 1|E|and when the graph is directed, the cycle vectors belong to Q|E|,where E is the edge set of the graph. The vector space spanned bythe cycle vectors is the cycle space of the graph. The problem ofcomputing a linearly independent set of cycles that span this spaceand whose sum of weights is the least is the minimum cycle basisproblem. This is an important problem theoretically and it also hasseveral applications. We worked on faster deterministic, randomised,and approximation algorithms for this problem.

We designed a simple algebraic algorithm for the minimum cyclebasis problem, where along with the cycles that constitute the min-imum cycle basis we also compute witness vectors which are certifi-cates of membership for each cycle in this basis. We used a fast ma-trix multiplication subroutine to obtain these witnesses efficiently. Inundirected graphs, the witnesses are in 0, 1|E|, however in directedgraphs, these vectors are in Q|E| and thus their coordinates couldbecome too large. We control this in the randomised algorithm byworking in Fp for a random prime p and bound the error probabilityby 1/4. In the deterministic algorithm (in joint work with RameshHariharan and Kurt Mehlhorn), we work in a ring ZN where wecompute an integer N that is not too large.

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Applied Probability

Effective decision making under uncertainty underpins the explod- I believe stochastic methods will transformpure and applied mathematics in the begin-ning of the third millennium. Probabilityand statistics will come to be viewed as thenatural tools to use in mathematical as wellas scientific modelling.

– David Mumford, The Dawning of theAge of Stochasticity, 2000.

ing technological revolution over the last few decades. Probabilistictechniques have played a key role in taming uncertainty and proba-bilistic thinking has come to pervade various disciplines of the com-puting and systems sciences. The underlying principle behind thissurge has been that while individual future outcomes may not bepredictable, it is nevertheless true in many physical and social set-tings that the interaction of these random outcomes have a high de-gree of regularity and structure. Such regularity, which often needsto be framed in probabilistic terms, facilitates analysis and control oflarge and complex systems. As a result, probability has become thefoundation of research in such diverse endeavours as machine learn-ing and artificial intelligence, design and control of communicationsand social networks including the Internet and the world wide web,and the management of global financial and insurance risk markets.

Probabilistic ideas have also served to establish and elucidate con-nections between different theoretical areas of research, and increas-ingly, many important research questions appear at the interface be-tween probability and areas such as optimisation, control theory,statistics, and machine learning. Probability has also acted as abridge between computer science and the natural sciences, and un- Know me a master of the dice, and an expert

in computation.– R. tuparn. a in the Mahabharata, Chap-

ter 70 of the Vana Parva (“The Book ofthe Forest”).

derlies the exciting progress in population genetics, the modellingof evolution, and in understanding the connections between phasetransitions and computation.

Reflecting the ubiquity of probability theory across computingand systems science as a tool for modelling and analysis, STCS has agrowing group in applied probability. Research in this area in STCSspans across learning and sampling problems, connections with al-gorithms and computational complexity, rare event analysis, queuingtheory and financial mathematics. Research interests of the membersof the group are described next.

Hariharan Narayan’s recent work has focused on sampling prob-lems that arise in several learning paradigms, including in Bayesianlearning, and on learning from structured data. The first line of workhas shown how interior point methods developed in convex optimi-sation can be married with Markov chain Monte Carlo techniques toprovide fast sampling algorithms for continuous distributions. Thesecond line of work has given algorithms for learning from data that

applied probability

is, modulo noise, known to be located on a manifold. Such manifoldconstraints arise naturally in scientific applications due to the rele-vant data generating processes often having only a small number oftrue degrees of freedom, but taking advantage of them for learningtasks is often a challenging task.

Piyush Srivatava’s research is on the applications of probability incomputation. A major focus of his work has been on computationalproblems related to probabilistic graphical models, and especially onexploiting tools from complex analysis and statistical mechanics forattacking such problems. Recent work along these lines by Piyushand his co-authors has resulted in new algorithms for counting andsampling problems in graphical models as well as for the structurerecovery problem in models of social networks, and has also con-tributed to strengthening the connections between these algorithmicproblems and phase transitions. In addition to his work on graphicalmodels, Piyush has also worked on dynamical systems arising in themodels of evolution, and is generally interested in the computationalunderpinnings of mathematical models in the sciences.

Sandeep Juneja’s research in the last few years involves designand analysis of Monte Carlo algorithms in a variety of frameworksincluding in nested simulation, perfect sampling of spatial processes,efficient rare event simulation, and multi-armed bandit problems.The motivation for some of these problems comes from portfolio riskmeasurement and options pricing in financial mathematics, wherehe and co-authors develop, analyse and optimise some popular ma-chine learning methods applied to high dimensional finance prob-lems. Sandeep and co-authors have also conducted game-theoreticequilibrium analysis of arrival times of strategic customers to a va-riety of queues and develop insights on ways to control the price ofanarchy of such systems.

Future plans

In the coming years, we plan to continue our research on foun-dational problems in applied probability germane to the changingworld. We hope to grow through hiring faculty whose work, whileretaining the school’s conceptual focus, can further diversify appliedprobability research in the school. In addition to more classical areassuch as stochastic control and optimisation, we will especially lookfor researchers working on emerging areas such as machine learningand data science.

We also plan to deepen engagement with the industry and the in-dustrial research and development labs in the country. We plan toset up a virtual centre specialising in learning theory and data sci-ence at STCS, for which we plan to reach out to the external agenciesincluding the industry for support.

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research highlights: sandeep juneja

Research Highlights: Sandeep Juneja

Sandeep’s research in the last ten years has focused on the followingbroad heads:

• Monte Carlo methods including in rare event simulation, select-ing the best systems or pure exploration in multi-armed banditframework, perfect sampling for spatial processes and nested sim-ulation.

• Financial mathematics including computational algorithms androbust estimation methods.

• Game theoretic strategic equilibrium arrivals to queues.

Monte Carlo Methods

(Some of related work is covered under Financial Mathematics)

Rare event simulation: First provably efficient rare event simulationalgorithm was developed by [33]. Since then the field has seen anexplosive growth (see [3], [8] for an overview). Over the last fiveyears Sandeep’s work has focused on efficient importance samplingbased simulation of stochastic processes involving fat-tailed randomvariables. In an earlier work, [4] had proved impossibility of arriv-ing at an asymptotically optimal state-independent importance sam-pling scheme for first passage probabilities involving fat-tailed ran-dom increments. Murthy, Juneja and Blanchet (2014) [31] revisit thisproblem and observe that if one is allowed to partition the first pas-sage probability and sample the partitions through clever randomi-sations, asymptotically efficient state-independent importance sam-pling schemes are indeed feasible. In fact, they design schemes withasymptotically vanishing relative errors. Agarwal, Dey and Juneja(2013) [1] develop a new approach to rare event simulation wherethey exploit the saddle-point-based representations that exist for rareprobabilities, which rely on inverting the characteristic functions ofthe underlying random vectors. These representations reduce therare event estimation problem to evaluating certain integrals, whichmay via importance sampling be represented as expectation.

Selecting the best system: The problem of finding the best systemamongst many has been extensively studied in statistics, simulation,and more recently, computer science. It is also referred to as theproblem of finding the best arm in the multi-armed bandit literature.Glynn and Juneja (2004) [15] used large deviations theory to opti-mally allocate simulation budget amongst different designs to max-imise the exponential decay rate of probability of incorrect selection.However, this methodology required estimating large deviations ratefunction. Significant literature thereafter used similar ideas for sim-ulation budget allocation. In a recent work [16], Glynn and Juneja(2018) show the impossibility of devising algorithms that exploit the

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theoretically guaranteed exponential convergence of incorrect selec-tion probability without additional restrictions on underlying distri-butions. Further they show that when upper bounds on moments ofunderlying random variables are known, algorithms providing suchguarantees can be devised.

Perfect sampling of spatial Markov processes: Generating perfect sam-ples distributed as Gibbs spatial processes is a classical problem instatistical physics and in applied probability. Post [32] many cou-pling from the past based algorithms have been devised to generatesuch samples (see [28], [13]). These methods are much faster thannaive rejection sampling. Moka, Juneja and Mandjes 2017 [30] revisitthis problem and combine importance sampling, careful partitioningwith rejection methods that are provably shown to improve uponcoupling from the past methods in certain asymptotic regimes. En-route, they conduct large deviations analysis of hard sphere modelsin regimes where the number of spheres increases to infinity whiletheir radii shrinks to zero at varying rates. The theoretical techniquesfor developing some of the asymptotic results were developed earlierin Juneja and Mandjes (2013) [22].

Financial mathematics

Portfolio risk measurement is of obvious importance to large banksand other financial institutions. A portfolio may contain many thou-sands of financial instruments including options and other financialderivatives whose prices may be expressed as suitable conditionalexpectations. Measuring tail risk of such portfolios involves nestedMonte Carlo. In the outer loop, economic scenarios are created; inthe inner loop, options, derivatives, etc. are evaluated conditionedon the generated scenarios. Practitioners view this nested proce-dure as computationally prohibitive and typically use ad-hoc pric-ing adjustments, leading to inaccuracies. Gordy and Juneja (2010)[17] show how to optimally structure the nested simulation betweenthe two loops and observe that the apprehensions of large computa-tional burden were misplaced. Hong and Juneja (2009) [19], Hong,Juneja and Liu (2017) [20], Agarwal and Juneja (2015) [2] use kernelbased machine learning methods for nested estimation in portfoliorisk measurement as well as in American options pricing, and outlineoptimal implementations with a precise delineation of performancedegradation as a function of the underlying problem dimension.

Pricing high dimensional American and Bermudan options is an impor-tant and technically challenging optimal stopping problem in mathfinance. Function approximation methods combined with approxi-mate dynamic programming have become popular in practice, how-ever, speed and accuracy of price estimation remains a concern, see,e.g., [29], [34] for seminal works. Further, there exists elegant addi-tive and multiplicative duality associated with the optimal stopping

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problem, see [18]. Juneja and Kalra (2009) [25] (also [7], [6]) introduceperfect control variates and perfect importance sampling schemesthat tie the two dualities to simulation methods and results in sub-stantially faster algorithms to compute provably close and accuratelower as well as upper biased price estimators.

Credit risk, incorporating user views in an entropic framework: Under-pricing of exotic instruments such as CDO’s (collateral default obli-gation) and CDS’s (credit default swap) played an important role inthe financial crisis in 2008-09. Use of Gaussian Copulas to model de-pendence was a key reason for this underpricing, see [14]. Bassam-boo, Juneja and Zeevi (2008) [5] analyse portfolio risk models wherethe underlying dependence is modelled using more general copu-las including t-copulas that are known to better capture extremaldependence. They developed both large deviation asymptotics fortail risk measures as well as provably efficient simulation estimationtechniques. Using these better dependence measures, Sandeep andco-authors reached the broad conclusion that the risk of these exoticinstruments is far larger than suggested by prevalent models.

In a related problem, Risk Management Institute at NUS reportsonline default probabilities of sixty thousand firms worldwide. Theyuse complex sequential Monte Carlo to approximately determinemaximum likelihood parameters (MLE’s) of underlying parameters[12]. Deo and Juneja (2017) [9] observe that since such probabili-ties are small, straightforward closed form approximations to theseparameters exist, that given the inherent model errors, are as goodor bad as the MLE. All this is rigorously shown in an asymptoticframework where the default probabilities decrease to zero whilethe number of firms and time periods increases to infinity. Dey andJuneja (2012) [10], [11] develop KL divergence and Renyi entropybased techniques for arriving at a probability model for a financialsystem that is closed to a reference model and accurately incorpo-rates expert views as well as market prices.

Strategic arrivals to queues

Governed by the very urgent need to decide when to go down forlunch at TIFR west canteen, this line of work was initiated in thecanteen queue when Rahul Jain (USC) was visiting TIFR. Jain, Junejaand Shimkin (2010) [21] consider the problem of strategic arrivalsto queues when the customers are modelled as fluid particles. Thecosts are linearly associated with waiting times as well as time ofservice completion. This framework has a rich history in the trans-portation framework where seminal work was conducted by Vickrey(1969) [35]. Sandeep and co-authors observe that the fluid frame-work provides a great deal of tractability in analysing in closed formthe resulting unique equilibrium customer profile, as well as in de-termining the price of anarchy. The framework is also amenable toanalysing many reasonable what-if queries. Juneja and Jain (2010)

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[24] received the best paper award for this work at ICST Fourth Inter-national Conference on Performance Evaluation Methodologies andTools. Later, Juneja, Raheja and Shimkin (2012) [26] received the bestpaper award Sixth International ICST Conference on PerformanceEvaluation Methodologies and Tools, for extensions involving ran-dom fluid arrivals and other enhancements. A key issue in the fluidanalysis is whether its equilibrium approximates the finite but largecustomer regimes. Juneja and Shimkin (2013) [27] show this to be in-deed the case. They also derive the rate of convergence of finite sys-tem profiles to the fluid system. Interestingly, they show that finitesystem has a unique equilibrium profile that is symmetric. Typically,symmetry is assumed for tractability; the fact that this system doesnot allow asymmetric solutions was intriguing.

Juneja and Manjunath (2016) [23], analyse the problem of howlong to stay in a comfortable lounge before becoming miserable wait-ing in a queue for service. They identify the equilibrium queue andlounge profile and characterise the system price of anarchy.

References

[1] Ankush Agarwal, Santanu Dey, and Sandeep Juneja. Efficient simulation of largedeviation events for sums of random vectors using saddle-point representations.Journal of Applied Probability, 50(3):703–720, 2013.

[2] Ankush Agarwal and Sandeep Juneja. Nearest neighbor based estimationtechnique for pricing bermudan options. International Game Theory Review,17(01):1540002, 2015.

[3] Søren Asmussen and Peter W Glynn. Stochastic simulation: algorithms and analysis,volume 57. Springer Science & Business Media, 2007.

[4] Achal Bassamboo, Sandeep Juneja, and Assaf Zeevi. On the inefficiency of state-independent importance sampling in the presence of heavy tails. OperationsResearch Letters, 35(2):251–260, 2007.

[5] Achal Bassamboo, Sandeep Juneja, and Assaf Zeevi. Portfolio credit risk withextremal dependence: Asymptotic analysis and efficient simulation. OperationsResearch, 56(3):593–606, 2008.

[6] Nomesh Bolia and Sandeep Juneja. Function-approximation-based perfect con-trol variates for pricing american options. In Simulation Conference, 2005 Proceed-ings of the Winter, pages 1876–1883. IEEE, 2005.

[7] Nomesh Bolia, Sandeep Juneja, and Paul Glasserman. Function-approximation-based importance sampling for pricing american options. In Proceedings of the36th conference on Winter simulation, pages 604–611. Winter Simulation Confer-ence, 2004.

[8] James Bucklew. Introduction to rare event simulation. Springer Science & BusinessMedia, 2013.

[9] Anand Deo and Sandeep Juneja. Credit risk: Simple closed form approximatemaximum likelihood estimator. 2017.

[10] Santanu Dey and Sandeep Juneja. Incorporating fat tails in financial modelsusing entropic divergence measures. arXiv preprint arXiv:1203.0643, 2012.

[11] Santanu Dey, Sandeep Juneja, and Karthyek RA Murthy. Incorporating viewson marginal distributions in the calibration of risk models. Operations ResearchLetters, 43(1):46–51, 2015.

[12] Darrell Duffie, Leandro Saita, and Ke Wang. Multi-period corporate defaultprediction with stochastic covariates. Journal of Financial Economics, 83(3):635–665, 2007.

[13] Pablo A Ferrari, Roberto Fernández, and Nancy L Garcia. Perfect simulation forinteracting point processes, loss networks and ising models. Stochastic Processesand their Applications, 102(1):63–88, 2002.

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[14] Paul Glasserman and Jingyi Li. Importance sampling for portfolio credit risk.Management science, 51(11):1643–1656, 2005.

[15] Peter Glynn and Sandeep Juneja. A large deviations perspective on ordinal opti-mization. In Proceedings of the 36th conference on Winter simulation, pages 577–585.Winter Simulation Conference, 2004.

[16] Peter Glynn and Sandeep Juneja. Selecting the best system, large deviations, andmulti-armed bandits. arXiv preprint arXiv: 1507.04564, 2015.

[17] Michael B Gordy and Sandeep Juneja. Nested simulation in portfolio risk mea-surement. Management Science, 56(10):1833–1848, 2010.

[18] Martin B Haugh and Leonid Kogan. Pricing american options: a duality ap-proach. Operations Research, 52(2):258–270, 2004.

[19] L Jeff Hong and Sandeep Juneja. Estimating the mean of a non-linear function ofconditional expectation. In Winter Simulation Conference, pages 1223–1236. WinterSimulation Conference, 2009.

[20] L Jeff Hong, Sandeep Juneja, and Guangwu Liu. Kernel smoothing for nestedestimation with application to portfolio risk measurement. Operations Research,65(3):657–673, 2017.

[21] Rahul Jain, Sandeep Juneja, and Nahum Shimkin. The concert queueing game:to wait or to be late. Discrete Event Dynamic Systems, 21(1):103–138, 2011.

[22] S Juneja and M Mandjes. Overlap problems on the circle. Advances in AppliedProbability, 45(3):773–790, 2013.

[23] S Juneja and D Manjunath. To lounge or to queue up. ACM SIGMETRICSPerformance Evaluation Review, 44(2):39–41, 2016.

[24] Sandeep Juneja and Rahul Jain. The concert/cafeteria queueing problem: a gameof arrivals. In Proceedings of the Fourth International ICST Conference on PerformanceEvaluation Methodologies and Tools, page 59. ICST (Institute for Computer Sciences,Social-Informatics and Telecommunications Engineering), 2009.

[25] Sandeep Juneja and Himanshu Kalra. Variance reduction techniques for pricingamerican options using function approximations. The Journal of ComputationalFinance, 12(3):79, 2009.

[26] Sandeep Juneja, Tushar Raheja, and Nahum Shimkin. The concert queueinggame with a random volume of arrivals. In Performance Evaluation Methodologiesand Tools (VALUETOOLS), 2012 6th International Conference on, pages 317–325.IEEE, 2012.

[27] Sandeep Juneja and Nahum Shimkin. The concert queueing game: strategicarrivals with waiting and tardiness costs. Queueing Systems, 74(4):369–402, 2013.

[28] Wilfrid S Kendall. Perfect simulation for the area-interaction point process. InProbability towards 2000, pages 218–234. Springer, 1998.

[29] Francis A Longstaff and Eduardo S Schwartz. Valuing american options by sim-ulation: a simple least-squares approach. The review of financial studies, 14(1):113–147, 2001.

[30] SB Moka, S Juneja, and MRH Mandjes. Perfect sampling for gibbs processeswith a focus on hardsphere models. arXiv preprint arXiv:1705.00142, 2017.

[31] Karthyek RA Murthy, Sandeep Juneja, and Jose Blanchet. State-independent im-portance sampling for random walks with regularly varying increments. Stochas-tic Systems, 4(2):321–374, 2014.

[32] James Gary Propp and David Bruce Wilson. Exact sampling with coupledmarkov chains and applications to statistical mechanics. Random structures andAlgorithms, 9(1-2):223–252, 1996.

[33] David Siegmund. Importance sampling in the monte carlo study of sequentialtests. The Annals of Statistics, pages 673–684, 1976.

[34] John N Tsitsiklis and Benjamin Van Roy. Regression methods for pricing complexamerican-style options. IEEE Transactions on Neural Networks, 12(4):694–703, 2001.

[35] William S Vickrey. Congestion theory and transport investment. The AmericanEconomic Review, 59(2):251–260, 1969.

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Research Highlights: Hariharan Narayanan

Random Sampling and Optimisation

One area of research that Hariharan Narayanan has been involved infor the past decade is that of randomised interior point methods. Theidea here is to put together techniques from interior point methodsfor convex optimisation and methods of analysis of Markov chainsto develop new and faster algorithms for sampling convex sets.

In the papers “Random Walks on Polytopes and an Affine Inte-rior Point Method for Linear Programming” by Ravi Kannan andHariharan Narayanan, Mathematics of Operations Research, 2012,and “Randomised interior point methods for sampling and optimisa-tion”, by Hariharan Narayanan, Annals of Applied Probability, 2016,a new random walk known as Dikin walk was defined and analysedfor sampling from the uniform distribution in a convex set.

In a paper titled “Efficient sampling from time-varying log-concavedistributions”, joint with Alexander (Sasha) Rakhlin in the Journal ofMachine Learning Research (2017), Hariharan Narayanan extendedthe Dikin walk to situations where the distribution being sampledwas logconcave rather than uniform.

A paper titled “Escaping the Local Minima via Simulated Anneal-ing: Optimization of Approximately Convex Functions”, joint withAlexandre Belloni, Tengyuan Liang and Sasha Rakhlin, has appearedin the Conference on Learning Theory 2015. This paper did not useinterior point methods, but did use a random walk.

A recent joint work with Adam Gustafson titled “John’s Walk”,uploaded to the arxiv, uses John’s ellipsoids instead of Dikin ellip-soids for sampling a polytope from the uniform measure. As a result,the mixing time has no dependence on the number of constraints ofthe polytope.

Lastly, a preprint titled “On the distribution of random words ina compact Lie group”, was uploaded to the arxiv, wherein a randomMarkov chain on a compact Lie group is analysed, and its mixingbehaviour is characterised up to a 2−Wasserstein distance of ε touniformity. This provides a quantitative version of a weak variantof a question asked by Lubotzky, Phillips and Sarnak, which wasreiterated recently by Bourgain and Gamburd.

Manifold learning

A manifold embeddedin Euclidean space

In recent years, high dimensional statistics has focused on methodsof alleviating the curse of dimensionality. One assumption that facil-itates this is the Manifold hypothesis: that data lie in the vicinity of alow dimensional manifold. This could be due to the generating pro-cess possessing symmetries and/or few essential degrees of freedom.Manifold learning is a collection of methodologies for analysing highdimensional data based on the Manifold hypothesis. This has beenan area of intense activity over the past two decades.

A first paper in a series on manifold learning, Reconstruction

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research highlights: piyush srivastava

and interpolation of manifolds I: The geometric Whitney problem,Charles Fefferman, Sergei Ivanov, Yaroslav Kurylev, Matti Lassas,Hariharan Narayanan, has been put up on the arxiv and has beensubmitted to a journal.

A joint work of Hariharan Narayanan with Charles Fefferman andSanjoy Mitter titled “Testing the Manifold Hypothesis” completedand has been published in the Journal of the American MathematicalSociety. (February 2016)

An arxiv preprint titled “Manifold Learning Using Kernel DensityEstimation and Local Principal Components Analysis” of HariharanNarayanan with Kitty Mohammed on fitting a manifold to data usingdensity estimators has been submitted to a journal.

An arxiv preprint titled “Structural Risk Minimization for C1,1(Rd)

Regression”, that was joint work with Adam Gustafson, MatthewHirn, Kitty Mohammed, Hariharan Narayanan and Jason Xu thatconsiders a structured family of C1,1−regression problems was up-loaded to the arxiv recently.

Multi-agent systems

This field, deals with the processes by which interacting agents canconverge to a solution of an optimisation problem, through purelylocal interactions, in the absence of a central agency.

The paper “Language evolution and the Consensus Problem ina social network”, by Hariharan Narayanan and Partha Niyogi, ex-plores the following question in this area. Suppose we are givenagents on the vertices of a graph that communicate via edges. Sup-pose each agent holds a belief that is a distribution over all possiblewords for a certain concept. Then a natural update wherein eachagent produces a word based on its belief and updated its beliefbased on the words produced by its neighbours in a natural way.What is shown is that the beliefs of the agents converge with proba-bility one to a point mass, and all the words produced after a certaintime are the same. Further the time taken to converge, which is a ran-dom variable, has a mean governed by the mixing time of a randomwalk on the graph.

Research Highlights: Piyush Srivastava

Piyush Srivastava’s research is broadly on probabilistic structuresarising in computation, with a current focus on the relationship be-tween phase transitions in statistical physics and inference problemson graphical models, and the emerging connections of this work withthe stability theory of polynomials.

Graphical models give a formal way to succinctly describe theprobability distribution of random variables whose mutual correla-tions are structured. They have been extensively used in several areasof computer science as models of systems with large number of in-teracting components; examples include representations of pixels in

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an image in computer vision, and participants in a social network.In the “undirected” version of such a model, a graph represents thesystem, the vertices of the graph represent the components, and theedges model the possible interactions among the components. Eachvertex can be in one of several possible configurations (e.g., whenmodelling a social network, this configuration might represent thepolitical party preferred by the individual represented by the vertex);the configuration of the system is the collection of the configurationsof all the vertices. The model assigns a weight w(σ) to each con-figuration σ: the weights are such that they “factor” over the edgesand vertices of the graph, and hence are easily computable. Theseweights induce a probability distribution (known as the Gibbs distri-bution) on the configurations given by µ(σ) = 1

Z w(σ), Z = ∑σ w(σ)

is a normalisation factor known as the partition function. Such Gibbsdistributions are Markovian in the sense that the marginal distribu-tion of a subset S of vertices is conditionally independent of the restof the vertices conditioned on the configuration of the boundary ofthe set S. This conditional independence constraint is a natural fea-ture of many physical systems whose interactions are local in nature.It is not surprising therefore that an almost identical formalism hadbeen developed in statistical physics under the name of spin systems,first in the form of the Ising model used in the study of phase tran-sitions in magnets.

+ +

+

−

− −

−

w(C) = λ#(+)β|C|

Vertex activity

Edge activity

ZI(β, λ) ··= ∑cuts C

w(C)

The Ising model partition function

The Ising model and the hard core model (which assigns a weightλ|I| to each independent set I in terms of an activity parameter λ) aretwo of the simplest examples of graphical models. Both have beenwidely studied in both statistical physics and computer science: incomputer science, the Ising model has been used to model socialnetworks [10], while the hard core model, in addition to its rela-tion to the classical independent set problem, has also been used inthe modelling of communication networks [6]. In both models, onesees a so called “uniqueness” phase transition, where the parameterspace for the model partitions into two disjoint regions separated bya boundary, such that the qualitative features of the Gibbs distribu-tion are very different on the two sides of the boundary. This is bestexemplified in case of the d-regular tree: on one side, there are longrange correlations: fixing the state of the vertices at distance ` fromthe roots has a non-vanishing effect on the distribution of the rooteven as ` increases to infinity. On the other side there is “unique-ness” or decay of correlations: as ` increases to infinity, the effect ofthe leaves at distance ` on the root decays to 0. In the case of thehard core model, long range correlations occur on the d-regular treewhen λ > λc(d) = (d− 1)d−1/(d− 2)d, while uniqueness holds forλ < λc(d) [6]. The Ising model has more parameters, but admits asimilar description of the boundary.

A different notion of phase transitions considers the behaviourof “observables” of the model (such as the expected size of the in-dependent set in the hard core model) as a function of the modelparameters, and asks where the observables are not smooth func-

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tions of the parameters. Due to the form of the Gibbs distribution,natural observables correspond to derivatives of log Z (e.g., the ex-pected size of an independent set in the hard core model is exactlyλ

d log Z(λ)dλ ). Hence, they will be smooth in regions of the complex

plan where log Z is analytic: or equivalently, where the polynomialZ(λ) is zero-free or stable: this connection to the stability theory ofpolynomials dates back to the work of Yang and Lee [28].

Both these notions of phase transitions have important algorithmicimplications.

Uniqueness on the tree and approximate counting

Joint work of Piyush with Alistair Sinclair and Mark Thurley [21]shows that when the parameters of a general two-spin anti-ferro-magnetic spin system are in the uniqueness region of the d regulartree, there is a polynomial time algorithm for approximating the par-tition function on all regular graphs of degree d. Subsequent to theresults of this paper, Sly and Sun [24] have shown that an FPTASfor this problem when the parameters are outside the uniquenessregion of the infinite d-regular tree would imply that NP=RP. A sim-ilar correspondence between phase transitions and complexity hadbeen shown earlier for the hard core model in the seminal work ofWeitz [27] and Sly [23]. Taken together with these results, the resultsin [21] strengthen the tight correspondence between the uniquenessphase transition and computational complexity—shown earlier forthe hard core model—by extending it to the more general class ofanti-ferromagnetic spin systems.

Underlying both the correlation decay algorithms cited above [27,21] is a proof that correlation decay on the d-regular tree implies cor-relation decay on all graphs of maximum degree d. Joint work ofPiyush with Alistair Sinclair, Daniel Štefankovic and Yitong Yin [22,20] goes beyond this dependence on maximum degree by relatingthe correlation decay threshold for the hard core model on generalgraphs to their connective constant. The connective constant is a natu-ral measure of the “average” degree of a graph related to the growthin the number of self-avoiding walks in the graph as a function oftheir length: it has been especially well-studied in the case of integerlattices [9]. As corollaries, they also obtain correlation decay (andalgorithms) for the hard core model on the random graph modelG(n, d/n) under previously conjectured conditions. For various reg-ular lattices that had earlier been studied in statistical physics, theirresults also improve upon the known bounds on λ under which cor-relation decay in the hard core model provably holds. In particular,the best known bound for the 2D integer lattice Z2 before this workhad been obtained by Restrepo et al. [14] using much more numeri-cally intensive methods.

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Zeros of polynomials, computation of means, and the Lovász LocalLemma

The Lee-Yang view of phase transitions in terms of the analyticity oflog Z (or the stability of Z) has also turned out to be fruitful in com-plexity theoretic and algorithmic contexts. Joint work of Piyush withAlistair Sinclair [19] pointed out that the #P-hardness of computing

X

Y

O

|z| = 1

Lee-Yang theoremZeros of ZI lie on the unit circle

the magnetization of the ferromagnetic Ising model would follow ifthe celebrated Lee-Yang theorem on the zeros of the Ising partitionfunction [7] could be extended to guarantee that the zeros of the par-tition function on a connected graph are distinct, and obtained the#P hardness of the exact computation of average quantities such asthe magnetization and susceptibility at non-zero fields by proving thisextension (a joint paper of Piyush with Mario Szegedy [25] gives an-other proof of this extension). More recent joint work of Piyush withLeonard J. Schulman and Alistair Sinclair [15] proposed a differentapproach exploiting the interpretation of these averages as formalderivatives of the logarithm of the corresponding partition functionto prove the #P hardness of a wider class of problems, including thecomputation of the average size of independent sets and matchings.

More recently, the work of Barvinok [2] has led to a new class ofalgorithms for approximating the partition function that directly usethe analyticity of log Z, instead of using Markov chains or correla-tion decay, in order to approximate Z: the method has been appliedto several models in the last couple of years. Recent joint work ofPiyush with Jincheng Liu and Alistair Sinclair [8] uses the methodto provide the first deterministic algorithms for approximating thepartition function of the ferromagnetic Ising model (with a non-zerofield) on bounded degree graphs and hypergraphs that go beyondwhat is achievable using the correlation decay method. While a ran-domised Markov chain Monte Carlo algorithm for this problem re-stricted to the graph setting was devised by Jerrum and Sinclair inthe late 1980s, the new results for the hypergraph setting cannot yetbe achieved even using Markov chain methods. The main new in-gredient for the hypergraph result is a new stability result for thepartition function of the hypergraph Ising model which generalisesan old previous result of Suzuki and Fisher [26].

The partition function of the hard core model for negative andcomplex values of the parameter λ has also emerged as an importantsetting for the exploration of the connection between these variousideas. Note that there is no associated Gibbs distribution when λ isnot a positive real number. However, the work of Shearer, and Scottand Sokal [17, 18] has shown that the hard core partition function fornegative and complex λ has a deep connection with the Lovász locallemma. Informally, the Lovász local lemma provides the guaranteeof a positive probability of “success” against a large number of “badevents” which have a prescribed dependency structure given as agraph. The results of Shearer, and Scott and Sokal show that radiusof the largest possible disk in the complex plane, centred at 0, in the

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interior of which the hard core partition function of the dependencygraph does not vanish (known as the Shearer bound) determines the“best” possible guarantee in Lovász local lemma, and the value ofthe partition function at negative real λ in this disk correspond tothe best possible a priori probability of “success” in the set up of theLovász local lemma.

Joint work of Piyush with Nicholas J. A. Harvey and Jan Von-drák [5] shows that these connections can be exploited to providefast approximation algorithms for the hard core partition functionfor negative and complex λ satisfying the Shearer bound. This al-gorithm also generalises to the multivariate version of the Shearerbound, and also allows one to give new sub-exponential time con-structive versions of the local lemma that are qualitatively differentfrom the more traditional resampling approach.

Interestingly, even though there is no natural notion of “correla-tion” in the model when λ is not positive real, these results are basedon an analysis of Weitz’s correlation decay approach in this setting.In contrast, Patel and Regts [12] have also obtained algorithms forapproximating the hard core partition function at negative and com-plex using Barvinok’s approach of using the stability of Z directly,but the running time of the algorithms obtained using their methoddoes not seem to lead to any improvement over exponential timewhen applied to the question of obtaining an algorithmic Lovász lo-cal lemma.

Phase transitions in structure recovery

In the modelling of systems such as social networks with graphicalmodels, an important problem is to learn the parameters and thestructure of the model itself from the observed data. One such classof problems is community detection, where one assumes that the net-work being modeled has a community structure, and that the inter-actions between nodes in the graphical model being used to modelthe network differ depending upon whether the nodes lie in the samecommunity or not. The problem then is to recover the communitylabel of the nodes given empirical observations of the network. Thecommunity detection problem has been specially well studied in thecontext of the stochastic block model (see, e.g., the recent survey byAbbe [1]).

Joint work of Piyush with Quentin Berthet and Philippe Rigol-let [3], inspired by the use of the Ising model in the modelling ofopinions in social networks [10], considered the community detec-tion problem when the interaction is modeled as an Ising model,but with different inter-community and intra-community interactionstrengths. They show that the number of samples of this “Isingblockmodel” needed for a high probability estimation of the under-lying community structure is tightly coupled with a phase transitionin a variant of the mean-field Ising model. In particular, they showthat as a function of the number p of nodes in the model, the num-

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applied probability

ber of samples needed is Θ(log p) on one side of the phase transition,and increases sharply to Θ(p log p) on the other. They also show thaton both sides of the phase transition, a semi-definite programmingapproach, analogous to those used in the literature on the stochasticblock model, is able to perform the recovery with an optimal (up toconstant factors) number of samples.

Probability in computation

Along with the problems mentioned above, Piyush’s research focusesbroadly on problems concerning probability and computation, espe-cially those with connections to the sciences. Piyush’s research onthis broad theme includes work on models of evolution and on causalinference: his work in collaboration with Narendra M. Dixit, IoannisPanageas and Nisheeth Vishnoi [11, 4] gives rigorous convergencebounds for classical stochastic models of evolution with asexual re-production, while his joint work with Leonard J. Schulman [16] initi-ated the study of numerical properties of causal inference algorithmsin a well-studied Bayesian network based framework introduced byJudea Pearl [13].

Reference[1] Emmanuel Abbe. Community detection and stochastic block models: Recent

developments, March 2017. Available at arXiv:1703:10146.

[2] Alexander Barvinok. Computing the permanent of (some) complex matrices.Found. Comput. Math., 16(2):329–342, January 2015. arXiv:1405:1303.

[3] Quentin Berthet, Philippe Rigollet, and Piyush Srivastava. Exact recovery in theIsing blockmodel. Available at arXiv:1612.03880, 2017. To appear in the Annalsof Statistics.

[4] Narendra M. Dixit, Piyush Srivastava, and Nisheeth K. Vishnoi. A finite popu-lation model of molecular evolution: Theory and computation. J. Comput. Biol.,19(10):1176–1202, October 2012.

[5] Nicholas J. A. Harvey, Piyush Srivastava, and Jan Vondrák. Computing the in-dependence polynomial: from the tree threshold down to the roots. In Proc. 29thAnnual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 1557–1576,January 2018.

[6] F. P. Kelly. Stochastic models of computer communication systems. J. Royal Stat.Soc. Ser. B, 47(3):379–395, January 1985.

[7] T. D. Lee and C. N. Yang. Statistical theory of equations of state and phasetransitions. II. Lattice gas and Ising model. Phys. Rev., 87(3):410–419, 1952.

[8] Jingcheng Liu, Alistair Sinclair, and Piyush Srivastava. The ising partition func-tion: Zeros and deterministic approximation. In Proc. 58th IEEE Symposium onFoundations of Computer Science (FOCS), pages 986–997, October 2017. Availableat arXiv:1704.06493.

[9] Neal Madras and Gordon Slade. The Self-Avoiding Walk. Birkhaüser, Boston,1996.

[10] Andrea Montanari and Amin Saberi. The spread of innovations in socialnetworks. Proceedings of the National Academy of Sciences, 107(47):20196–20201,November 2010.

[11] Ioannis Panageas, Piyush Srivastava, and Nisheeth K. Vishnoi. Evolutionarydynamics in finite population mix rapidly, January 2016.

[12] Viresh Patel and Guus Regts. Deterministic polynomial-time approximation al-gorithms for partition functions and graph polynomials, July 2016. Avialblle atarXiv:1607.01167. To appear in SIAM J. Comput.

[13] J. Pearl. Causality. Cambridge University Press, 2000.

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https://arxiv.org/abs/1405:1303

https://arxiv.org/abs/1612.03880




[14] Ricardo Restrepo, Jinwoo Shin, Prasad Tetali, Eric Vigoda, and Linji Yang. Im-proved mixing condition on the grid for counting and sampling independentsets. Probab. Theory Related Fields, 156(1-2):75–99, June 2013.

[15] Leonard J. Schulman, Alistair Sinclair, and Piyush Srivastava. Symbolic integra-tion and the complexity of computing averages. In Proc. 56th Annual IEEE Sympo-sium on Foundations of Computer Science (FOCS), pages 1231–1245. IEEE ComputerSociety, October 2015.

[16] Leonard J. Schulman and Piyush Srivastava. Stability of causal inference. In Proc.32nd Conference on Uncertainity in Artifical Intelligence (UAI), June 2016. Availableat http://auai.org/uai2016/proceedings/papers/214.pdf.

[17] Alexander D. Scott and Alan D. Sokal. The repulsive lattice gas, the independent-set polynomial, and the Lovász local lemma. J. Stat. Phys., 118(5-6):1151–1261,March 2005.

[18] J. B. Shearer. On a problem of spencer. Combinatorica, 5(3):241–245, September1985.

[19] Alistair Sinclair and Piyush Srivastava. Lee-Yang theorems and the complexity ofcomputing averages. Comm. Math. Phys., 329(3):827–858, August 2014. Extendedabstract in Proc. 45th Annual ACM Symposium on Theory of Computing (STOC)(June 2013), pp. 625–634.

[20] Alistair Sinclair, Piyush Srivastava, Daniel Štefankovic, and Yitong Yin. Spatialmixing and the connective constant: Optimal bounds. Probability Theory andRelated Fields, 168(1–2):153–197, June 2017. Extended abstract in Proc. 26th AnnualACM-SIAM Symposium on Discrete Algorithms (SODA), 2015, pp. 1549–1563. Fullversion available at arXiv:1410.2595.

[21] Alistair Sinclair, Piyush Srivastava, and Marc Thurley. Approximation algo-rithms for two-state anti-ferromagnetic spin systems on bounded degree graphs.J. Stat. Phys., 155(4):666–686, March 2014. Extended abstract in Proc. 23rd AnnualACM-SIAM Symposium on Discrete Algorithms (SODA) (Jan. 2012), pp. 941–953.

[22] Alistair Sinclair, Piyush Srivastava, and Yitong Yin. Spatial mixing and ap-proximation algorithms for graphs with bounded connective constant. InProc. 54th Annual IEEE Symposium on Foundations of Computer Science (FOCS),pages 300–309. IEEE Computer Society, October 2013. Full version available atarXiv:1308.1762.

[23] Allan Sly. Computational transition at the uniqueness threshold. In Proc. 51stAnnual IEEE Symposium on Foundations of Computer Science (FOCS), pages 287–296. IEEE Computer Society, October 2010.

[24] Allan Sly and Nike Sun. Counting in two-spin models on d-regular graphs. Ann.Probab., 42(6):2383–2416, November 2014.

[25] Piyush Srivastava and Mario Szegedy. A simplified proof of a Lee-Yang typetheorem, July 2014. Available at arXiv:1407.5991.

[26] Masuo Suzuki and Michael E. Fisher. Zeros of the partition function for theHeisenberg, Ferroelectric, and general Ising models. J. Math. Phys., 12(2):235–246, 1971.

[27] Dror Weitz. Counting independent sets up to the tree threshold. In Proc. 38thAnnual ACM Symposium on Theory of Computing (STOC), pages 140–149. ACM,May 2006.

[28] C. N. Yang and T. D. Lee. Statistical theory of equations of state and phasetransitions. I. Theory of condensation. Phys. Rev., 87(3):404–409, August 1952.

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Computational Complexity

Consolidation of knowledge obtained through observation andanalysis is central to the enterprise of science. Scientific theories, in-volving rules and principles, often incomplete and imprecise, emergein order to explain and predict the seemingly inscrutable patterns inour observations. In this way, science helps us in dealing with, andsometimes in exploiting, complexity. Means of computation bringwith them new notions of complexity. Why can we bisect but not tri-sect angles with a ruler and compass? Why can we construct squareroots but not cube roots? So is trisection indeed more complex thanbisection? Invention of every new tool for computation issues newchallenges to understand its scope and limitations.

A person who can, within a year, solvex2 − 92y2 = 1, is a gan. akah. .∗

– Brahmagupta(Brahmasphut.asiddhanta,

c. 7th century CE)

∗gan. akah. : Sanskrit, nom. sing. n., lit.that which computes or calculates;

mathematician, astronomer.

Computational complexity theory is the abstract study of com-putational problems and the resources that are needed to solve them.Like in other areas of formal study, the abstractions are often basedon developing models. How is the input presented (e.g., as a se-quence of bits)? How does one model the computational problemthat needs to be solved? How is the solution represented (as an algo-rithm, a heuristic) and how is its correctness evaluated? And finally,is the solution efficient? How is efficiency to be measured? Efficiencyand complexity are closely related and inseparable notions. In manynaturally occurring systems, the quest for efficiency is a strong guid-ing force for the emergence of complexity. In biological evolution,an organism might be considered more advanced or complex if it isable to more efficiently exploit the resources in its environment.

Steve Cook meets the source of allcomplexity (Elephanta, 2013)

As a conceptual development, for example, the decimal (or binary)system provided simultaneously a means for succinct representationof numbers and their efficient manipulation. Determining the funda-mental obstacles to the efficient solution of computational problems,and classifying the problems according to their hardness, is the cen-tral goal of computation complexity theory. With changing context,new models have emerged and new resources have been studied,and the field today includes a rich and diverse body of fundamentalinsights, results and techniques. The role of various resources andtheir trade-offs, how we must restrict access to the input in orderto make the model relevant to applications, when can we not solve aproblem exactly but must settle for approximate solutions, models of

computational complexity

noise in the data, the effectiveness of randomised strategies, the re-lationship between randomness and hardness, etc., all are legitimateobjects of study in Computational Complexity Theory.

The relationship between deterministic and non-deterministic com-putation, as represented by the P not equal NP question, is the mostimportant problem in the field. Over the past four decades, sev-eral approaches have been tried, including those inspired by com-putability theory, by circuit complexity, and by algebraic complexity. Theprogress on this question has been frustratingly slow. In other areas,however, remarkable success has been achieved. Space complexity israther well understood; deep connections between pseudorandomnessand hardness of computation; results from communication complexityhave found unforeseen applications in a variety of areas; the notionof probabilistic proofs has spectacularly revolutionised our under-standing of approximability of hard problems. These studies involveinsights from mathematical areas of combinatorics, algebra, analysis,probability and information theory; they have in turn helped bringmany mathematical problems into sharper focus; this has benefitedall of these fields.

Areas

Members in our school are active in the development of manysub-areas of Complexity theory and their interaction with other fields.These sub-areas include Algebraic Complexity, Boolean Circuit Com-plexity, Coding Theory, Communication Complexity, Hardness ofApproximation and Pseudorandomness. We next give very brief in-troductions to these fields, followed by a short description of how weplan to grow in the future.

x y z

+ + + + + +

× × ×

+

f (x, y, z)

An algebraic circuit

Algebraic Complexity Several natural computational tasks and prim-itives happen to be algebraic in nature. Some standard examples arelinear algebraic primitives such as matrix ranks, polynomials suchas determinants or permanents, factorisation of integers, and so on.In such instances, it is natural to ask for algebraic constructions ofsuch primitives. Algebraic complexity theory studies the hardnessof algebraic computational tasks with respect to the number of basicalgebraic operations required to perform them. In the setting whenwe want to study multivariate polynomials, the basic operations arejust additions and multiplications. Algebraic circuit complexity aimsto study multivariate polynomials that are computed by algebraiccircuits.

Algebraic circuit complexity admits an analogue of the “P vs NP”question, called the “VP vs VNP” question (named in honour ofValiant for his seminal work that started this area). Fortunately, wehave several structural results for algebraic circuits that make prov-ing lower bounds for algebraic circuit classes more tractable thanthe Boolean counterparts. A flurry of recent activity has led to re-

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searchers to become increasingly optimistic about proving that theclasses VP 6= VNP in the not-too-distant future. In our group at TIFR,Ramprasad Saptharishi is one of those optimistic algebraic complex-ity theorists, who has been involved in some of the recent progressin the field.

x y z

∨ ∨ ∨ ∨ ∨ ∨

∧

∧

¬ ∧

∨

A boolean circuit

Boolean Circuit Complexity This is a very natural and clean combi-natorial model of computation, that abstracts digital hardware allaround us. Unlike an algorithm or a computer program that has towork for all possible input lengths, a given circuit works on a specificinput length. For example, a programmer who writes a program tofactor integers cannot assume that the given integer is 128-bit long. Acircuit designer comes up with one specific circuit for factoring 128-bit integers, another for 256-bit ones, yet another for 512-bit ones.Moreover, the three circuits may work very differently, unlike a sin-gle program for factoring integers. How quickly does the size ofthese circuits grow? Do they grow only polynomially in the inputlength, or is an exponential growth inevitable? If the former is truefor classical circuits, then the consequences will be truly amazing.Yet we have no clue to this fundamental question!

In general, we understand very little about the power of generalBoolean circuits. To make progress, researchers have tried under-standing restricted circuits. Two such classes of circuits, where re-markable progress has been made, are monotone circuits and cir-cuits of small depth. Several apparently hard to compute functionsare monotone, like Clique detection. Can we compute these func-tions efficiently by monotone circuits, i.e., ones with no negation.Though celebrated classical results exist, there are many questionsthat remain unanswered. Two examples are the following: what isthe monotone complexity of graph matching, a monotone problemthat can be solved very efficiently using non-monotone circuits. Canmonotone circuits solve it “quasi-efficiently”? This area has seen arecent revival. A depth-restricted circuit class that is of great inter-est, not only in complexity theory but also in learning using neuralnetworks, is that of constant-depth threshold circuits. While prac-titioners in learning theory seem to be forging ahead with learningconcepts expressible well and efficiently by such threshold circuits,basic questions about their expressive power remain unsolved. De-veloping a mathematical theory of such networks is a fundamentalchallenge.

In TIFR, Arkadev Chattopadhyay, Prahladh Harsha and JaikumarRadhakrishnan work on various aspects of circuit complexity.

Coding Theory Over the last seventy years, few research areas havehad such a profound and ubiquitous impact across both theory andpractice as the study of error-correcting codes. Claude Shannonand Richard Hamming co-founded the field of error-correcting codeswith their seminal work in the late 40’s. These works gave us themathematical footing to understand how much meaningful informa-

93


tion may be transferred across both noiseless and noisy channels.Since this foundational work, coding theory has been actively pur-sued by researchers across several scientific disciplines – informa-tion theory, electrical engineering, mathematics, linguistics, and com-puter science. One of the important algorithmic challenges is to de-sign “good” codes with efficient encoding and decoding algorithms.On the complexity front, coding theory has found myriad applica-tions in the construction of pseudo-random objects, list-decodablecodes, etc.

Modern applications of codes both in practice and theory, warrantstudying codes which exhibit even stronger encoding, testing anddecoding algorithms. In particular, one would like to design codeswhich allow for super-efficient testing and decoding, super-efficientin the sense that the testing and decoding algorithms have randomaccess to the corrupted codeword and do not even get enough timeto read the query all the bits of it. Unlike traditional coding the-ory, wherein a random codebook typically achieves optimal param-eters and the quest is for an explicit code; it is even unclear if codeswith such super-efficient testing/decoding properties exist. At TIFR,Arkadev Chattopadhyay, Prahladh Harsha, Vinod Prabhakaran, Jaiku-mar Radhakrishnan and Ramprasad Saptharishi work in various ar-eas related to error-correcting codes.

Communication Complexity The study of Communication Complex-ity was formally introduced in the seminal work of Yao (1979), in

Alice and Bob went at it,Dishing it out, bit by bit,

Neither of them would take a hit,So, Ω(n) it was by the time they quit.

– Vivek S Borkar

order to model the information processing bottleneck arising in dis-tributed computing. In this initial model, two players, typicallycalled Alice and Bob, with unlimited computational power abstractthe distributed nature of the problem. Each player only has a part ofthe input to a common task that they want to perform jointly. What isthe minimum amount of communication they need with each otherto perform the task? The measure of communication could be, forexamples of concreteness, the total number of bits exchanged, thenumber of interactions needed when each interaction is limited to afew bits, etc. Quite surprisingly, this simple model has found manyapplications in a diverse variety of areas in computer science, evenwhen there is no manifestly distributed computing going on.

Today the field of communication complexity is mature but rapidlygrowing, both in the hands of core communication complexity theo-rists and researchers who are looking at specialised models/problemsto make progress on exciting applications in other fields. It is cer-tainly one of the best models that has been exported by complex-ity theorists to the rest of computer science. Among the membersof the School, Arkadev Chattopadhyay, Prahladh Harsha, JaikumarRadhakrishnan and Pranab Sen work on various aspects of the field.In addition, Vinod Prabhakaran has been looking at the interface ofcommunication complexity and privacy.

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Hardness of Approximation One of the great successes of theoreti-cal computer science over the last three decades has been a greaterunderstanding of the approximability of various algorithmic optimi-sation problems. The celebrated PCP theorem (proved in the early90’s) demonstrates that the approximation version of several naturaloptimisations problems is as hard as the original optimisation prob-lem. Since this discovery in the early 90’s, hardness of approxima-tion has a been a very active field in computer science leading to anelaborate study of PCPs and proof systems. Probabilistically checkableproofs (PCPs), as the name suggests, provide an extremely efficientmeans of proof checking. PCPs are a means of encoding mathemati-cal proofs into a format such that the encoded proof can be checkedvery efficiently, although in a probabilistic manner, by looking at it atonly a constant number of locations (in fact, 3 bits suffice!). Thoughsurprising initially, this view of proof verification is intimately tiedto how well certain problems can be approximated.

More than two decades after the discovery of the the PCP the-orem, we have by now a very good understanding of the limits ofapproximation for a very broad collection of optimisation problems.However, for some other problems, such as vertex cover, max-cut,and 3-colouring; this machinery has its limitations and we do notyet understand their exact approximability threshold. Research inthis area has led to a better understanding of PCPs and improvedconstructions. One such direction led to the Unique Games Conjec-ture (UGC). The UGC stands at the centre of a web of reductionsand its positive resolution would imply immediately, through thesereductions, tight hardness of approximation for a wide collection ofproblems. In our research group at TIFR, Prahladh Harsha, Jaiku-mar Radhakrishnan and Ramprasad Saptharishi have been involvedin various aspects of proof checking and hardness of approximation.

Pseudorandomness Pseudorandomness is a general notion that for-malises, as the name suggests, random-looking objects. These takemany facets in complexity — error correcting codes, functions withlarge circuit complexity, expander graphs are all examples of pseu-dorandom objects. That is, a random set of points in 0, 1n will forma code with high probability, a random boolean function would havelarge circuit complexity, etc. Given that these randomly chosen ob-jects have very useful properties, can we find explicit constructions ofobjects that look random though constructed deterministically? For “How hard could it be to find hay in a

haystack?”

- Howard Karloff

example, can we construct efficient codes of good rate? Can weconstruct explicit expander graphs of small degree? These are theflavour of questions in the area of pseudorandomness. Also, thereare strong interconnections between various pseudorandom objectslisted above.

These objects help in understanding the role of randomness incomputation. For a concrete example, suppose we can find a pseu-dorandom distribution of small support that fools a certain algorithm,i.e. the algorithm’s performance on the pseudorandom distribution

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is not too different from its performance on truly random inputs.Then we can replace the random bits used by an enumeration ofour pseudorandom distribution, thereby completely derandomisingthe algorithm. If we can construct such distributions for any efficientrandomised algorithm, then BPP = P, and there is strong evidencesuggesting that this must indeed be the case.

In our group at STCS, Arkadev Chattopadhyay, Prahladh Har-sha, Vinod Prabhakaran, Jaikumar Radhakrishnan and RamprasadSaptharishi study various aspects of pseudorandomness in computerscience.

Quantum computation Over the past three decades, quantum com-puting has emerged as an active area of research. The currently ac-cepted theoretical formulation of quantum mechanics has deep im-plications for efficient computation. Our seeming inability to simu-late even simple quantum systems on classical computers led Feyn-man to suggest that we might perhaps be able to rig quantum me-chanical devices to solve computational problems efficiently. Thispromise has been realised theoretically over the past three decadeswith the discovery of some remarkable quantum algorithms, e.g.,Shor’s quantum factoring algorithm or Grover’s quantum search al-gorithm in the 1990s. Simultaneously, notions such as query com-plexity, communication complexity, cryptography, privacy, etc. havebeen generalised to the quantum setting, and sophisticated toolshave been developed to study them.

In STCS, Pranab Sen and Jaikumar Radhakrishnan study variousaspects of quantum computation and information.

Vision for Growth

The number of faculty members with research interests in com-plexity theory has grown significantly in the past decade, and as aresult the school now possesses expertise in a wide range of areas.The faculty and students are well-poised to produce high qualityand high impact contributions in core areas. At the same time, thefaculty should expand its scope to address areas where complexityis encountered in computer science and perhaps in other areas ofscientific enquiry. Aided initially by post-doctoral researchers andvisitors, the graduate program should expand to accommodate ex-ploratory research activities with potential to take the study of com-putational aspects of complexity beyond the currently practised coreareas.

Research Highlights: Arkadev Chattopadhyay

Arkadev studies natural models of computation with an aim to un-derstand the minimum amount of resources needed by them to solve

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research highlights: arkadev chattopadhyay

simple problems. The focus so far has been on models that are char-acterised by having various elements computing locally without fullaccess to data. The model then faces a bottleneck to aggregate theinformation generated by the various disparate elements. The bulkof his work is on two such general models: the first is Yao’s 2-partycommunication model and its various multiparty extensions. In thismodel, the information bottleneck is very explicit. The second modelis that of low-depth boolean circuits. The information bottleneck hereis less obvious. It roughly arises due to the fact that the informationgenerated by individual gates of the circuit have to be quickly (insmall depth) merged to generate the desired output. Both modelsare natural, play a central role in the theory of computation andhave many basic open problems.

Multiparty Communication: Arkadev has worked extensively on mul-tiparty communication complexity. An old research interest is the‘Number on the Forehead’ (NOF) model, famously introduced byChandra, Furst and Lipton (STOC’83). His more recent interest, mo-tivated by distributed computing, is a multiparty ‘Number-in-Hand’setting where communication among the parties is point-to-pointalong the edges of an underlying communication network.

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A Yes instance for the Set-Disjointnessproblem

In the NOF model, each of the k parties is missing a portion ofthe input, metaphorically held on its forehead. Thus, when thereare three or more players there is a large overlap between infor-mation held by any two players. It is known that such overlapcan be exploited to design surprisingly powerful protocols. On theother hand, lower bounds in this model are well known to yieldprogress in other areas including circuit complexity, proof complex-ity, pseudo-randomness, as well as data structures. Arkadev hasmade several contributions to this area [2, 3, 5, 1, 15, 11]. His mostimportant results the following [2, 3]: Arkadev [2] proved the firststrong lower bounds on the NOF complexity of a function com-putable by constant-depth circuits in polynomial size. Besides yield-ing independently interesting exponential lower bounds on the sizeof certain depth-3 threshold circuits, it paved the way for a laterwork that was considered a breakthrough. Building on this work,Chattopadhyay and Ada [3] and independently, Lee and Shraibman(CCC’08), were able to prove the first polynomial lower bounds onthe NOF complexity of a very important and basic function, calledSet-Disjointness. Proving such a lower bound, even for three play-ers, was a well-known open problem for nearly twenty years. Thatwork was followed up by exciting improvements from several otherresearchers, but obtaining a tight lower bound on the randomisedNOF complexity still remains open.

Arkadev and co-authors [13, 14, 9], inspired by distributed com-puting, studied the influence of network topology on multiparty‘number-in-hand’ point-to-point communication complexity. In manyscenarios, a number of agents want to perform collaboratively a com-mon task. The task needs to access inputs which are distributed

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among some of the agents, called terminals. The other agents, whodo not have inputs, also participate in processing and forwardinginformation. The agents are connected by an underlying communi-cation network. Each agent can talk to another if and only if there isa link connecting them in the network (unlike in the previous (NOF)multiparty blackboard model that we considered). We want to un-derstand the dependence of the minimum amount of communicationneeded over the links of the network to perform a task, on the un-derlying network topology. An important difference from a standarddistributed computing scenario is that agents decide on a protocolusing their full global knowledge of the network. A bit more pre-cisely, there is a global clock that ticks. At each tick of the clock,each agent determines, based on just its own input and all messagesit has received in the past, how to communicate with its neighboursin the network. This includes, for each neighbour, the message thatthis agent needs to send to the neighbour and whether the neigh-bour is expecting to receive anything from it. In a legal protocol,the following is always satisfied for all vertices u, v that are adjacent:the agent on vertex u sends a message of a specific length to theagent at v if and only if the agent at v was expecting so based onits internal state. Moreover, the protocol has access to an unlimitednumber of random coins that are public and therefore accessible toall agents free of cost. There are two natural ways to measure theamount of communication: first is the total number of bits transmit-ted over all the edges of the network. In a series of two works [13, 14]with collaborators, Arkadev proved tight topology sensitive boundson the total communication needed to compute simple functions likeElement-Distinctness, and testing bipartiteness, connectedness andother natural properties of a graph. These works made interestingcombination of arguments from communication complexity and thetheory of metric embeddings. In a later work [9], Arkadev provedtopology sensitive optimal bounds on the total number of rounds ofcommunication needed to compute functions like Set-Disjointness.For round-complexity, each edge has a fixed capacity in terms of thenumber of bits it can carry in each round, i.e., tick of the clock. Thiscapacity is typically set to unity. It is not hard to see that a commu-nication protocol optimising the total number of communicated bitscan be quite different from one that optimises the total number ofrounds for computing the same task.

Simulation Theorems, Asymmetric Communication, Data-Structures: Withcollaborators Michal Koucký, Bruno Loff and former Ph.D. studentSagnik Mukhopadhyay, Arkadev have been steadily working [4, 8, 7]on developing simulation theorems that lift relatively more rudimen-tary query complexity lower bounds to communication complexitylower bounds. In the decision tree setting, the algorithm is chargedone unit of cost for accessing/querying an input bit, out of a totalof n bits. This is the only source of cost for the algorithm. Thus, forefficiently computing a function f , a query/decision-tree algorithm

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research highlights: arkadev chattopadhyay

optimises the amount of queries needed in the worst case. Manylower bounds are known for this set-up. For example, simple func-tions like OR, AND and Parity have full cost of Ω(n). Consider theset-up of composed function: f g, a boolean function on 2nb inputbits, where g : 0, 12b → 0, 1 is a boolean function 2b bits and f onn bits. For such a composed function, there is a natural 2-party com-munication set-up. Alice gets nb input bits and Bob gets the othernb bits, with each of them missing b bits of input from each of the ninstances of g. Given a decision-tree query algorithm for f , there is anatural communication protocol for Alice and Bob towards solvingf g. Simulate the query algorithm for f , replacing the query of theith input bit of f with an invocation of the optimal communicationprotocol to solve the ith instance of g. The resulting communicationprotocol will have cost at most the product of the query cost of f andthe communication cost of g. Determining conditions under whichthis is an optimal protocol for f g is a very important problem. Ina classical work, Raz and McKenzie (FOCS’98) showed that this istrue when g is the indexing function on sufficiently large number ofinput bits, compared to the input size of f , i.e. b ≥ nc, for somelarge number c. This result was famously used by them to make abreakthrough to separate the monotone depth hierarchy. Much morerecently, Göös, Pitassi and Watson (FOCS 2015) made another break-through by applying the Raz-McKenzie simulation theorem to solvean old open problem. In a joint work [7], Arkadev extended theRaz-McKenzie simulation theorem by providing a sufficient generalproperty for g that guarantees the naive query simulating protocolto be optimal for f g for all f . This work also shows that naturalfunctions like Inner-Product and various Gap-Hamming functionshave this property, even when the inner function block size b is onlyO(log n).

While the above considered settings were ones in which Alice andBob have inputs of comparable size, it is natural to consider situ-ations where they do not, i.e., one player, say Alice, holds a muchlarger input of length N bits compared to what the other, Bob, hasof length n. Unlike before, we now allocate different budgets ofcommunication for each player, commensurate with its length of in-put. Thus, a problem can be non-trivially solved when Alice com-municates o(N) bits and Bob communicates o(n) bits. This settingwas considered in the seminal work of Miltersen, Nisan, Safra andWigderson (MNSW) in the nineties to analyse information transferbottlenecks facing data-structure algorithms that tried to minimisethe space used for storing data and the time spent on answeringqueries simultaneously. In particular, MNSW introduced a techniquecalled the richness method to prove strong lower bounds for de-terministic and one-sided randomised protocols. In another recentjoint work [8], Arkadev developed a technique, based on a novelsimulation theorem for this setting for a class of functions. Moreprecisely, let f be any boolean function on p bits and IP be inner-product on n bits. Then, f p×1 IP evaluates on x1, . . . , xp, each an

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n-bit string held by Alice, and y, an n-bit string with Bob, simplyas f

(IP(x1, y), . . . , IP(xp, y)

). It is not hard to observe that a simple

generic protocol that works for every such f is the one that simulatesa parity decision tree algorithm for f . In this protocol, Alice communi-cates O

(PDT( f ).n

)many bits and Bob does PDT( f )-many to Alice.

We prove that this is essentially an optimal deterministic protocolunless Bob communicates all her bits, i.e. Ω(n), to Alice, as long asp ≤ n/80. This is the first simulation theorem in the asymmetricsetting and interestingly, unlike in the symmetric case, parity deci-sion trees show up. Using this simulation theorem, they were ableto show for the first time, that the richness method of MNSW doesnot in general give tight bounds on the asymmetric complexity offunctions. Additionally, this yielded the first strong lower boundsfor natural data-structure problems like Vector-Matrix-Vector prod-uct problems that are of significant interest.

Boolean Circuits: Arkadev has also made several contributions [17,10, 16, 18, 6, 12, 11] to proving lower bounds on the size of constant-depth circuits to compute functions. The most recent such work [12]is with his Ph.D. student Nikhil Mande, where they resolve, amongother things, a problem on threshold circuits that has been open formore than twenty-five years since the seminal work of Goldmann,Håstad and Razborov (GHR-Complexity’92). While techniques ofdeep learning seem to be revolutionising the practice of machinelearning algorithms, the theory behind it is far from being under-stood. One impediment to this is the lack of our understanding ofthe expressive power of threshold circuits, where each gate is a linearthreshold function (LTF), or just a half-space. The power of an LTFis derived from the freedom of choosing weights associated with itsvariables. Indeed, neural network based learning techniques seem toexploit this freedom crucially. Yet, GHR showed that in such acyclicdepth-2 networks, if the top gate has small weight, then the weightson the bottom gates do not matter much. Every such bottom heavynetwork could be simulated by a slightly larger network where thebottom gates are light. The problem of understanding if in everydepth-2 network one could assume the bottom layer to be light, re-mained open and was identified repeatedly, by other researchers, asan important one. Arkadev and Nikhil [12] solved this problem com-pletely by exhibiting a simple function f that necessitates the bottomlayer to be heavy in every small depth-2 threshold circuit comput-ing f . In another pair of notable works [17, 10], Arkadev with col-laborators showed that the simple function Inner Product (and theboolean function MODp) does not even correlate well with systemsof generalised linear constraints expressed over MODq (where p, qare co-prime). This work combined exponential sum estimates withideas from additive combinatorics, solving an open problem due toBeigel and Maciel from 1997.

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research highlights: prahladh harsha

References

[1] Anil Ada, Arkadev Chattopadhyay, Omar Fawzi, and Phuong Nguyen. The NOFmultiparty communication complexity of composed functions. ComputationalComplexity, 24(3):645–694, 2015. Preliminary version in ICALP 2012.

[2] Arkadev Chattopadhyay. Discrepancy and the power of bottom fan-in in depth-three circuits. In 48th FOCS, pages 449–458, 2007.

[3] Arkadev Chattopadhyay and Anil Ada. Multiparty communication complexityof disjointness. Electronic Colloquium on Computational Complexity (ECCC), 15(002),2008.

[4] Arkadev Chattopadhyay, Pavel Dvorák, Michal Koucký, Bruno Loff, and SagnikMukhopadhyay. Lower bounds for elimination via weak regularity. In 34thSTACS, pages 21:1–21:14, 2017.

[5] Arkadev Chattopadhyay, Jeff Edmonds, Faith Ellen, and Toniann Pitassi. Upperand lower bounds on the power of advice. SIAM J. Comput., 45(4):1412–1432,2016. Preliminary version in SODA’12.

[6] Arkadev Chattopadhyay, Frederic Green, and Howard Straubing. Circuit com-plexity of powering in fields of odd characteristic. Chicago J. Theor. Comput. Sci.,2016, 2016.

[7] Arkadev Chattopadhyay, Michal Koucký, Bruno Loff, and Sagnik Mukhopad-hyay. Simulation theorems via pseudorandom properties. CoRR, abs/1704.06807,2017.

[8] Arkadev Chattopadhyay, Michal Koucký, Bruno Loff, and Sagnik Mukhopad-hyay. Simulation beats richness: New data-structure lower bounds. In STOC,2018. to appear.

[9] Arkadev Chattopadhyay, Michael Langberg, Shi Li, and Atri Rudra. Tight net-work topology dependent bounds on rounds of communication. In 28th SODA,pages 2524–2539, 2017.

[10] Arkadev Chattopadhyay and Shachar Lovett. Linear systems over finite abeliangroups. In IEEE CCC, pages 300–308, 2011.

[11] Arkadev Chattopadhyay and Nikhil S. Mande. Weakly-unbounded versus un-bounded error protocols in the NOF model. Theory of Computing. to appear.

[12] Arkadev Chattopadhyay and Nikhil S. Mande. Weights at the bottom matterwhen the top is heavy. Electronic Colloquium on Computational Complexity (ECCC),24:83, 2017. Under Submission.

[13] Arkadev Chattopadhyay, Jaikumar Radhakrishnan, and Atri Rudra. Topologymatters in communication. In 55th FOCS, pages 631–640, 2014.

[14] Arkadev Chattopadhyay and Atri Rudra. The range of topological effects oncommunication. In 42nd ICALP, pages 540–551, 2015.

[15] Arkadev Chattopadhyay and Michael E. Saks. The power of super-logarithmicnumber of players. In RANDOM, pages 596–603, 2014.

[16] Arkadev Chattopadhyay and Rahul Santhanam. Lower bounds on interactivecompressibility by constant-depth circuits. In 53rd FOCS, pages 619–628, 2012.

[17] Arkadev Chattopadhyay and Avi Wigderson. Linear systems over compositemoduli. In 50th FOCS, pages 43–52, 2009.

[18] James Martens, Arkadev Chattopadhyay, Toniann Pitassi, and Richard S. Zemel.On the expressive power of restricted boltzmann machines. In 27th NIPS, pages2877–2885, 2013.

Research Highlights: Prahladh Harsha

Prahladh Harsha’s research interests are in the area of theoreticalcomputer science, with special emphasis on computational complex-ity. He is best known for his work in the area of probabilisticallycheckable proofs (PCPs).

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Probabilistically checkable proofs (PCPs): The PCP theorem [3, 2] is acornerstone of modern computational complexity, and is connectedto many different areas of theoretical computer science, such as op-timisation and approximation, error correcting codes, cryptographyand interactive proofs. Informally speaking, the PCP Theorem statesthat any mathematical proof can be rewritten into a polynomiallylonger probabilistically checkable proof (PCP) such that its veracitycan be checked very efficiently, although in a probabilistic manner, bylooking at the rewritten proof at only a constant number of locations(in fact, 3 bits suffice) and furthermore proofs of false assertions arerejected with probability at least 1/2. The PCP Theorem has, sinceits discovery, attracted a lot of attention, motivated by its connectionto the hardness of approximation [23, 3, 2].

Local consistency between queries

At the technical-core of the PCP theorem is a local-to-global argu-ment that allows deducing a global property from local pieces that fittogether only approximately. An important question in this context isto understand which objects and properties satisfy such local-to-globalbehaviour. Such an understanding has led to construction of sim-pler and more efficient PCPs, improved local testing algorithms forpolynomials, which have in turned improved better hardness of ap-proximation results. Below, we discuss some of Prahladh’s researchcontributions in this context. In particular, we give a short descrip-tion of his work related to PCP composition, testing of Reed-Mullercodes, hardness of approximate colouring and high-dimensional ex-panders.

PCP composition: Proof composition is an essential ingredient in allknown PCP compositions. Prahladh’s most significant contributionis the notion of modular composition in PCP constructions. Previousconstruction of PCPs were extremely involved and elaborate, espe-cially in the composition step. His thesis work had involved provingmodular composition in the low-error regime [9, 8]. This modularcomposition was used to give an alternate combinatorial proof ofthe PCP Theorem [13] via gap amplification, which was then usedto give the current best shortest PCPs [10, 13]. In latter work withDinur [18], new variants of PCPs known as “decodable PCPs” wereintroduced to facilitate very smooth composition – in fact, composi-tion becomes almost definitional and syntactic given these variants.This resulted in the first generic, composition method for low errortwo-query PCPs. Earlier composition methods were either inapplica-ble in the low-error regime or non-modular (i.e., very much tailoredto the specific PCPs that were being composed), if not both. Thisnew composition theorem could be then used to give a considerablysimpler and modular proof of the recent result of Moshkovitz andRaz [32] – construction of 2-query low-error PCPs of nearly linearlength, which is the starting point of almost all known inapprox-imability results today. Subsequently, in joint work with Dinur andKindler [19], a modular generalisation of previous PCP constructionswas developed in the low-error regime [33, 4, 16] by studying a new

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notion of PCP soundness called “distributional soundness”. Thisnew notion of soundness allows one to invoke proof composition asuper-constant number of times without incurring a blow-up in thesoundness error, thus resulting in better PCP constructions.

Testing results for Reed-Muller codes: The problem of local testing ofcodes has received a lot of attention over the last two decades. Thebasic problem of Reed-Muller code testing is to check if a given func-tion f : Fn

q → Fq is close to a degree-d multivariate polynomial (overFq, the finite field of q elements). This problem, in its local testingversion, for the case when q = 2 was first studied by Alon, Kauf-man, Krivilevich, Litsyn and Ron [1], who proposed and analysed anatural 2d+1-query test for this problem. Subsequent to this work,improved analyses and generalisations to larger fields were discov-ered. These tests and their analyses led to several applications, es-pecially in hardness of approximation, which in turn spurred otherReed-Muller testing results (which were not necessarily local tests).Motivated by its connection to hardness of approximate colouringof hypergraphs, the following type pf multiplication-based tests tocheck if a given function f : Fn

q → Fq is a degree d polynomial werestudied.

Testing Reed-Muller codes – restrictionto a plane

• Teste,k: Pick P1, . . . , Pk independent random degree-e polynomialsand accept if and only if the function f · P1 · · · Pk is the evaluationof a degree-(d + ek) polynomial (i.e., is a codeword of the Reed-Muller code of dimension n and order (d + ek)).

The initial works studied the setting when e = 1 [1, 11]. Dinur andGuruswami [17] extended for it large e (but only k = 1) to show thatif β : Fn

2 → F2 is 2n−d/100-far from n-variate polynomials of degreer, then for a random degree-e polynomial p, the probability that thefunction β · p is a degree (d + e) polynomial is doubly exponentiallysmall in e (ie., 1/22Ω(e)

) where e = (n − d)/4. Motivated by theapplication of these results to hardness of approximate hypergraphcolouring, the above result of Dinur and Guruswami, in joint workwith Guruswami, Håstad, Srinivasan and Varma [24] was extendedto show the following result for low-degree polynomials over theternary field F3: Let β : Fn

3 → F be 3d/2-far from any n-variatepolynomial of degree at most (2n− 2d− 1), then its correlation witha random square of a degree d polynomial is exponentially small

in d. More precisely,∣∣∣∣Ep∈Pn

d

[ω

∑x∈Fn3

β(x)·(p(x))2]∣∣∣∣ = 1/22Ω(d)

where

ω = e2πi/3.In subsequent joint work with Srikanth Srinivasan [25], the follow-

ing robust analysis of the test Teste,1 was obtained: if β : Fn2 → F2 is

2n−d/100-far from n-variate polynomials of degree r, then the prob-ability that for a random degree-e polynomial p, the function β · pis 2Ω(n−d)-close a degree (d + e) polynomial is doubly exponentiallysmall in e (ie., 1/22Ω(e)

) where e = (n− d)/4. The difference betweenthis result and the previous result of Dinur and Guruswami being

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that while the previous result studied the probability of β · p beinga degree (d + e) polynomial, the new result studied the probabilityof β · p being close to degree (d + e) polynomials. This robust analogallows one to extend the testing results for Teste,k for k > 1 and e > 1(prior results only considered the case when either k = 1 or e = 1).

The last two decades have seen tremendous progress in design-ing better approximation algorithms as well as understanding thehardness of approximating constraint satisfaction problems. Despitethis progress, the status of the natural problems, such as approxi-mate colouring of 3-colourable graphs, has not been resolved yet. Infact, even for the case of constant colourable hypergraphs, there hasbeen (until recently) an exponential (if not doubly exponential) gapbetween the best known approximation algorithms and inapprox-imability results. In joint work with Guruswami, Håstad, Srinivasanand Varma [24], Prahladh proved the following inapproximabilityresults for hypergraph colouring. The following problems are quasi-NP-hard

A 3 colouring of the Peterson Graph

Hardness of hypergraph colouring:

• Colouring a 2-colourable 8-uniform hypergraph with 22O(√

log log n)

colours.

• Colouring a 4-colourable 4-uniform hypergraph with 22O(√

log log n)

colours.

• Colouring a 3-colourable 3-uniform hypergraph using at most(log n)O(1/ log log log n) colours.

U = Clausesr V = Varsr

πuv : 0, 13r → 0, 1r

LU : U → 0, 13r

LV : V → 0, 1r

u

v

π

PCP Theorem =⇒ 3SAT reduces toLabelCover

For the first two cases, the hardness results obtained are super-polynomial in what was previously known, and in the last case it isan exponential improvement. In fact, prior to this result, (log n)O(1)

colours was the strongest quantitative bound on the number of coloursruled out by inapproximability results for O(1)-colourable hyper-graphs, and (log log n)O(1) for O(1)-colourable, 3-uniform hyper-graphs. These results are obtained using using the low-degree poly-nomial code (aka, the “short code” of Barak et al. [5]) and Reed-Muller testing results discussed in the previous section.

PCPs and high-dimensional expanders: More than two decades afterthe discovery of the the PCP theorem, we have by now a very goodunderstanding of the limits of approximation for a very broad collec-tion of optimisation problems. However, for some other problems,such as vertex cover and max-cut, this machinery has its limitationsand we do not yet understand their exact approximability thresh-old. Research in this area led to the seminal paper of Khot [27],wherein he proposed the Unique Games Conjecture (UGC). The UGCstands at the centre of a web of reductions and its positive resolutionwould imply immediately, through these reductions, tight hardnessof approximation for a wide collection of problems. Another funda-mental question in this area is understanding the time complexity ofthe reductions guaranteed by the PCP Theorem. This relates to un-

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derstanding the size of the optimisation problem at which approxi-mation hardness sets in. This is usually stated in terms of the SlidingScale Conjecture (SSC) of Bellare et al. [7]. Recently, a new directionconnecting the study of PCPs with the area of high dimensional ex-panders in mathematics was proposed.

An expander is a sparse graph which is nevertheless highly con-nected. High dimensional expansion, informally speaking, is a gen-eralisation of expansion in a graph to higher dimensions, namelyto hypergraphs or to simplicial complexes. In fact, with the bene-fit of hindsight, if one revisits the PCP constructions till date, onenotices that a common underlying object in all these constructionsare certain high-dimensional expanding objects. For instance, all di-rect product and parallel repetition theorems are implicitly based onthe properties of the Johnson graph while all algebraic constructionsof PCP based on the low-degree tests are obtained via propertiesof the Grassmanian graph. The Johnson graph and Grassmaniangraph are classic examples of high-dimensional expanders. In fact,recently it has been observed that certain properties on these graphs(especially the Grassmanian) have been used to prove the 2-2 conjec-ture, coming tantalisingly close to a resolution of the Unique GamesConjecture [28, 21, 20, 6, 29]. Recent works in high-dimensional ex-panders [31, 30] have led to constructions of constant degree Ra-manujan complexes, which are the analogues of Ramanujan expandergraphs to the high-dimensional setting.

Very recently, in joint work with Irit Dinur and Yuval Filmus,Prahladh investigated the properties of the Johnson graph and itsderandomisation, the Ramanujan graphs of Lubotzky et al. [31, 30].He show that the classical direct product theorems [26, 22] can beextended to higher dimensions and we can prove high-dimensionalagreement theorems on the Johnson graph [14]. He then, was ableto use these agreement theorems to prove the right generalisation ofFriedgut-Kindler-Naor and Kindler-Safra-type theorems to all slicesof the Boolean hypercube [15]. The novel feature of these proofs isthat they are general enough that they not only hold for the Johnsongraph, but for certain high-dimensional expanders too [12].

References[1] Noga Alon, Tali Kaufman, Michael Krivelevich, Simon Litsyn, and Dana Ron.

Testing Reed-Muller codes. IEEE Trans. Inform. Theory, 51(11):4032–4039, 2005.(Preliminary version in 7th RANDOM, 2003).

[2] Sanjeev Arora, Carsten Lund, Rajeev Motwani, Madhu Sudan, and MarioSzegedy. Proof verification and the hardness of approximation problems. J.ACM, 45(3):501–555, May 1998. (Preliminary version in 33rd FOCS, 1992).

[3] Sanjeev Arora and Shmuel Safra. Probabilistic checking of proofs: A new char-acterization of NP. J. ACM, 45(1):70–122, January 1998. (Preliminary version in33rd FOCS, 1992).

[4] Sanjeev Arora and Madhu Sudan. Improved low-degree testing and its appli-cations. Combinatorica, 23(3):365–426, 2003. (Preliminary version in 29th STOC,1997).

[5] Boaz Barak, Parikshit Gopalan, Johan Håstad, Raghu Meka, Prasad Raghaven-dra, and David Steurer. Making the long code shorter. In Proc. 53rd IEEE Symp.on Foundations of Comp. Science (FOCS), pages 370–379, 2012.

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[6] Boaz Barak, Pravesh Kothari, and David Steurer. Small-set expansion in short-code graph and the 2-to-2 conjecture. (manuscript), 2018.

[7] Mihir Bellare, Shafi Goldwasser, Carsten Lund, and Alexander Russell. Efficientprobabilistically checkable proofs and applications to approximation. In Proc.25th ACM Symp. on Theory of Computing (STOC), pages 294–304, 1993.

[8] Eli Ben-Sasson, Oded Goldreich, Prahladh Harsha, Madhu Sudan, and Salil Vad-han. Short PCPs verifiable in polylogarithmic time. In Proc. 20th IEEE Conf. onComput. Complexity, pages 120–134, 2005.

[9] Eli Ben-Sasson, Oded Goldreich, Prahladh Harsha, Madhu Sudan, and Salil Vad-han. Robust PCPs of proximity, shorter PCPs and applications to coding. SIAMJ. Comput., 36(4):889–974, 2006. (special issue on Randomness and Computation;Preliminary version in 36th STOC, 2004).

[10] Eli Ben-Sasson and Madhu Sudan. Short PCPs with polylog query complexity.SIAM J. Comput., 38(2):551–607, 2008. (Preliminary version in 37th STOC, 2005).

[11] Arnab Bhattacharyya, Swastik Kopparty, Grant Schoenebeck, Madhu Sudan, andDavid Zuckerman. Optimal testing of Reed-Muller codes. In Proc. 51st IEEESymp. on Foundations of Comp. Science (FOCS), pages 488–497, 2010.

[12] Yotam Dikstein, Irit Dinur, Yuval Filmus, and Prahladh Harsha. Boolean functionanalysis on high-dimensional expanders. (manuscript), 2018.

[13] Irit Dinur. The PCP theorem by gap amplification. J. ACM, 54(3):12, 2007. (Pre-liminary version in 38th STOC, 2006).

[14] Irit Dinur, Yuval Filmus, and Prahladh Harsha. Agreement tests on graphs andhypergraphs. (manuscript), 2017.

[15] Irit Dinur, Yuval Filmus, and Prahladh Harsha. Low degree almost Booleanfunctions are sparse juntas. (manuscript), 2017.

[16] Irit Dinur, Eldar Fischer, Guy Kindler, Ran Raz, and Shmuel Safra. PCP charac-terizations of NP: Toward a polynomially-small error-probability. Comput. Com-plexity, 20(3):413–504, 2011. (Preliminary version in 31st STOC, 1999).

[17] Irit Dinur and Venkatesan Guruswami. PCPs via the low-degree long codeand hardness for constrained hypergraph coloring. Israel Journal of Mathemat-ics, 209:611–649, 2015. (Preliminary version in 54th FOCS, 2013).

[18] Irit Dinur and Prahladh Harsha. Composition of low-error 2-query PCPs usingdecodable PCPs. SIAM J. Comput., 42(6):2452–2486, 2013. (special issue for FOCS2009; Preliminary version in 51st FOCS, 2009).

[19] Irit Dinur, Prahladh Harsha, and Guy Kindler. Polynomially low error PCPswith polyloglog n queries via modular composition. In Proc. 47th ACM Symp. onTheory of Computing (STOC), pages 267–276, 2015.

[20] Irit Dinur, Subhash Khot, Guy Kindler, Dor Minzer, and Muli Safra. On non-optimally expanding sets in Grassmann graphs. In Proc. 50th ACM Symp. onTheory of Computing (STOC), 2018. (To appear).

[21] Irit Dinur, Subhash Khot, Guy Kindler, Dor Minzer, and Muli Safra. Towardsa proof of the 2-to-1 games conjecture? In Proc. 50th ACM Symp. on Theory ofComputing (STOC), 2018. (To appear).

[22] Irit Dinur and David Steurer. Direct product testing. In Proc. 29th IEEE Conf. onComput. Complexity, pages 188–196, 2014.

[23] Uriel Feige, Shafi Goldwasser, László Lovász, Shmuel Safra, and Mario Szegedy.Interactive proofs and the hardness of approximating cliques. J. ACM, 43(2):268–292, March 1996. (Preliminary version in 32nd FOCS, 1991).

[24] Venkat Guruswami, Prahladh Harsha, Johan Håstad, Srikanth Srinivasan, andGirish Varma. Super-polylogarithmic hypergraph coloring hardness via low-degree long codes. SIAM J. Comput., 46(1):132–159, 2017. (Preliminary version in46th STOC, 2014).

[25] Prahladh Harsha and Srikanth Srinivasan. Robust multiplication-based tests forReed-Muller codes. In Akash Lal, S. Akshay, Saket Saurabh, and Sandeep Sen,editors, Proc. 36th IARCS Annual Conf. on Foundations of Software Tech. and The-oretical Comp. Science (FSTTCS), volume 65 of LIPIcs, pages 17:1–17:14. SchlossDagstuhl, 2016.

[26] Russell Impagliazzo, Valentine Kabanets, and Avi Wigderson. New direct-product testers and 2-query PCPs. SIAM J. Comput., 41(6):1722–1768, 2012. (Pre-liminary version in 41st STOC, 2009).

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[27] Subhash Khot. On the power of unique 2-prover 1-round games. In Proc. 34thACM Symp. on Theory of Computing (STOC), pages 767–775, 2002.

[28] Subhash Khot, Dor Minzer, and Muli Safra. On independent sets, 2-to-2 games,and Grassmann graphs. In Proc. 49th ACM Symp. on Theory of Computing (STOC),pages 576–589, 2017.

[29] Subhash Khot, Dor Minzer, and Muli Safra. Pseudorandom sets in Grassmanngraph have near-perfect expansion. (manuscript), 2018.

[30] Alexander Lubotzky, Beth Samuels, and Uzi Vishne. Explicit constructions oframanujan complexes of type Ad. Eur. J. Comb., 26(6):965––993, 2005.

[31] Alexander Lubotzky, Beth Samuels, and Uzi Vishne. Ramanujan complexes oftype Ad. Israel Journal of Mathematics, 149(1):267–299, 2005.

[32] Dana Moshkovitz and Ran Raz. Two-query PCP with subconstant error. J. ACM,57(5), 2010. (Preliminary version in 49th FOCS, 2008).

[33] Ran Raz and Shmuel Safra. A sub-constant error-probability low-degree test,and a sub-constant error-probability PCP characterization of NP. In Proc. 29thACM Symp. on Theory of Computing (STOC), pages 475–484, 1997.

Research Highlights: Jaikumar Radhakrishnan

Jaikumar Radhakrishnan works in the area of complexity theory. Heis interested in the application of combinatorial, algebraic, proba-bilistic and information-theoretic tools to understand the limitationsof various models of computing. He has contributed results in areassuch as approximation algorithms, randomised algorithms, circuitcomplexity, pseudorandomness, classical and quantum communi-cation complexity, coding theory, streaming algorithms, distributedcomputing and classical and quantum information theory. We de-scribe below four recent works in which Jaikumar participated.

Set membership with few bit probes: Consider the following static datastructure problem. Let s(m, n, t) be the minimum number of bits re-quired to represent a set of size at most n from the universe [m] as ashort bit string so that question of the form “Is x in S?” can be an-swered after a small number of bit-probes into the string. The ques-tion of trade-off between the space used and the number of queriesis a fundamental question: it essentially asks how well one can com-press a string of length m with at most n ones so that each bit ofthe original string can be recovered locally, by reading only t bits ofthe compressed string. In the average case setting, the question wasconsidered by Minsky and Papert [14]. The problem was consideredin the worst-case setting by. Buhrman et al. [5], who showed that ifrandomisation is allowed while answering the queries and a smallprobability of error ε is tolerated, then one can represent the set us-ing O(ε−2n log m) and answer membership queries using just onerandom probe. However, in the deterministic setting, the functions(m, n, t) is not fully understood even for small values of t. Alonand Feige [1] showed that s(m, n, 2) = O(mn log((log m)/n)/ log m).This showed that even for t = 2 we can do better than storing the en-tire characteristic vector of the set; however the savings are small andvanish when the n ≥ log m. Mohit Garg and Jaikumar [9] strength-ened this bound by showing that there exist constants C, D > 0 such

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that

C ·m1− 1bn/4c ≤ s(m, n, 2) ≤ D ·m1− 1

4n+1 .

Interestingly, this data structure problem turned out to be closelyrelated to the satisfiability of 2SAT instances arising out of densegraphs of large girth. In general, the fundamental question of deter-mining s(m, n, t) for several interesting ranges of parameters remainsopen; see [16] for a survey of data structure problems in the bit probemodel.

Communication assisted agreement distillation Bogdanov and Mossel [4]consider the following problem. Suppose Alice receives a string of un-biased independent random bits and Bob receives a noisy copy of the samebits, where each bit is flipped with probability ε < 1/2. Alice and Bobmust output strings of k bits, where each is uniformly distributed, and thetwo strings agree with high probability. They showed that if no com-munication is allowed, then the probability of agreement is at most2−(ε/(1−ε))k and this bound is the best possible.B := 4ε(1− ε)

ε/(1− ε)

c = B(1− γ)− 2√

B(1− B)γ

c

γ

Communication versus agreementtrade-off

What if Alice and Bob are allowed to communicate some smallnumber of bits? Venkat Guruswami and Jaikumar [11] studied the re-lationship between communication and the probability of agreement.Suppose Alice wishes to send Bob a message of δk bits in order toensure that their k-bit outputs agree with probability 2−γk. How bigmust δ be as a function of γ? Using the standard hypercontractivityinequality, they show that δ(γ) ≥ B(1− γ)− 2

√B(1− B)γ. where

B = 4ε(1− ε). This implies that for δ(γ) = 0, we have γ ≥ ε/(1− ε),recovering the original result of Bogdanov and Mossel. Protocols arealso obtained that show that this trade-off between communicationand the probability of error is asymptotically tight.

Zero-error list decoding capacity of channels Shannon introduced theconcept of zero-error capacity of a discrete noisy channel [18], alsoreferred to as the Shannon capacity of a graph. Such a channel can bemodeled as a bipartite graph H = (V, W, E), with V correspondingto channel inputs, W to channel outputs, where (v, w) ∈ E if w canbe received at the channel output when v is transmitted on the chan-nel. One can associate a “confusability” graph G = (V, E′) with sucha channel, where (v1, v2) ∈ E′ if there is a common output w ∈ Wsuch (v1, w), (v2, w) ∈ E, so that v1, v2 can be confused with each0

1

2

0

1

2

3 3

The 4/3 channel

other. The zero error capacity of the channel is the largest asymp-totic rate at which information can transmitted with no error on thechannel, in n independent uses of the channel for large n. Lovászproved that the Shannon capacity of the 5-cycle, which is the small-est non-trivial case, is log2

√5 by introducing his influential Theta

function [13]. The work described below concerns the zero error listdecoding capacity that was introduced by Elias [8]. For a fixed L, thelist-of-L zero error capacity of a channel is the largest asymptotic rateat which one can communicate on the channel so that the decodercan output L codewords which must include the transmitted one).

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research highlights: jaikumar radhakrishnan

The smallest non-trivial case for zero error list decoding capac-ity is the 3/2 channel, where V = W = 1, 2, 3 and (v, w) ∈ E ifand only if v 6= w. Since every pair of input symbols can be con-fused with each other, the Shannon capacity of this channel is 0.However, there exists a code C ⊆ 1, 2, 3n of rate R bounded awayfrom 0 (i.e., of size 2Rn) which permits list-of-2 decoding with no er-ror on the 3/2 channel. The best known lower bound on R (to ourknowledge) approaches 1

4 log295 ≈ 0.212 [12]; the best upper bound

is log2(3/2) ≈ 0.585. It remains a major open question to improvethese bounds.

Marco Dalai, Venkat Guruswami and Jaikumar [7] recently madeprogress on the corresponding problem for the 4/3 channel (withlists of size 3), using a probabilistic combination of the Plotkin boundin coding theory and Hansel’s lemma for covering the completegraph by bipartite graphs.

Theorem. The size of a 4/3 code C ⊆ 1, 2, 3, 4n satisfies |C| ≤26n/19+o(n) ≈ 20.3158 n.

This improves upon a result of Arikan [3], who showed an upperbound of 20.3512 n. The problem for the general q/(q− 1) channel (forq large) is also not fully understood. In fact, it is known that thecapacity of this channel can be no better than 1/q even when the listsize is a fixed function of q; however, if the list size is restricted tobe q − 1, the rate of any code is exponentially small. On the otherhand, if lists of size q ln q are allowed, then one can achieve a rateclose to 1/q. Siddharth Bhandari and Jaikumar (to appear in ISIT2018, see also the earlier work in [6]) showed that with lists of sizesignificantly smaller than q ln q, the rates will be exponentially small.

Theorem. For every ε < 1/6, for all large q and large enough m,we have n(m, q, εq ln q) ≥ Ω(exp (q1−6ε/8) log2 m). Thus, for all ε <

1/6, the capacity of the q/(q− 1) channel with lists of size at mostεq ln q is exp(−Ω(q1−6ε)).

X3

X5 X1

X6

1 0

X4

0 1

X9 X6

X4

1 0

X7

1 0

An example decision tree

1

1

1

1

1

1

1

1

⊥

⊥

⊥

⊥

⊥

⊥

⊥

0

0

0

0

0 0

0⊥

A Yes instance of the GPW pointerfunction

Randomised versus deterministic query complexity For a Boolean func-tion f , let R0( f ) be the zero-error randomised query complexityof f , that is, the expected number of queries made for the worst-case input by the best randomised algorithm for f that answers cor-rectly on every input; D( f ) denote the deterministic query com-plexity of f . A long-standing conjecture of Saks and Wigderson(1986) stated that for any Boolean function f , R0( f ) = Ω(D( f )0.753...).Sagnik Mukhopadhyay, Jaikumar and Swagato Sanyal [15, 17] con-sidered the query complexity of the pointer function, GPWr×s, ofGöös, Pitassi and Watson [10]. For this function, they showed (a)R1(GPW

r×s) = Θ(r + s), and (b) R1(GPWr×s) = Θ(r +

√rs), where

R1 denotes the randomised one-sided error query complexity. Theseresults imply that (i) R0(GPW

s2×s) = O(D(GPWs2×s)2/3), thereby re-futing the conjecture of Saks and Wigderson, and (ii) R1(GPW

s×s) =

O(R0(GPWs×s)2/3), thereby providing a polynomial separation be-

tween the randomised zero-error and one-sided error query com-

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plexity measures. (In independent work, Ambainis et al. [2] exhib-ited the widest possible (quadratic) separations between determinis-tic and zero-error randomised query complexity, as well as betweenbounded-error and zero-error randomised query complexity, by con-sidering variants of this pointer function.)

References[1] Noga Alon and Uriel Feige. On the power of two, three and four probes. In

Proceedings of the Twentieth Annual ACM-SIAM Symposium on Discrete Algorithms,SODA 2009, New York, NY, USA, January 4-6, 2009, pages 346–354, 2009.

[2] Andris Ambainis, Kaspars Balodis, Aleksandrs Belovs, Troy Lee, Miklos Santha,and Juris Smotrovs. Separations in query complexity based on pointer functions.In Proceedings of the 48th STOC, pages 800–813, 2016.

[3] Erdal Arikan. An upper bound on the zero-error list-coding capacity. IEEE Trans.Information Theory, 40(4):1237–1240, 1994.

[4] Andrej Bogdanov and Elchanan Mossel. On extracting common random bitsfrom correlated sources. IEEE Transactions on Information Theory, 57(10):6351–6355, 2011.

[5] Harry Buhrman, Peter Bro Miltersen, Jaikumar Radhakrishnan, and SrinivasanVenkatesh. Are bitvectors optimal? SIAM J. Comput., 31(6):1723–1744, 2002.

[6] Sourav Chakraborty, Jaikumar Radhakrishnan, Nandakumar Raghunathan, andPrashant Sasatte. Zero error list-decoding capacity of the q/(q-1) channel. InFSTTCS 2006: Foundations of Software Technology and Theoretical Computer Sci-ence, 26th International Conference, Kolkata, India, December 13-15, 2006, Proceedings,pages 129–138, 2006.

[7] Marco Dalai, Venkatesan Guruswami, and Jaikumar Radhakrishnan. An im-proved bound on the zero-error list-decoding capacity of the 4/3 channel. In2017 IEEE International Symposium on Information Theory, ISIT 2017, Aachen, Ger-many, June 25-30, 2017, pages 1658–1662, 2017.

[8] Peter Elias. Zero error capacity under list decoding. IEEE Trans. InformationTheory, 34(5):1070–1074, 1988.

[9] Mohit Garg and Jaikumar Radhakrishnan. Set membership with a few bitprobes. In Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Dis-crete Algorithms, SODA 2015, San Diego, CA, USA, January 4-6, 2015, pages 776–784, 2015.

[10] Mika Göös, Toniann Pitassi, and Thomas Watson. Deterministic communicationvs. partition number. In Proceedings of the 56th FOCS, pages 1077–1088, 2015.

[11] Venkatesan Guruswami and Jaikumar Radhakrishnan. Tight bounds forcommunication-assisted agreement distillation. In 31st Conference on Computa-tional Complexity, CCC 2016, May 29 to June 1, 2016, Tokyo, Japan, pages 6:1–6:17,2016.

[12] J. Körner and K. Marton. New bounds for perfect hashing via information theory.European Journal of Combinatorics, 9:523–530, 1988.

[13] László Lovász. On the shannon capacity of a graph. IEEE Trans. InformationTheory, 25(1):1–7, 1979.

[14] Marvin Minsky and Seymour Papert. Perceptrons - an introduction to computationalgeometry. MIT Press, 1987.

[15] Sagnik Mukhopadhyay and Swagato Sanyal. Towards better separation betweendeterministic and randomized query complexity. In 35th IARCS Annual Confer-ence on Foundation of Software Technology and Theoretical Computer Science, FSTTCS2015, December 16-18, 2015, Bangalore, India, pages 206–220, 2015.

[16] Patrick K. Nicholson, Venkatesh Raman, and S. Srinivasa Rao. A survey of datastructures in the bitprobe model. In Space-Efficient Data Structures, Streams, andAlgorithms - Papers in Honor of J. Ian Munro on the Occasion of His 66th Birthday,pages 303–318, 2013.

[17] Jaikumar Radhakrishnan and Swagato Sanyal. The zero-error randomized querycomplexity of the pointer function. In 36th IARCS Annual Conference on Founda-tions of Software Technology and Theoretical Computer Science, FSTTCS 2016, Decem-ber 13-15, 2016, Chennai, India, pages 16:1–16:13, 2016.

[18] Claude E. Shannon. The zero error capacity of a noisy channel. IRE Trans.Information Theory, 2(3):8–19, 1956.

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research highlights: ramprasad saptharishi

Research Highlights: Ramprasad Saptharishi

The fundamental goal in theoretical computer scientist is to under-stand computational tasks and Ramprasad’s research focus is on un-derstanding hardness of computational problems that are algebraic innature.

Algebraic complexity theory

This field aims to find explicit polynomials that require large algebraiccircuits to compute them. Valiant [20] defined the classes VP and VNP

as algebraic analogues of the classes P and NP. Showing that P 6= NP

would imply that VP 6= VNP (under mild conditions) and thus iscertainly an easier task. Further, the algebraic analogues have morestructure and this has been exploited in many non-trivial results.

One of the possible directions towards proving lower bounds foralgebraic circuits is via depth reduction.

Background There has been a lot of work in this area and to geta better sense of the results it would be good to first look at somestructural results for arithmetic circuits. These are typically in theform of depth reductions wherein it is shown that a general circuit canbe equivalently simulated by “shallow” circuits.

Starting with Agrawal and Vinay [4], a series of results [12, 19]have shown that depth four circuits are nearly as powerful as gen-eral circuits, by presenting a depth reduction to squash any sub-exponential sized circuit to a sub-exponential sized depth four cir-cuit. The contra-positive of such a result can be stated as follows:

If an n-variate degree d polynomial f cannot be computed by homoge-neous depth four circuits with bottom fan-in bounded by

√d of size

nO(√

d), then it cannot be computed by polynomial sized arithmeticcircuits.

Computed by

algebraic circuits

of poly(n, d) size

Cannot be computed by

depth-4 circuits

of nO(√

d) size

(Or)


algebraic circuits

of poly(n, d) size


depth-4 circuits

of nO(√

d) size

Depth Reduction

This phenomenon is often referred to as the “chasm at depth four” inthe sense that in order to prove general circuit lower bounds, we merelyneed to show good enough lower bounds for homogeneous depth four cir-cuits.

Ramprasad and his coauthors [8] showed that the depth reduc-tion, surprisingly, can be further pushed to depth three circuits ofessentially the same size but over characteristic zero fields. Such aresult was not believed to be true as all prior depth reductions weresyntactic and field independent whereas a reduction to depth threecircuits must necessarily fail over finite fields. This also shows thatthere is a “chasm at depth three” albeit in the non-homogeneousregime.

The last few years has seen tremendous activity in the field. Start-ing with the work of Kayal [10] and Gupta, Kamath, Kayal andSaptharishi [9], a new technique called shifted partial derivatives wasintroduced to prove lower bounds for subclasses of homogeneous

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depth-4 circuits. Ramprasad and his coauthors [9] presented a 2Ω(√

d)

lower bound for precisely the class of depth-4 circuits obtained fromthe depth reduction of Agrawal and Vinay [4, 12, 19]. Subsequently,there has been a flurry of activity that resulted in many improve-ments and generalisation and the field currently stands at the “edgeof the chasm”:

In order to prove VP 6= VNP, we merely need to prove an nω(√

d) lowerbound for homogeneous depth four circuits.

Currently, we know of lower bounds of nΩ(√

d) for not just homoge-neous depth four circuits [11, 16], but for even more general models.

Thus the difference between the current state of the art, and therequired threshold to show VP 6= VNP is just the difference betweenω(·) and Ω(·) in the exponent! Thus any asymptotic improvementin the exponent would yield VP 6= VNP.

The question of proving lower bounds for arithmetic circuits is themost fundamental question question in the area of algebraic complex-ity. Any progress in this field, in terms of even lower bounds forrestrictive classes, would be a big step towards the eventual goal ofshowing VP 6= VNP. In the last few years, several groups have driventhe recent flurry of activity, and the entire field has an optimism thatthe main goal of VP 6= VNP would be solved in the near future.Some of the contributions of Ramprasad and his coauthors includethe only known exponential lower bound for homogeneous depth-5 circuits [15], functional lower bounds and connections to booleancircuits [7], separations between homogeneous depth-4 and depth-5circuits [14].

Polynomial identity testing

Non-commutative models: The current focus of Ramprasad’s researchis algebraic complexity from the context of polynomial identity test-ing (PIT). It is known that polynomial identity testing and lowerbounds are two sides of the same coin. Recent results of Saxena et al.[6, 2] have revived the approach via PIT by showing that it suffices toconstruct deterministic identity tests with running time polynomialin the size of the circuits, over barely non-constant number of variables.Ramprasad and Anamay Tengse, a graduate student at TIFR, are in-vestigating such identity tests for some restricted models of circuits,such as read-once branching programs, as a first step.

Identity tests via algebraic independence: Another approach to iden-tity testing was via the notion of algebraic independence. Beecken,Mitmann and Saxena [5] showed that deterministic constructionsalgebraic-rank-preserving maps would yield identity tests. Using this,Ramprasad and his coauthors [3] unified all known polynomial timeblackbox PITs at that time, however this approach only works overcharacteristic zero fields as it crucially relies on the Jacobian criterion.A recent results of Pandey, Saxena and Sinhababu [17] extended the

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Jacobian criterion over finite characteristic fields. Ramprasad andPrerona Chatterjee, another graduate student at TIFR, are currentlyworking on extending these connections to PIT.

Reed-Muller codes in the Shannon model

As mentioned earlier, coding theory is another field where the struc-ture of polynomials play an important role. Reed-Muller codes are afamily of codes that are just evaluations of low degree polynomials.Although much is known about these codes in the adversarial errormodel (or the Hamming model), we do not have a full understand-ing of how well they perform under random errors and erasures.Recently, Abbe, Shpilka and Wigderson [1] studied these codes un-der the Binary Erasure Channel (BEC) and showed that Reed-Mullercodes achieve capacity for certain ranges of parameters correspond-ing to codes of very high or very low rate. Kudekar et al. [13] showedthat constant rate (bounded away from 0 or 1) Reed-Muller codesachieve capacity in the erasure channel.

m/20 m

o(m) o(√(m/ log m))O(

√m)

Regime of r where Reed-Muller codeRM(m, r) are known to achieve

capacity for the BECA natural question then arises, “what is the resilience of Reed-

Muller codes under random errors?” Abbe, Shpilka and Wigder-son [1] showed that resilience of Reed-Muller codes under randomerasures, for certain parameters, would also shed light on the re-silience of Reed-Muller codes under random errors, for related pa-rameters. Ramprasad and his coauthors [18] gave an algorithmic ver-sion of this connection. Currently, this algorithm efficiently correctsthe largest number of random errors for a wide range of parametersknown thus far.

References

[1] Emmanuel Abbe, Amir Shpilka, and Avi Wigderson. Reed-muller codes forrandom erasures and errors. IEEE Trans. Information Theory, 61(10):5229–5252,2015.

[2] Manindra Agrawal, Sumanta Ghosh, and Nitin Saxena. Bootstrapping variablesin algebraic circuits. Electronic Colloquium on Computational Complexity (ECCC),25:35, 2018.

[3] Manindra Agrawal, Chandan Saha, Ramprasad Saptharishi, and Nitin Saxena.Jacobian hits circuits: Hitting sets, lower bounds for depth-d occur-k formulasand depth-3 transcendence degree-k circuits. SIAM J. Comput., 45(4):1533–1562,2016.

[4] Manindra Agrawal and V. Vinay. Arithmetic circuits: A chasm at depth four. InFoundations of Computer Science (FOCS), pages 67–75, 2008.

[5] Malte Beecken, Johannes Mittmann, and Nitin Saxena. Algebraic independenceand blackbox identity testing. Inf. Comput., 222:2–19, 2013.

[6] Michael Forbes, Sumanta Ghosh, and Nitin Saxena. Towards blackbox identitytesting of log-variate circuits. Electronic Colloquium on Computational Complexity(ECCC), 25:36, 2018.

[7] Michael A. Forbes, Mrinal Kumar, and Ramprasad Saptharishi. Functional lowerbounds for arithmetic circuits and connections to boolean circuit complexity. In31st Conference on Computational Complexity, CCC 2016, May 29 to June 1, 2016,Tokyo, Japan, pages 33:1–33:19, 2016.

[8] Ankit Gupta, Pritish Kamath, Neeraj Kayal, and Ramprasad Saptharishi. Arith-metic Circuits: A Chasm at Depth Three. In Foundations of Computer Science(FOCS), pages 578–587, 2013.

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[9] Ankit Gupta, Pritish Kamath, Neeraj Kayal, and Ramprasad Saptharishi. Ap-proaching the chasm at depth four. Journal of the ACM, 61(6):33:1–33:16, 2014.Preliminary version in the 28th Annual IEEE Conference on Computational Com-plexity (CCC 2013).

[10] Neeraj Kayal. An exponential lower bound for the sum of powers of boundeddegree polynomials. Electronic Colloquium on Computational Complexity (ECCC),19(81), 2012.

[11] Neeraj Kayal, Nutan Limaye, Chandan Saha, and Srikanth Srinivasan. An Ex-ponential Lower Bound for Homogeneous Depth Four Arithmetic Circuits. InFoundations of Computer Science (FOCS), 2014.

[12] Pascal Koiran. Arithmetic circuits: The chasm at depth four gets wider. Theoret-ical Computer Science, 448:56–65, 2012.

[13] Shrinivas Kudekar, Santhosh Kumar, Marco Mondelli, Henry D. Pfister, ErenSasoglu, and Rüdiger L. Urbanke. Reed-muller codes achieve capacity on erasurechannels. IEEE Trans. Information Theory, 63(7):4298–4316, 2017.

[14] Mrinal Kumar and Ramprasad Saptharishi. Finer separations between shallowarithmetic circuits. In 36th IARCS Annual Conference on Foundations of SoftwareTechnology and Theoretical Computer Science, FSTTCS 2016, December 13-15, 2016,Chennai, India, pages 38:1–38:12, 2016.

[15] Mrinal Kumar and Ramprasad Saptharishi. An exponential lower bound forhomogeneous depth-5 circuits over finite fields. In 32nd Computational ComplexityConference, CCC 2017, July 6-9, 2017, Riga, Latvia, pages 31:1–31:30, 2017.

[16] Mrinal Kumar and Shubhangi Saraf. On the power of homogeneous depth 4arithmetic circuits. In Foundations of Computer Science (FOCS), 2014.

[17] Anurag Pandey, Nitin Saxena, and Amit Sinhababu. Algebraic independenceover positive characteristic: New criterion and applications to locally low alge-braic rank circuits. In 41st International Symposium on Mathematical Foundationsof Computer Science, MFCS 2016, August 22-26, 2016 - Kraków, Poland, pages 74:1–74:15, 2016.

[18] Ramprasad Saptharishi, Amir Shpilka, and Ben Lee Volk. Efficiently decod-ing reed-muller codes from random errors. IEEE Trans. Information Theory,63(4):1954–1960, 2017.

[19] Sébastien Tavenas. Improved bounds for reduction to depth 4 and depth 3. Inf.Comput., 240:2–11, 2015. Preliminary version in the 38th Internationl Symposiumon the Mathematical Foundations of Computer Science (MFCS 2013).

[20] Leslie G. Valiant. Completeness Classes in Algebra. In Symposium on Theory ofComputing (STOC), pages 249–261, 1979.

Research Highlights: Pranab Sen

Efficient algorithms for quantum information locking

Information locking is a purely quantum phenomenon where a uni-formly random message is encoded in a quantum system using aclassical key of much smaller size. Without the key, no measurementof this quantum state can extract more than a negligible amount ofinformation about the message, in which case the message is said tobe ‘locked’. Furthermore, knowing the key, it is possible to recover,that is ‘unlock’, the message perfectly.

Pranab Sen and his coauthors obtained the first efficient algo-rithms for locking classical messages such that the information thatcan be extracted without knowledge of the key is negligible. Theydid so by discovering a novel connection between information lock-ing and embedding `2-norm into `1 norm and ‘quantising’ an effi-cient deterministic classical algorithm of the norm embedding prob-lem by Indyk (2007). The quantisation process required them to de-fine and construct a new notion of randomness extractors called per-mutation extractors. Their protocol for information locking includes

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research highlights: pranab sen

a simplified version that can be implemented with current quantumtechnology. As a by-product of their connection between locking andnorm embedding, they showed for the first time existence of lock-ing schemes with negligible information leakage and constant keysize. Their existence results use techniques similar to the probabilis-tic proof of Dvoretzky’s theorem in geometric functional analysis.(JACM 2013, QIP 2011 Plenary, STOC 2011, O. Fawzi, P. Hayden, P.Sen).

Zero Knowledge against Quantum Attacks

Zero knowledge protocols are a central concept in cryptography.These protocols allow a prover to convince a verifier about the truthof a statement without revealing any additional information aboutthe statement, even if the verifier cheats by deviating from the pre-scribed protocol. With the advent of quantum computation an im-portant question rears its head – what happens to classical zero-knowledge protocols when the cheating verifier has access to a quan-tum computer? Note that even if the verifier cheats quantumly, themessages exchanged with the prover and the prover itself continueto be classical. Thus, the prover does not know if it is interactingwith a classical or quantum verifier. One may expect that quantumcomputers can break some classical zero-knowledge protocols, i.e. aquantum verifier interacting with the prover may be able to extractinformation about from the message transcript that a classical verifiercannot. As one example, the Feige-Fiat-Shamir zero-knowledge pro-tocol for identity verification can be broken by a quantum computersimply because it relies on the hardness of factoring for security.

In a breakthrough, Watrous (STOC 2006) showed that two wellknown classical protocols viz. the graph isomorphism and graph3-colouring protocols of Goldreich, Micali and Wigderson continueto be zero knowledge against cheating quantum verifiers. ExtendingWatrous’ work greatly, Pranab Sen and his coauthors showed thatany problem that has a classical zero-knowledge protocol againstthe honest verifier also has, under a reasonable condition, a clas-sical zero-knowledge protocol which is secure against all classicaland quantum polynomial time verifiers, even cheating ones. Theircondition is a natural strengthening of the notion of honest verifiercomputational zero-knowledge, and includes in particular, the com-plexity class of honest verifier statistical zero-knowledge. (ICALP2008 Best Paper Track C, S. Hallgren, A. Kolla, P. Sen, S. Zhang).

Future work

Future work envisaged in quantum computation and complexity in-cludes making the locking protocol tolerant to small amounts ofquantum noise which would make it amenable to small scale ex-periments. Another direction of research will be to obtain new andstronger lower bounds in quantum communication complexity usingquantum information theoretic tools (e.g. deepening the scope and

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reach of the quantum information cost methods).

116

Formal Methods

Automata, Logics and Formal Verification

Modelling of computation using logic is a classical theme. Turingmodelled the computations of Turing machines in First Order Logic(FO), and thereby showed that satisfiability of FO is undecidable.Since then, logics have been found to match most computationalclasses. For example, Fagin showed that, expressively, the ExistentialSecond Order Logic (ESO) exactly corresponds to the NP languages.Temporal and modal logics, which explicitly model time, are central tothe study of computation in several settings. In the 1960’s, Kampshowed the expressive equivalence between FO[<] over words andLinear Temporal Logic (LTL). MacNaughton showed their equiva-lence with star-free regular languages and Schützenberger charac-terised these languages algebraically using the monoid variety Ap.Since then, first-order definable languages and logically induced finestructure within such languages continue to be studied intensively.

The importance of studying correspondences between logics andautomata was also recognised early. Büchi and Elgot (1961) showedthat monadic second-order logic over words (MSO[<]) exactly cor-responds with finite state automata (DFA). The effective reductionsbetween MSO[<] and DFA exemplify the formula-automaton con-struction technique which is at the heart of many model checkingalgorithms. The logic-automaton paradigm has since then been ex-tended to richer notions of computation including data, time, concur-rency and probabilistic choice.

Alur and Henzinger pioneered the study of logics and automatafor real-time computation. The study of expressive powers of timedlogics and automata, the algorithmic properties of these automata,and exploring their logic-automaton connection continue to be fruit-ful areas of current research.

The idea of using logic to reason about the correct working of pro-grams was formalised by Turing, Floyd, Hoare and Dijkstra, amongothers. Subsequently, with the introduction of Temporal logic (LTL)by Pnueli, methods for proving correctness of reactive and concur-rent programs were vigorously developed. Emerson, Clark and Sifakispromoted the idea of algorithmically verifying the logical propertiesof programs. This has grown into the area of Formal Verification ofPrograms with considerable applicability in developing high integrity

formal methods

systems and software. Hoare logics have been extended to modernprogramming languages and they form the theoretical core of pro-gram proving. In particular, giving assertional proofs of concurrentprograms is a very rich area of research.

For reactive and embedded systems, temporal logics have becomethe preferred formal specification mechanism. Model checking al-lows temporal properties of finite state systems to be verified by ex-ploring their state-graph. Efficient symbolic techniques (based onBDD/SAT/SMT solving) for exploring very large state-graphs havebeen investigated. This has generated considerable interest in tempo-ral and real-time logics for specifying requirements. Another promi-nent and related theme is the verification of probabilistic models. Log-ics for specifying properties of such probabilistic models have alsobeen studied.

Thus, the study of computation and systems using logics is a wellestablished theme. Equally influential has been the study of compu-tational techniques to analyse logical formulas. This allows buildingtools for doing logical reasoning using computers. Theorem proversare programs which can check, or even find, a proof of a logical as-sertion. Theorem proving has been applied to check the correctnessof proofs of many programs. It has also started making in-roads intochecking proofs in mathematics. These theorem provers are rootedin the study of proof theory, which focuses on proof systems and al-gorithmic techniques of finding and checking proofs.

On the other hand, model checkers are programs that algorithmi-cally explore the models of a logical formula. There has been ex-plosive growth in algorithmic techniques for solving propositionalsatisfiability, and modern SAT solvers can often solve formulas withmillions of clauses and variables. This has been extended to SMT

solvers that can handle constraints in specified theories (such as lin-ear arithmetic). Model checking for temporal (and modal) logicsrelies on representing the models of a formula by an automaton.Thus the logic-automaton connection forms the core of most tempo-ral (modal) model checking.

TIFR was an early entrant in India in the area of formal verifica-tion as well as semantics of programs. Several well-cited results wereobtained in the 1980’s and 90’s. The work in the last 10 years hasmainly focused on formal analysis of behavioural requirements (forembedded systems), controller synthesis from temporal specifica-tions, model checking of concurrent programs, verification of weakmemory models, and theorem proving.

On the theoretical side, in the area of real-time computation, therehas been work on expressive power of real-time logics, the logic-automaton connection for timed automata, and on elaborating thelogical structure of first-order definable languages. In the area ofproof systems, there has been exploration of Proof Assistants for FiniteSet Combinatorics, Diagrammatic reasoning, Non-self-referential para-doxes and interactive theorem proving.

On the practical side, work has addressed development of auto-

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mated verification and synthesis tools for various kind of programs.These include trace summariser for concurrent programs under weakmemory, proving correctness of programs that involve array proper-ties with alternating quantifiers, etc. There has been work on improv-ing performance of underlying solvers. A soft requirement guidedcontroller synthesis engine for generating robust and high qualitycontrollers has been developed. Innovative applications of formalmethods to financial markets, and biological systems have been at-tempted.

In the area of high performance computation, there was work onload balanced scheduling in multi-core, multi-place systems usingaffinity based techniques. Security issues in operating systems aswell as SCADA systems were studied and some novel formulationshave been proposed. An enhanced information security flow modelwas also proposed.

Research Highlights: Ashutosh Gupta

The research of Ashutosh Gupta is focused on developing scalablesoftware verification technology. His work is broadly focused onthree fronts. The first is about developing verification tools and al-gorithms for the classes of programs on which the state-of-the-artverification tools fail to work. The second is about improving perfor-mance of the solvers that are backbone of any verification tool. Thethird is finding new applications of the technology developed forthe verification such as analysis of biological systems, and financialmarkets.

On the first front, he has been involved developing several ver-ification tools that have won verification competitions and won abest paper award. Currently, he is focused on two tools. The firsttool TARA [POPL’15] is about analysing concurrent programs underweak memory and producing concise summaries of bad behaviours,which can be used for several program analysis queries such as effi-cient insertions of memory fences. The second tool TILER [SAS’17] isabout analysing array programs with complex properties. There hasbeen many works that analyse array programs. However, the toolshave limited applicability since they fail to handle the other kinds ofcomplexities in the programs such as pointers. We are developingTILER such that it will be resilient against such variations.

On the second front, he has been involved in improving the per-formance of SMT solvers that are backbone of verification tools. Re-cently, he has been focused on improving solvers for the theory ofbit-vectors and the theory of partial orders. For the bit-vectors, he hasadded support of simplifications due to the algebraic identities thatcurrent bit-vector solvers do not pay attention to and sometimes endup bit blasting the input problem unnecessarily [VMCAI’17]. Sinceanalysis of concurrent programs dependent on the theory of partialorders, he has been keenly focused on improving performance of thetheory of partial orders by introducing more effective graph search

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algorithms and better data structures to support backtracking.The verification technology is applicable to analysis of any dy-

namic system. He has been looking into analysis of biological sys-tems and financial markets. In biology, there are many systems thatare poorly understood and have high similarities with computationalsystems. For example, vesicle traffic system that transports materialsamong compartments of cells. The system has not been fully dis-covered and lots of information is missing. The system forms a net-work with graph properties. We have developed a synthesis tool thatfinds missing parts of an partially known vesicle traffic system [SASB2017]. He has also looked into applicability of verification methodson the analysis of financial markets. We looked into a problem ofcompliance of a stock exchange with regulations. We collected datafor an exchange and checked against a monitors that are synthesisedfrom specifications.

In short, his work has been aiming to solve instance of harder andharder problems on machines. The software verification problemand others are examples of it.

Research Highlights: Paritosh K Pandya

In the last 10 years, my research has focused mostly on logics, au-tomata and formal verification of timed systems, where I have workedon both the theoretical foundations as well as built prototype toolsfor formal verification of Embedded real-time systems. I have partic-ipated in large externally funded project at the strategic Centre forFormal Design and Verification of Software (CFDVS), IIT Bombay.

Expressiveness and Decidability of Metric Temporal Logics

Since the inception of work on timed logics and automata, issues offinding decidable real-time logics with reasonable expressive powerhave been central. For untimed logics, the celebrated Buchi-Kamp-McNaughten theorems establish expressive equivalence of FO[<],LTL[U, S] and Star-free regular languages. In the real-time scenario,Alur and Henzinger (1991) asked whether First order logic of Dis-tance FO[<,+1], a hybrid logic TPTL[U, S], and the metric temporallogic MTL[UI , SI ] all have the same expressive power. All these log-ics have undecidable satisfiability. They also introduced the logicMITL[UNS, SNS] and showed that it has decidable satisfiability andmodel checking. The proof was by reduction from MITL to nondeter-ministic timed automata (NTA). Unfortunately, the logic was much lessexpressive than NTA. Since then, the quest for decidable and yet ex-pressive logics (marked by Buchi-Kamp-McNaughten-like theorems)has been open. Our work has contributed to finding the answers.

Ehrenfeucht-Fraisse (EF) Games and Expressiveness of Metric TemporalLogics: We defined EF Games for Metric Temporal Logics. These wereused to delineate expressiveness of several important fragments of

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MTL[UI , SI ]. It was shown that logics BoundedMTL[UI , SI ], MITL[UI , SI ]

and MTL[F, P], incorporating the restrictions of bounded intervals,non-punctuality and unary modalities respectively, are expressivelyincomparable. Using the EF-game technique, the 20 year old conjec-ture of Alur and Henzinger that MTL[UI , SI ] is less expressive thanTPTL[U, S] was shown to be true [10].

Simoni Shah and I defined a class of Timed Unambiguous Lan-guages (TUL), and studied its logical and automata based charac-terisations. We showed that MITL[F∞, F∞] which has unary modali-ties with only lower-bound constraints is (surprisingly) expressivelycomplete for Partially-Ordered 2-Way Deterministic Timed Automata(PO2DTA) and the reduction from logic to automaton gives us itsNP-Complete satisfiability [12].

Since its introduction by us, the MTL EF games have become a fre-quently used technique. A comprehensive picture of expressivenessof various subclasses of MTL[UI , SI ] has emerged. This is used to pro-vide a map of MTL subclass’ expressivity versus satisfaction complex-ity. We have added several new points on this map by sharpeningthe existing frontiers [10, 12, 16].

Temporal Projections and Decidability of Metric Temporal Logic with Count-ing and Regularity: In a path-breaking result, Ouaknine and Worrellshowed that MTL[UI ] over finite words had decidable satisfiability.The result was proved by reduction to language equivalent 1-clockAlternating Timed Automata (1-ATA). Unfortunately, the former ismuch less expressive than the later. In a series of papers, we have in-vestigated extensions of MTL[UI ] with increasing expressive powerby adding non-punctual past modality SNS, a Counting modalityCn>n

I (φ), and a regular expression modality RegI(re). The decidabli-ity of each logic was proved by giving an equi-satisfiable reduction tothe base logic MTL[UI ] using a novel technique of temporal projections.Our logic MTL[RegI(re), SNS] is amongst the most expressive metrictemporal logic with decidable satisfiability known today [17, 19, 21].

In recent joint work with Khushraj Madnani and SN Krishna, weanalysed the expressive power of MTL[RegI(re)], and showed that itexactly corresponds with 1-clock Alternating timed automata with reset-free loops (1-ATA-rfl). Moreover, it also corresponds with an exten-sion of MSO[<] with guarded metric quantifier blocks, giving a logicQkMSO[<]. We have shown a four-variable property for QkMSO whichis similar to the famous 3-variable property of FO[<]. We have alsocharacterised the two-variable fragment Q2MSO with correspondingautomata and temporal logic.

A Determinisable class of Timed automata: Nondeterministic timed au-tomata (NTA) are known to be more powerful than deterministictimed automata. Moreover, the problem of knowing whether an(NTA) is determinisable is undecidable. Alur et al. (1994) proposedEvent Clock Automata ECA as a determinisable class of timed au-tomata. In joint work with Vijay Suman, SN Krishna and Lakshmi

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Manasa, we have proposed Integral reset timed automata, (IRTA), whichis the only other known class of determinisable timed automata. Itis shown that IRTA and ECA have incomparable expressive powers.Moreover, IRTA has the property of reduction to 1-clock IRTA [3, 6].This property is critical for many timed game synthesis algorithms.

First-Order Definable Languages and its Logical fine structure

Classical result of Kamp proved expressive equivalence betweenFO[<] and temporal logic TL[U, S], where as Schutzenberger alge-braically characterised this class with finite monoid variety Ap andhence provided an algorithm to determine whether a regular lan-guage is FO-definable. Thomas studied the quantifier-alternation hier-archy within FO[<] and showed that it is strict. Yet another hierarchywas that of FO[<, S] with successor predicate S(x, y). The low levelsof FO[<] quantifier alternation hierarchy have invited significant at-tention with the results ∆2[<] ≡ FO2[<] ≡ [F, P] ≡ PO2DFA ≡ DAbeing proved in 70’s and 80’s. Since then the progress has beenslow. It is only recently that the problem of membership in thirdand fourth levels of the hierarchy has been solved. A similar hierar-chy exists for FO[<, S] with ∆i[<, S] ⊆ ∆i+1[<]. For temporal logics,Therien and Wilke have defined an Until hierarchy, TLk[U, S/F, P],obtained by counting the nesting depth of U, S operators but ignor-ing the F, P operators. In joint work with A. Krebs, K. Lodaya, S.Shah, H. Straubing, we have refined the understanding of relation-ships between these hierarchies, as well as their logical and automatatheoretic characterisations.

• We showed that BΣ2[<] and ∆2[<, S] have incomparable expres-sive power, thereby showing that BΣ2[<] ⊂ ∆3[<] (see [8]).

• We gave an automaton characterisation of FO2[<, S]. We also de-fined a novel class of Deterministic temporal logics for FO2[<] andshowed that they all have NP-Complete satisfiability [1, 13, 20].

• We showed that TLk[U, S/F, P] ⊂ ∆k+1[<] there by relating theuntil hierarchy and the quantifier-alternation hierarchy [Submit-ted].

• We defined a betweenness predicate a(x, y) stating that a occursbetween positions x and y. This generalises previously studiedpredicates a(x) and S(x, y) which give logics FO[<] and FO[<, S].We show that ∆k[<, bet] = ∆k+1[<].

• We studied the logic FO2[<, bet] and showed that the logic hasthreshold counting abilities. Several threshold counting temporallogics with matching expressive power have been formulated andtheir satisfaction complexities have been worked out. Importantly,we gave an algebraic characterisation of FO2[<, bet] as monoid va-riety MeDA. This provides a method to decide if a regular lan-guage is FO2[<, bet] definable [18].

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Logics and Tools for Requirement Modelling, Model Checking andController Synthesis

Since the introduction of Temporal logic (LTL) by Pnueli (1977) andModel Checking by Emerson, Clark, and Sifakis (1986) there has beenintense research on methods for algorithmically verifying the logicalproperties of complex finite state systems. These tools work by re-ducing the problem to either automaton emptiness/language inclu-sion checking problem, or to logical satisfiability solving (SAT/SMT).Interval temporal logic is an alternate logic to LTL proposed by Halpern,Moszkowski and others, with some distinct advantages over LTL.Zhou, Hoare and Ravn proposed Duration Calculus (DC) as a prac-tical requirement modelling language (1991). A discrete time ver-sion of DC, further enhanced with second-order quantification, calledQuantified Discrete-time Duration Calculus (QDDC) was defined, and amodel checker for this logic called DCVALID was released in 1996.This remains a leading such tool for interval logic based specifica-tion. In several papers, we had investigated the applicability and ef-ficiency of tool DCVALID in reasoning about requirements as well asmodel checking complex safety and bounded liveness properties ofsystems [15]. In last 5 years, we have mainly focused on the controllersynthesis problem for QDDC specification. We have also investigatedformal analysis of requirements for their consistency and completeness.

DCSYNTH: Guided Controller Synthesis using Soft Requirements: Wehave formulated a technique for computation of a controller whichguarantees that hard requirements hold invariantly. Moreover, the con-troller also meets as many soft requirements as possible at each step, ina locally optimal fashion. All requirements are formulas of temporallogic QDDC, which can express complex safety and bounded live-ness properties. We have shown with examples and experiments thispast time soft requirement guided synthesis provides a useful abil-ity to specify and efficiently synthesise high quality controllers. soThe proposed method is implemented in a tool DCSYNTH. In jointlywork with A. Wakankar and R. Matteplackel, a case study of a minepump specification and its controller synthesis was used to illustrateour approach. A more extensive example of AMBA Bus arbiter con-troller specification and synthesis was also derived. (This work is hasbeen submitted.) We have also explored the use of soft requirementguided synthesis in generating robust controllers which can tolerateand recover from environmental assumption failures. Applicabilityof this technique to generate run time property enforcement shieldsis also under investigation.

DCTOOLS: heterogeneous requirement modelling: Interval temporal logicQDDC is a succinct and powerful logic for expressing safety andbounded liveness properties of systems. In practical requirementmodelling, heterogeneous visual and textual formalisms are used tospecify requirements. These include timing diagrams, message se-

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quence charts, state charts, temporal logic constraints etc. In jointwork with R Matteplackel and A Wakankar, we have proposed QDDC

as an intermediate requirement representation, where all these di-verse specifications can be semantically defined and logically inte-grated. With the integrated specification, analysis of diverse con-sistency conditions on the heterogeneous requirement can be car-ried out using the satisfiability checking of QDDC. The second or-der quantification constructs of QDDC are critical in formulatingconsistency. For example, ∀I∃O. D(I, O) states that for each inputsequence, there exists an output sequence such that specificationD(I, O) holds. This is sometimes called receptivity, or complete-ness of requirements. The method has been implemented in a toolDCTOOLS which provides translation of timing diagrams and mes-sage sequence charts into QDDC [22].

References[1] K. Lodaya, P.K. Pandya, S.S. Shah. Marking the chops:an unambiguous temporal

logic. in Proc. 5th IFIP International Conference on Theoretical Ccomputer Science(IFIP TCS 2008), Milano, Springer, 2008.

[2] Swarup Mohalik, A. C. Rajeev, Manoj G. Dixit, S. Ramesh, P. Vijay Suman, Par-itosh K. Pandya, and Shengbing Jiang. Model checking based analysis of end-to-end latency in embedded, real-time systems with clock drifts. in Proc. DesignAutomation Conference (DAC 2008), Anaheim, California, June 2008, ACM Press,2008.

[3] V. Suman, P.K. Pandya, S.N. Skrishna, L. Manasa. Integral reset timed automata:langauge inclusion and expressiveness. in Proc. Formal Modelling and Anaylsis ofTimed Systems (FORMATS 2008), Saint Malo, France, LNCS 5215, Springer, 2008.pp 78-02.

[4] P.K. Pandya. A Sampling Approach to the Analysis of Metric Temporal Logic. inPerspectives in Concurrency Theory, A Festschrift for P.S. Thiagarajan, UniversityPress (India) Pvt Ltd, 2008.

[5] V. Suman, P.K. Pandya. Timed and Hybrid Automata in SAL. in Proc. SecondInternational Workshop on Real Time and Embedded Systems (RTES 2008), Timisoara,Romania. IEEE Computer Society Press, 2008.

[6] V. Suman, P.K. Pandya. Determinization and Expressiveness of Integer ResetTimed Automata with Silent Transitions. in Proc. third International Workshop onLanguages and Automata Theory and Applications (LATA 2009), Tarragona, Spain,LNCS 5457, Springer, 2009.

[7] A Wakankar, AK Bhattacharjee, SD Dhodapkar, PK Pandya, K Arya. Automatictest case generation in model based software design to achieve higher reliability.in Proc. 2nd International Conference on Reliability, Safety and Hazard (ICRESH),IEEE , 2010.

[8] K. Lodaya, P.K. Pandya, S.S. Shah. Around Dot-depth Two. in Proc. 14th Conf.on Development in Language Theory (DLT2010), London, Canada, LNCS 6224,Springer, 2010.

[9] P.K. Pandya, S.S. Shah. Unambiguity in Timed Regular Langauges: Automataand Logics. in Proc. Formal Modelling and Anaylsis of Timed Systems (FORMATS2010) Austria, LNCS 6246, Springer, 2010.

[10] P.K. Pandya, S.S. Shah. On Expressive Powers of Timed Logics: ComparingBoundedness, Non-punctuality and Deterministic Freezing. in Proc. 22nd In-ternational Conference on Concurrency Theory (CONCUR 2011) Aachen, Germany,LNCS 6901, Springer, 2011.

[11] D. Kini, S.N. Krishna, P. Pandya. On Construction of Safety Signal Automata forMITL[UI , SI ] using Temporal Projections. Proc. Formal Modelling and Anaylsis ofTimed Systems (FORMATS 2011) Aalborg, Denmark, LNCS 6919, Springer, 2011.

[12] P.K. Pandya, S.S. Shah. The unary fragments of metric interval temporal logic:bounded versus lower bound constraints. in Proc. 10th International Symposium onAutomated Technology for Verification and Analysis (ATVA 2012), Trivendrum, LNCS7561, Springer, 2011.

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[13] P.K. Pandya, S.S. Shah. Deterministic Logics for UL. in Proc. 10th InternationalColloquium on Theoretical Aspects of Computing (ICTAC 2013), Shanghai, LNCS8049, Springer, 2013.

[14] P.K. Pandya, P.V. Suman. An Introduction to Timed Automata. in Modern Ap-plications of Automata Theory, IISC Research Monograph Series, World Scientific,2012.

[15] A. Babu, P.K. Pandya. Chop Expressions and Discrete Duration Calculus. inModern Applications of Automata Theory, IISC Research Monograph Series, WorldScientific, 2012.

[16] K. Madnanni, SN Krishnan, P.K. Pandya. On Unary Fragments of MTL and TPTLover Timed Words. in Proc. 11th International Colloquium on Theoretical Aspects ofComputing (ICTAC 2014), LNCS 8687, Springer, 2014.

[17] SN Krishnan, K. Madnanni, P.K. Pandya. Partially Puntual Metric TemporalLogic is Decidable. in Proc. International 21st International Symposium on TemporalRespresenation and Reasoning (TIME 2014), Verona, IEEE Computer Society, 2014.

[18] Andreas Krebs, Kamal Lodaya, Paritosh Pandya, and Howard Straubing. Two-variable logic with a between relation. in Proceedings of the 31st Annual ACM/IEEESymposium on Logic in Computer Science (LICS 2016), AMC/IEEE, 2016. pp. 106–115.

[19] S.N. Krishna, K. Madnani,and P.K. Pandya. Metric temporal logic with count-ing, International Conference on Foundations of Software Science and ComputationStructures (FoSSaCS 2016), LNCS 9634, Springer, 2016. pp. 335–352

[20] K. Lodaya, and P.K. Pandya. Deterministic Temporal Logics and Interval Con-straints, Proceedings of the Ninth Workshop on Methods for Modalities (M4M 2017),Indian Institute of Technology, Kanpur, India. LNCS , Springer, 2017. pp. 23–40.

[21] S.N. Krishna, K. Madnani,and P.K. Pandya. Making metric temporal logic ra-tional. 42nd International Symposium on Mathematical Foundations of Computer Sci-ence, (MFCS 2017), August 21-25, 2017 - Aalborg, Denmark. LIPIcs 83, SchlossDagstuhl - Leibniz-Zentrum fuer Informatik, 2017. pp.77:1–77:14,

[22] Raj Mohan Matteplackel, Paritosh K. Pandya, and Amol Wakankar. Formaliz-ing timing diagram requirements in discrete duration calulus. 15th InternationalConference on Software Engineering and Formal Methods (SEFM 2017), Trento, Italy,September 4-8, LNCS 10461, Springer 2017. pp. 253–268.

Research Highlights: N Raja

Non Self-referential Paradoxes and Classical Set Theory

We have presented novel proofs of Cantor’s theorem in set theory:namely that the cardinality of the power set of a set X exceeds thecardinality of X, and in particular the continuum is uncountable.One of our proofs is inspired by Yablo’s non-self-referential Liar’sparadox, and it has a dual relationship to yet another proof inspiredby Kurepa’s Lemma. Every proof of Cantor’s theorem – that for noset there is a function mapping its members onto all its subsets –constructs a subset which is ‘left-over’ by any onto mapping fromany set to its power set. The traditional diagonalisation proof in-volves an explicit invocation of the negation operation in order todefine the ‘left-over’ subset. Our proof differs from the most wellknown proof of Cantor’s theorem, by the notable feature that it canbe constructed in a way which does not require an explicit use of thenotion of negation. Our proof uses diagonalisation at a higher level,and constructs the ‘left-over’ subset without explicitly invoking thenegation operation.

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Programming Language Semantics and Social Choice Theory

Mathematical structures designed for building semantics of variouslogics and programming languages have largely dealt with set the-oretic constructions from the time of Tarski. We have explored non-standard semantic domains and focused on the theory of social choicefor studying semantics of computation. There are interesting inter-connections between results in the theory of social choice and thoseof formal logic in the context of reasoning about programming lan-guage semantics. We have examined results from voting theory,which studies individual and collective social choice and aggrega-tion of the choices, and demonstrated in particular an interestingcorrespondence between judgement aggregation mechanisms fromvoting theory and semantic notions in models of computation.

Diagrammatic Reasoning for Boolean Equations

Diagrammatic approaches to deductive and formal reasoning haveseen a resurgence in recent years. We have proposed a diagrammaticmethod for deciding whether Boolean equations over set-valued vari-ables are tautologies or not. Conventional diagrammatic approachesto the above decision problem work reasonably well when the to-tal number of sets under consideration is rather small. However,conventional approaches become cumbersome, if not completely un-usable, while dealing with a large number of sets. We have devisedan algorithm for the above decision problem, that scales well whenthe number of set variables in the equations increases rapidly.

Proof Assistants and Formalised Finite Set Combinatorics

Formalisation of any mathematical theory becomes a difficult taskbecause the length of a formal proof blows up significantly. We havedeveloped a library of definitions and facts on sets and posets thatcan reduce this effort. To demonstrate the effectiveness of our li-brary, in joint work with Abhishek Kr. Singh, we have developedfully formalised proofs of some central theorems from combinatoricssuch as Dilworth’s decomposition theorem, Mirsky’s theorem, Hall’smarriage theorem and the Erdös-Szekeres theorem. Dilworth’s de-composition theorem is the key result in these formalisations. Weuse Dilworth’s theorem in the proofs of Hall’s Marriage theorem andthe Erdös-Szekeres theorem. The combinatorial objects involved inthese theorems are sets, posets, graphs and sequences. All the proofsare formalised in the Coq proof assistant. Our initial formalisationuses the Principle of Excluded Middle. We have explored the pos-sibility of using computation instead of deductions when reasoningwith finite structures and applied this approach in developing a fullyconstructive formalisation of the above results

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Interactive Proof Checking and Program Verification

Formal logic has for long been believed to be an impractical meansfor constructing detailed correctness proofs of programs. In a con-troversial paper, R.A. De Millo, R.J. Lipton and A.J. Perlis arguedagainst formal verifications of programs, mostly motivating their po-sition by an analogy with proofs in mathematics, and in particularwith the impracticality of a strictly formalist approach to this dis-cipline. The recent, impressive advances in the field of interactivetheorem proving with the associated development of powerful toolssuch as proof assistants provide an interesting ground for a criti-cal revisiting of those theses. These advances have given rise to aninteresting consequence – viz. the practical feasibility of importingtechniques developed in the computer science community and rede-ploying them to improve the main activity of the working mathe-matician, namely the process of proof development. At the core ofsuch redeployed techniques lie the notions of formal systems, formalreasoning, and formal proofs. However the process of formalisingmathematics is a highly non-trivial task, and gives rise to a num-ber of challenging and interesting issues which need to be addressedin order to make the discipline of machine-supported mathematicsmore prevalent in the future.

In joint work with Andrea Asperti and Herman Geuvers, we haveanalysed the languages that are being used to express proofs, andalso addressed a number of issues which arise while making the in-terface of proof checking systems more user friendly. We have alsore-examined the social nature of proof and program developmentand shown that though its social aspect is uncontroversial and in-eluctable, at the same time formal verification is not antithetical toit. We have shown how formal verification can be made to cope withthe social aspects, and at the same time can also be made to easeand enhance the collaborative nature of the process of developmentof program correctness proofs

Conjugacy Equations in Combinatorics Over Words

We have solved decision problems associated with conjugacy equa-tions on languages. The conjugacy equation in the context of lan-guages are equations of the form XZ = ZY (where X, Y, Z rangeover languages), and the languages X, Y are said to be conjugatesprovided there exists a non-empty Z which satisfies the conjugacyequation. In joint work with Benny George Kenkireth, we have comeup with a characterisation of conjugacy based on relative lengths ofwords in the number of interesting contexts, and also obtained nec-essary and sufficient conditions over the length of words associatedwith the given languages. We also have characterisations based onthe structure of the elements in the constituent languages. Thesecharacterisations are initially formulated with stringent conditionson the size of the languages, but are later extended to encompasssets of arbitrary cardinality. We have also extended the list of results

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obtained with characterisations based on decision procedures overconjugators

Process Algebras and Frameworks of Mobility

In the design space of models for concurrency, there are three ma-jor paradigms which have been shown to extensive expressive power,namely the concurrent constraint paradigm, Actors, and the π-calculus.Each of them is known to be capable of embedding various othermodels of sequential and concurrent computation as special casesof itself. However, the above three paradigms appear to be com-pletely divergent from each other. This is not surprising since theyevolved from distinct sub-disciplines of computer science. The dif-ferences in the primitives of the two paradigms gives them theirdistinct flavour, but also makes the task of relating them a non-trivial one. We have related the paradigms of concurrent constraintsand π-calculus by defining a minimal calculus which contains thecore features of concurrent constraint paradigm, and by construct-ing a semantic-preserving embedding from it to the π-calculus. Wehave also related Actors and π-Calculus by first enriching the Actormodel by defining mechanisms for achieving higher levels of abstrac-tion. This helps in reasoning with collections of Actors termed ActorTroupes. In joint work with RK Shyamasundar, we identify a notionof observation equivalence between Actor Troupes; and provide asemantics for the enriched Actor model, in terms of the π-calculus– which is canonical process calculus for the semantic analysis ofobject-based concurrent systems. Further, we have shown that thealgebraic notion of weak reduction equivalence in π-calculus, cor-responds precisely to observation equivalence of the correspondingActor Troupes.

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Information Theory and Communications

The area of Information Theory, founded by Claude Shannon in 1948

motivated by the problem of communication, has had wide-rangingimpact, especially in communication engineering, theoretical com-puter science, statistics and machine learning, cryptography, classicaland quantum physics, biology and neuroscience, economics, proba-bility theory and mathematics in general. The work carried out inthe school reflects some of this diversity.

Wireless Communications

Developing a general theory for understanding fundamental perfor-mance limits of wireless networks, can be seen as the “Holy Grail”of modern network science. Wireless networks not only provide acommunication medium but progressively impact many importantaspects of life, such as bridging the urban-rural divide by providingbetter last-mile connectivity, enabling smart cities, vehicular commu-nication, empower IoT applications, etc. All these applications areenvisaged to be implementable in upcoming 5G standards, wherethe major technical challenges are i) providing massive capacity anddense connectivity, ii) support for an increasingly diverse set of ser-vices, applications and users, and iii) flexible and efficient use of allthe available wireless spectrum.

Theoretical work on wireless networks have been an active re-search area for couple of decades, but fundamental characteristics,such as capacity (maximum sum-throughput), coverage and connec-tivity, optimal scheduling, have not been fully understood due to thechallenging wireless propagation medium and the dynamic topol-ogy changes of the network. An alternative approach for design andanalysis is via extensive simulations, but that is generally prohibitivein computation and eventually has limited scope.

Quantum Information Theory

Quantum information theory was a thriving field even before thefirst musings on quantum computation arose in the early eighties.The goal of quantum information theory is to understand the infor-mation processing capabilities of quantum systems, i.e., qubits andquantum channels. In this setting, one can study the capabilitiesof quantum channels to transmit classical information, expressed as

information theory and communications

the classical capacity of the quantum channel, and the capabilities oftransmitting quantum information, expressed as the quantum capac-ity. A closely related area is the study of entanglement as a resource,both for its own sake as well as for its power to aid in informationtransmission.

Many results in classical information theory have direct analoguesin the quantum setting. However many do not, and this leads to sev-eral surprising and powerful features of quantum information the-ory that transcend the capabilities of classical information process-ing. The most well known example is the possibility of informationtheoretically secure private key distribution in the presence of aneavesdropper, also known as quantum key distribution. A less wellknown, but nevertheless very surprising, feature is the possibility ofusing very small private keys to encrypt cleartext in a quantum ci-phertext so that no bounded quantum memory adversary can gleanany information about the cleartext, a phenomenon known as quan-tum locking.

The last decade has seen phenomenal advances in quantum in-formation theory. Notable among them are quantum locking de-scribed above, superadditivity of classical capacity of a quantumchannel, zero knowledge interactive proofs immune to quantum at-tacks and one shot bounds in network quantum Shannon theory.Quantum information theoretic tools have also been applied to provelower bounds in quantum communication complexity, akin to similarwork in the classical setting. On the physics side, quantum informa-tion theoretic methods have been applied to problems in statisticalphysics and black hole entropy. Thus at present, quantum informa-tion theory is a very active and fruitful area of research.

Theoretical Computer Science

Information theoretic techniques have found uses in various areas ofcomplexity theory and combinatorics. In the area of pseudorandom-ness, information theoretic measures play a central role in quantify-ing the randomness available in imperfect sources and how much theavailable randomness can be refined. In complexity theory, informa-tion theoretic arguments are used to show lower bounds for a varietyof problems. In particular, the thriving area of communication com-plexity, described in greater detail in the Complexity Theory section,uses ideas closely related to information theory. Combinatorial argu-ments that underlie many technical proofs in this area are now reg-ularly being reformulated in information theoretic language; purelycombinatorial inequalities have formulated in information theoreticlanguage and tools from information theory have been employed toderive them. For example, ideas related to graph entropy have beenfruitfully applied to showing bounds on hashing, circuit complexity,and zero-error capacity of communication channels. Most problemsare far from settled in these areas, and there is scope for combininginformation theoretic techniques with recent developments based on

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algebraic methods.

Information Theoretic Cryptography

This area studies secure communication and computation withoutmaking any assumptions on the computational capabilities of theadversary. Shannon laid the foundations of modern cryptography inhis seminal 1949 paper by exactly characterising when secure com-munication is/is not possible. Later works studied how resourcessuch as noisy channels, distributed sources, or quantum phenomenoncan be exploited to achieve secure communication. In this line, sev-eral interesting questions on secure communication such as over net-works of noisy channels remain active topics of research. On infor-mation theoretically secure computation, several basic questions re-main open. Roughly, the goal of secure computation is for mutually

Though I am not naturally honest,I am sometimes so by chance.

– William Shakespeare,The Winter’s Tale

distrusting parties to collaborate to compute a function of their datawhile revealing nothing more about their private data to each other.In several problems of interest, such as multiparty computation, pri-vate information retrieval, and secret sharing, the best known resultson communication complexity have lower bounds which are nearlylinear and upper bounds which are exponential in the problem size.Reliable and secure cyber-physical systems which involve communi-cation, computation and control present a new set of challenges inthis area.

Future Plans

We plan to continue research in information theory and its applica-tions in the areas mentioned above as well as in areas such as ma-chine learning, statistics, data privacy, and life sciences. We hope todo this through hiring and by strengthening existing collaborationswith researchers at IIT Bombay. There is also a large scope for col-laborations with both the applied probability and complexity theorygroups within the school and the departments of theoretical physicsand biology in the institute.

Research Highlights: Vinod Prabhakaran

Claude Shannon established the field of information theory in 1948

while mathematically formulating the question of communicating asource (which models, for example, the output of a microphone ora video camera) over a noisy channel (e.g., the wireless channel).This point-to-point problem is now thoroughly understood from atheoretical point-of-view. The insights from this theoretical under-standing provide the underpinnings for the communication systemsof today. Our applications, however, have moved on from this simplesetting. We have networks of various devices/entities interacting in-advertently or otherwise, e.g., networks of wireless sensors, or socialnetworks of people. And in networks we may want to do more than

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just communicate. For instance, in many sensor networks, the objec-tive is often to carry out a computation on the data collected by thesensors; an individual sensor’s observation may be of little impor-tance. A very important challenge for information theory today isto formulate and answer fundamental questions on communicationand computation in networks which will lead to efficient architec-tures for the network applications of today and the future.

The discussion up till now did not pay much attention to who(else) learns what (else) in the process of accomplishing the commu-nication/computation task. Understanding how the communicationand computation in networks can be performed if there is an ad-ditional requirement of secrecy forms an important field of study.What additional resources are required to guarantee secrecy? Whatchanges to the architecture would this entail?

Modern cryptography is another field founded by Shannon whodiscovered the correct mathematical formulation for secret communi-cation (how two trusted parties can exchange a secret message whilekeeping out an untrusted eavesdropper). Subsequent work by cryp-tographers employing ideas from theoretical computer science andinformation theory have all but resolved this setting where trustedparties want to collaborate while keeping untrusted adversaries inthe dark and foiling their attempts at disrupting the collaboration. Amajor challenge in cryptography is efficiently enabling collaborationbetween mutually distrusting parties while preserving their privacy(e.g., private auctions, or e-voting in a social network without reveal-ing ones preferences publicly).

This is the context for my research. Below I will briefly describesome of the threads I have been pursuing over the last five-six yearssince joining TIFR and plan to pursue in the near future. In theinterest of space, the description below is brief and the backgroundis only lightly sketched. I invite the reader to peruse my papers formore details and background.

Secure multiparty computation: The goal of secure multiparty com-putation is to carry out computations on inputs distributed amongtwo or more parties, so as to provide each of them with no moreinformation than what their respective inputs and outputs reveal tothem. This models several practical settings where mutually dis-trusting parties need to collaborate, e.g., private auctions, privacy-preserving data mining, electronic voting, to name a few. The fieldwas founded by the pioneering work of several researchers includ-ing Shamir, Rivest, Adleman (1981), Blum (1981), Rabin (1981), andYao (1982). While the initial results relied on computational assump-tions, information theoretically secure schemes were obtained subse-quently starting with seminal works of Ben-Or, Goldwasser, Widger-son (BGW, 1988) and Chaum, Crépeau, Damgård (CCD, 1988).

Much of the effort has been on the question of feasibility – nec-essary and sufficient conditions on the types of resources availableat the parties in order to make a desired computation securely fea-

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sible. For instance, the BGW and CCD results assert that informa-tion theoretically secure computation of any function is feasible bya set of users if the fraction of colluders is less than 1/2 (for the socalled honest-but-curious case where the users do not deviate fromthe protocol; the threshold is 1/3 for the malicious model) providedthe users are fully connected by a network of private pairwise links.However, even for small networks, the amount of communicationrequired (i.e., communication complexity of secure computation) re-mains largely open. In recent work [20, 6], we provided the firstnon-trivial lower bounds and certain characterisations for communi-cation complexity in a three-party network. The results were builton several new information theoretic tools which, we believe, couldbe of more general interest. Indeed, our current efforts are on devel-oping a general lower bound for secure computation over networksanalogous to the cut-set bounds in network information theory. Inanother ongoing work, we have characterised which networks al-low secure computation, generalising the result of BGW and CCD tocommunication networks where not every pair of users may have acommunication link between them.

For information theoretically secure computation involving onlytwo parties (or more generally, where any number of parties may becorrupt), it is well known that, for all but a small set of functions, theparties must start out with a setup of correlated observations, e.g.,the input and output of a noisy channel between them (Kilian, 2000,for instance). However, it remains open how efficiently such stochas-tic resources can be turned into secure computation, in other words,what is the secure computation capacity of channels and correlatedsources. Based on a generalisation of the concept of common infor-mation, we developed state-of-the-art upper bounds on the rate ofsecure two-party sampling from distributed sources [11] and chan-nels [19]. One of our ongoing projects involves generalising the no-tions from above to study secure computation in noisy networks,where even feasibility results are unclear.

Physical-layer security: Starting with the pioneering work of Wyner(1975) on wiretap channels, it has been well recognised that stochas-tic resources (in the form of channels and correlated sources) avail-able at the communicating parties (and potentially at eavesdropperstoo) can be exploited to achieve secure communication and com-putation between collaborating parties without leaking informationto adversaries. The challenge is to devise protocols which makegood/optimal use of these resources.

In a series of works [7, 8, 9, 10], we explored the role of feed-back in secure communication over wireless networks. In fact, thepapers studied the more tractable erasure networks which, althoughonly a caricature of general networks, still capture the challenges andopportunities presented by a wireless network environment (broad-casting, multipath, channel variability, feedback). In a number ofcases we were able to derive secure message capacity characterisa-

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tions which provide engineering rules-of-thumb for real-life wirelessnetworks. Our results show that, in a large number of settings, it isoptimal to operate in a two-phase approach involving a secret keygeneration phase followed by a secure communication phase whichmakes use of the key generated in the first phase along with thechannel stochastic resources for security. We also developed a linearprogramming formulation to obtain the optimal parameter valuesfor the schemes.

In a separate set of works [4], we studied the secure computa-tion (specifically, oblivious transfer) capacity of the erasure wire-tap/broadcast channel.

Communication in the presence of jamming: We obtained capacity re-sults for communication over state dependent channels in the pres-ence of a jamming adversary [3]. Such channels arise in informationhiding (e.g., watermarking) and communication in the presence ofinterference. The technical contributions include a refined Markovlemma which may be of more general interest, especially, in ad-versarial settings. Our current efforts are on better understandingmulti-hop/relay-based communication and distributed source cod-ing in adversarial settings [16].

Plausible deniability: A notion of security related to, but differentfrom, security is that of plausible deniability. Here the aim is toprotect the identity of a user, e.g., a whistle-blower, after the actof communication. A third-party (e.g., an employer attempting tofind the source of a leak), who may have side-information about thecommunication, might require the communicating party/parties toreveal what they communicated. The goal is to design a commu-nication scheme where the parties have some flexibility to claim tohave sent an innocuous message without being detected. In [1], weformulate in information theoretic terms the problem of plausiblydeniable communication over a (broadcast) channel and characteriseits capacity. Future work includes studying the interplay of plausibledeniability and covert communication (where the goal is to hide thevery fact that communication ever took place).

Interactive computation: Finding good lower bounds on the mini-mum amount of communication required by two parties to computea function of their inputs (communication complexity) is a classi-cal problem in computer science and information theory. We in-troduced a new lower bound, called Rényi information complexity,which (specifically, the order infinity version) has the two leadinglower-bounds as its natural relaxations: (external) information com-plexity and logarithm of partition complexity which have so far ap-peared conceptually quite different from each other [17]. Further un-derstanding Rényi information complexity (of various orders) mighthave consequences for important direct-sum problems in communi-cation complexity, as it lies between communication complexity and

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information complexity.

Broadcast channels: Broadcast channels remain one of the most chal-lenging open problems in network information theory. We madeprogress in two different settings:In the degraded message set broadcast problem, the objective is fora transmitter to encode nested sets of streams of data so that dif-ferent receivers may receive, depending on their channel quality, allstreams belonging to a certain level of nesting. Such nested forms ofdata arise in practice, for example, in video coding where the lowestresolution data will be present in all sets while increasingly higherresolutions of data are present only in outer levels of nesting. Weresolved this problem in a special case of the combination networksetting and gave a novel coding scheme for the general problem [5].We also showed that the rate regions achieved by the recently pro-posed indirect decoding scheme for problems in network informa-tion theory are generally the same as that of the well known (andpotentially less involved) joint decoding scheme [12].It is well known that feedback from the receivers to the transmittermay improve the capacity region of broadcast channels. It was be-lieved that in all except the physically degraded case, feedback ofany form leads to an enlargement of the capacity region of the addi-tive memoryless Gaussian noise broadcast channel. In [18] we provethat for a class of non-degraded broadcast channels, noisy feedbackwith noise variance above a threshold does not result in such an en-largement.

Other works: Distributed Generation of Correlation: A fundamentalproblem with a wide range of applications is that of generating cor-related random variables in a distributed setting. This problem hasrecently attracted some attention in information theory literature. Westudied a distributed sampling scenario in which two agents observ-ing components of a correlated source must each generate compo-nents of a second correlated source aided by an “omniscient” thirdterminal which observes the two input sources and transmits rate-limited messages to assist the terminals in generating the requiredcorrelation in their outputs [21, 14].

Access Controlled Content Delivery Networks and Secure Index Coding:In content delivery networks, different users request, from a centralserver, content (specific files) they are interested in. The users mayhave caches where some part of the whole database of files may becached. Coded caching has been demonstrated to have significantbenefits in such content delivery networks. In [2], we studied accesscontrolled coded caching where we impose the additional constraintthat a user should not be able to learn anything, from either whatis stored in their caches or the server transmissions, about the filesthey did not request. We proposed a feasible scheme for this settingand proved its order-optimality. In ongoing work, we are studyinga similar notion of security in the index coding problem which has

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been shown to be of fundamental importance [15].Sampling Using Noisy Low-precision ADCs: Sensor networks present

various technological challenges arising mainly from the typicallylow cost of the devices and noisy environments where they needto operate. A particular crucial element of any sensor network isdata acquisition. In our work we studied sampling using (cheap)low-precision analog-to-digital converters in the presence of noise.Specifically, we considered (over)sampling, quantisation, and esti-mation of a bounded dynamic-range bandlimited signal affected byadditive independent Gaussian noise [22].

References[1] M. Bakshi and V. Prabhakaran, “Plausible Deniability over Broadcast Channels,”

IEEE Transactions on Information Theory, accepted, April 2018.

[2] V. Ravindrakumar, P. Panda, N. Karamchandani, and V. Prabhakaran, “PrivateCoded Caching,” IEEE Transactions on Information Forensics and Security, vol. 13,no. 3, pp. 685-694, March 2018.

[3] A. Budkuley, B. Dey, and V. Prabhakaran, “Communication in the Presence of aState-Aware Adversary,” IEEE Transactions on Information Theory, vol. 63, no. 11,pp. 7396-7419, November 2017.

[4] M. Mishra, B. Dey, V. Prabhakaran and S. Diggavi, “Wiretapped Oblivious Trans-fer,” IEEE Transactions on Information Theory, vol. 63, no. 4, pp. 2560-2595, April2017.

[5] S. Bidokhti, V. Prabhakaran, and S. Diggavi, “Capacity Results for MulticastingNested Message Sets Over Combination Networks,” IEEE Transactions on Infor-mation Theory, vol. 62, no. 9, pp. 4968-4992, September 2016.

[6] D. Data, V. Prabhakaran, and M. Prabhakaran, “Communication and Random-ness Lower Bounds for Secure Computation,” IEEE Transactions on InformationTheory, vol. 62, no. 7, pp. 3901–3929, July 2016.

[7] L. Czap, V. Prabhakaran, C. Fragouli, and S. Diggavi, “An LP Characterization ofthe Secret-Message Capacity of Three Erasure Networks with Feedback,” IEEETransactions on Information Theory, vol. 62, no. 5, pp. 2430–2480, May 2016.

[8] C. Fragouli, V. Prabhakaran, L. Czap, and S. Diggavi, “Wireless Network Secu-rity: Building on Erasures,” Proceedings of the IEEE, vol. 103, no. 10, pp. 1826–1840, October 2015.

[9] L. Czap, V. Prabhakaran, C. Fragouli, and S. Diggavi, “Secret Communicationover Broadcast Erasure Channels with State-feedback,” IEEE Transactions on In-formation Theory, vol. 61, no. 9, pp. 4788–4808, September 2015.

[10] L. Czap, C. Fragouli, V. Prabhakaran, and S. Diggavi, “Secure Network CodingWith Erasures and Feedback,” IEEE Transactions on Information Theory, vol. 61,no. 4, pp. 1667–1686, April 2015.

[11] V. Prabhakaran and M. Prabhakaran, “Assisted Common Information with anApplication to Secure Two-Party Sampling,” IEEE Transactions on Information The-ory, vol. 60, no. 6, pp. 3413–3434, June 2014.

[12] S. Bidokhti and V. Prabhakaran, “Is Non-unique Decoding Necessary?,” IEEETransactions on Information Theory, vol. 60, no. 5, pp. 2594–2610, May 2014.

[13] V. Narayanan and V. Prabhakaran, “Secure Computation in Incomplete Net-works,” submitted, 2018.

[14] G. Kurri, V. Prabhakaran, and A. Sarwate, “Coordination Using IndividuallyShared Randomness,” to be presented at IEEE International Symposium on Infor-mation Theory (ISIT), Vail, 2018.

[15] V. Narayanan. J. Ravi, V. Mishra, B. Dey, N. Karamchandani, and V. Prabhakaran,“Private Index Coding,” to be presented at IEEE International Symposium on In-formation Theory (ISIT), Vail, 2018.

[16] A. Budkuley, B. Dey, and V. Prabhakaran, “Coding for Arbitrarily Varying Re-mote Sources,” in Proc. IEEE International Symposium on Information Theory (ISIT),Aachen, 2017, pp. 729-733.

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research highlights: pranab sen

[17] M. Prabhakaran and V. Prabhakaran, “Rényi Information Complexity and anInformation Theoretic Characterization of the Partition Bound,” in Proc. 43rdInternational Colloquium on Automata, Languages and Programming (ICALP), Rome,2016, vol. 55, pp. 88:1-88:14.

[18] S. Pillai and V. Prabhakaran, “On the Noisy Feedback Capacity of GaussianBroadcast Channels,” in Proc. IEEE Information Theory Workshop (ITW), Jerusalem,2015, doi: 10.1109/ITW.2015.7133117.

[19] S. Rao and V. Prabhakaran, “A New Upperbound for the Oblivious TransferCapacity of Discrete Memoryless Channels,” in Proc. IEEE Information TheoryWorkshop (ITW), Hobart, 2014, pp. 35-39.

[20] D. Data, M. Prabhakaran, and V. Prabhakaran, “On the Communication Com-plexity of Secure Computation,” Advances in Cryptology – CRYPTO 2014: 34thAnnual Cryptology Conference, Santa Barbara, CA, USA, August 17-21, 2014, Pro-ceedings, Part II, pp. 199-216.

[21] V. Prabhakaran and A. Sarwate, “Assisted Sampling of Correlated Sources,”inProc. IEEE Symposium on Information Theory (ISIT), Istanbul, 2013, pp. 3155-3159.

[22] A. Kumar and V. Prabhakaran, “Estimation of Bandlimited Signals from theSigns of Noisy Samples,” in Proc. IEEE International Conference on Acoustics,Speech, and Signal Processing (ICASSP), Vancouver, 2013, pp. 5815-5819.

[23] M. Prabhakaran and V. Prabhakaran, “On Secure Multiparty Sampling For Morethan Two Parties,” in Proc. IEEE Information Theory Workshop (ITW), Lausanne,2012, pp. 99-103.

Research Highlights: Pranab Sen

One shot private capacity of the quantum wiretap channel

In a quantum wiretap channel, Alice encodes her classical message asa quantum state and inputs it to the channel, whose output is a quan-tum state jointly shared between Bob and Eve. The requirement isthat Bob should be able to decode Alice’s message with low error andEve should be able to get only negligible information about Alice’smessage. The maximum rate of sending messages under these con-straints is called the private capacity. Pranab Sen and his coauthorsobtained nearly matching upper and lower bounds on the one shotprivate capacity in terms of the hypothesis testing and smooth maxmutual information quantities. Their results subsume the known re-sults in the quantum asymptotic IID case and the classical asymptoticIID and non-IID (information spectrum) cases. The main technicalcontribution of their work is a one shot classical quantum coveringlemma which should find other applications to quantum Shannontheory. In order to prove their covering lemma, they had to developa novel operator Chernoff bound for non-square matrices. (QCRYPT2017, J. Radhakrishnan, P. Sen, N. Warsi).

One shot Marton inner bound for quantum broadcast channel

In a quantum broadcast channel without common message, Aliceencodes her pair of classical messages as a quantum state and inputsit to the channel, whose output is a quantum state jointly sharedbetween Bob and Charlie. The requirement is that Bob and Charlieshould be able to decode the messages intended for them with lowerror. Generalising a seminal inner bound for this problem in theclassical asymptotic IID setting by Marton (1979), Pranab Sen and

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his coauthors obtain the natural analogous inner bounds in the oneshot classical and quantum settings. In fact in the one shot classicalsetting, they are able to further extend their inner bound to the nat-ural analogue of Marton’s bound with common message. The maintechnical contributions of their work include a classical one shot mu-tual covering lemma based on rejection sampling and a finer anal-ysis of Marton’s arguments that allows them to decode the actualpair of strings ‘inputted’ to the channel as opposed to earlier workthat could only determine the band containing the message. (IEEETrans. Inf. Theory, 2016, J. Radhakrishnan, P. Sen, N. Warsi).

Han-Kobayashi inner bound for quantum interference channel

In a quantum interference channel, Alice and Bob encode their re-spective classical messages independently into quantum states andinput them to the channel, whose output is a quantum state jointlyshared between Charlie and David. The requirement is that Char-lie, David should be able to decode Alice’s, respectively Bob’s, mes-sages with low error. A seminal work by Han and Kobayashi (1981)gave the best inner bound for this channel in the classical asymp-totic IID setting. Pranab Sen showed for the first time how onecould get the same inner bound in the quantum asymptotic IIDsetting. He did so by constructing the first simultaneous decodersfor a two-sender and a restricted three-sender quantum multiple ac-cess channel, and showing how to use them as subroutines to ob-tain the Chong-Motani-Garg inner bound, known to be equivalent tothe Han-Kobayashi inner bound, for an interference channel. Othertechnical advances in his work include a proof that sequential de-coding achieves the standard rates for a quantum channel, a non-commutative union bound for POVM elements to analyse decodingerror probability and a geometric notion of approximate intersectionof two subspaces. (ISIT 2012, P. Sen).

Connection between smooth max relative entropy and von Neumannrelative entropy

Pranab Sen and his coauthors proved a fundamental result calledsubstate theorem upper bounding the smooth max relative entropyof a pair of quantum states in terms of the von Neumann relative en-tropy (also known as Kullback-Leibler divergence). Using this sub-state theorem, they proved privacy trade-offs between Alice and Bobfor quantum communication protocols solving the set membershipproblem. (JACM 2009, FOCS 2002, R. Jain, J. Radhakrishnan, P. Sen).

Future work

Future work envisaged in quantum information theory includes in-vestigating the capacity of quantum multi-terminal channels in theone shot setting. To this end, fundamental technical work will needto be done in order to obtain robust notions of union and intersec-

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tion of projectors. New packing and covering arguments suitable forthe one shot setting also need to be developed. Other directions ofresearch will be to address efficiency issues in encoding and decod-ing, derandomisation issues in superadditivity of channel capacities,decoupling arguments etc.

Research Highlights: Rahul Vaze

How to Use Multiple Antennas in Wireless Networks

The transmission capacity of an ad-hoc network is the maximumdensity of active transmitters in an unit area, given an outage con-straint at each receiver for a fixed rate of transmission. This workderives bounds on the transmission capacity of an ad-hoc networkwhen each node is equipped with multiple antennas.

For a single transmitter-receiver pair, when the receiver does notexperience any interference from any other transmitter, employingmultiple antennas at both the transmitter and the receiver either lin-early increases the capacity, or exponentially decreases the error ratewith SNR. In contrast, in a wireless network, where interference isthe performance limiter, finding how to best use the multiple an-tennas is a rather complicated issue. The problem is challengingbecause in the presence of interference, multiple antennas have dualroles at both the transmitter and the receiver side. On the transmit-ter side, multiple antennas can be used to beamform the signal to-wards the intended receiver or to suppress transmission (construedas interference) towards other receivers. Similarly, on the receiverside, each receiver can use its multiple antennas to improve the SNRfrom its intended transmitter or cancel the interference coming fromother transmitters. To further compound the problem, the roles ofmultiple antennas at both the transmitter and the receiver side areinter-dependent on each other.

In his work, he has shown that employing multiple antennas atall nodes, the transmission capacity scales at least linearly with thenumber of antennas, and sending only data stream from each trans-mitter achieves the linear scaling of the transmission capacity in bothcases. Thus, the non-trivial role of the multiple antennas is at the re-ceiver end, where they are used to reduce the interference.

Capacity of Wireless Networks

Earlier definitions of capacity for wireless networks, e.g., transport ortransmission capacity, for which exact theoretical results are known,are well suited for ad hoc networks but are not directly applicablefor cellular wireless networks that are structured. Rahul defined anew capacity metric that is relevant for practical wireless networksas the product of the number of base stations and the reciprocal ofthe expected delay (number of re-transmissions needed till success)experienced at any mobile user. Using tools from stochastic geome-

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try, surprisingly, he has able to give an exact characterisation of thisnew capacity metric. The analysis is non-trivial because of temporalcorrelations of interference seen at any receiver across time. The ca-pacity is shown to first increase polynomially with the BS density inthe low BS density regime and then scale inverse exponentially withthe increasing BS density. The exponential decrease is contrary tothe behavior of other similar capacity metrics like transport capacity,however, is a manifestation of tremendous growth of interference asthe base station density is increased beyond a point. A simple powercontrol strategy that is a function of the distance between the basestation and the mobile node is shown to achieve the capacity.

Energy Harvesting Communication

The idea of using renewable sources of energy to power commu-nication devices has been proposed to increase the lifetime of sen-sor networks, improve energy efficiency of low power devices, andalso provide a means for green communication. Harvesting energyfrom natural sources, however, makes the future available energyunpredictable. Typically, the energy arrival process is time vary-ing and energy is acquired from many disparate sources, such assolar, wind, radio waves, human workouts, etc., making it hard tomodel/estimate the energy arrival distribution. Consequently, as-suming energy arrivals to be non-ergodic is a natural choice whicheliminates the effects of any modelling/estimation errors. The mainchallenge in extending classical results under the average power con-straint to the energy harvesting (EH) scenario, is the strict energyneutrality constraint, i.e., the amount of energy spent by any timehas to be less than the total energy harvested. The energy neutralityconstraint makes the problem fundamentally hard.

Most of the prior work on algorithm design for green communica-tion considers an offline scenario, where all the future energy arrivalsare known in advance, that allows for easier analytical tractability,even though it is still non-trivial. In his work, Rahul has primarilyfocused on online algorithms for green communication, where theenergy arrival process is assumed to be non-ergodic, and can evenbe generated by an adversary. Both wireline and wireless channelmodel have been considered, and he has been able to derive opti-mal algorithms. Most of the prior work on EH has concentrated onthe case when only the transmitter is powered by EH device. In apractical setup, both the transmitter and the receiver are expected tobe driven by EH, however, in this case the problem is fundamentallydifferent and challenging because of it being a special case of stochas-tic control problem with asymmetric information. He has been ableto characterise optimal online algorithms when both the transmitterand the receiver are powered by EH assuming perfect coordinationbetween the transmitter and the receiver about the battery state un-der the wireline communication model. In recent work, even the re-striction of coordination has been removed and near-optimal online

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algorithm has been derived that requires only a vanishing amount offeedback about battery states.

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Teaching

Ph.D. programme

Admissions

Admissions are based on a nationwide entrance exam followed by interviews. Students with back-grounds in Computer Science, Electrical Engineering, Mathematics and related areas are eligible to takethe exam. The school offers admission to around 5–8 students yearly.

Timeline

Usually, students take a mix of basic and advanced courses in their first two semesters after which theydo a 6-month exploratory research project with a member of the faculty. Students also clear a Ph.D.qualifier exam by the end of their third semester. More advanced courses are available for the studentsthroughout the duration of their Ph.D. Typical length of the Ph.D. programme is 5–6 years from joining.Students who complete the requirements for a master’s degree are also awarded a master’s degree alongwith their Ph.D. degree. Theses are sent out to two external examiners. Based on their recommendationand a successful public defense, the Ph.D. degree is awarded by TIFR.

Year 1

Courses

Year 2 Year 3-4

Qualifiers and Ph.D. registration

Year 5

Ph.D. public defence

Year 6

Annual student review

Synopsis seminar and thesis submission

Project

Ph.D. degree

teaching

Courses offered

August – December 2008

• Probability Theory, (O. Dabeer)

• Design and Analysis of Algorithms, (S.K. Ghosh)

• Optimization, (S.K. Juneja)

• Automata and Computability, (P.K. Pandya)

• Mathematical Structures, (J. Radhakrishnan)

• Computer Science Logic, (N. Raja)

• Information Theory, (N. Sharma)

January – May 2009

• Digital Communications, (O. Dabeer)

• Wireless Networks (Reading Course), (O. Dabeer)

• Mathematical Finance, (S.K. Juneja)

• Derandomization, (J. Radhakrishnan)

• Computer Science Logic, (N. Raja)

• Introduction to Quantum Information Processing, (N. Sharma)


• Design and Analysis of Algorithms, (S.K. Ghosh)

• Graduate Seminar in Algorithms and Complexity, (M. Gopalkrishnan)

• Probability Theory, (S.K. Juneja)

• Computational Finance (Reading Course), (S.K. Juneja)

• Space Bounded Computations, (N. Limaye)

• Information Theory, (J. Radhakrishnan)

• Introduction to Logic, (N. Raja)

• Mathematical Structures, (N. Sharma)


• Optimization, (V.S. Borkar)

• Detection and Estimation Theory, ( O. Dabeer)

• Approximation Algorithms, (S.K. Ghosh)

• Limit of Approximability: PCPs and Unique Games, (P. Harsha)


• Space Bounded Computations, (N. Limaye)

• Automata and Computability: Theory and Practice, (P.K. Pandya)

• Information Theory, (J. Radhakrishnan)

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ph.d. programme

• Introduction to Logic, (N. Raja)

• Mathematical Structures, (N. Sharma)

• Multiple Antenna Communication, (R. Vaze)

• Multi-User Information Theory (Reading Course), (R. Vaze)


• Special Topics in Signal Processing (Reading Course), (V.S. Borkar)

• Geometric Graphs and Algorithms, (S.K. Ghosh)

• Some Topics in Stochastic Processes (Reading Course), (S.K. Juneja)

• Selected Topics in Algorithms, (T. Kavitha)


• Introduction to Probability, (J. Radhakrishnan)

• Formal Logic, (N. Raja)


• Information Theory, (R. Vaze)


• Optimization, (V.S. Borkar)

• Computational Geometry, (S.K. Ghosh)

• Computational Complexity, (P. Harsha)

• Probability and Stochastic Processes, (S.K. Juneja)

• Large Deviations Theory, Heavy Tailed Processes and Rare Simulation (Reading Course), (S.K.Juneja)

• Mathematical Foundations for Computer Science, (J. Radhakrishnan)

• Mathematical Foundations for Systems Science, (N. Sharma)


• Mathematical Structures for Systems Science, (O. Dabeer/M. Gopalkrishnan)

• Mathematical Foundations of Computer Science, (M. Gopalkrishnan/J. Radhakrishnan)

• Communication Complexity , (P. Harsha/J. Radhakrishnan)

• Stochastic Finance, (S.K. Juneja)

• Topics in Applied Probability (Reading Course), (S.K. Juneja)

• Algorithms and Data Structures, (T. Kavitha)


• Introduction to Probability Theory, (V. Prabhakaran)

• Topics in Linear Programming Convexity and Metric Embedding (Reading Course), (J. Radhakr-ishnan)

• Introduction to Symbolic Logic, (N. Raja)

• Information Theory, (N. Sharma)

• Machine Learning, (R. Vaze)

145

teaching


• Detection and Estimation Theory, (O. Dabeer)

• Algebra and Computation, (R. Gandhi)

• Modelling Cognitive Functions Using Neuroids, (M. Gopalkrishnan)

• Application of Computer Science to Other Sciences, (M. Gopalkrishnan)


• Combinatorial Optimisation, (T. Kavitha, J. Radhakrishnan)

• Network Information Theory, (V. Prabhakaran)


• Computational Biology, (A.V.Tendulkar)


• Mining of Massive Datasets (Reading Course), (O. Dabeer/V. Prabhakaran)

• Mathematical Structures for Systems Science, (S.K. Juneja)


• Information Theory, (V. Prabhakaran)

• Information Theory and Cryptography (Reading Course), (V. Prabhakaran)


• Algorithms and Data Structures, (J. Radhakrishnan)

• Elements of Mathematical Logic, (N. Raja)

• Introduction to Probability, (P.G.D. Sen)

• Machine Learning, (R. Vaze)


• Detection and Estimation Theory, (O. Dabeer)

• The Stochastic Thermodynamics of Computation, (M. Gopalkrishnan)

• Computational Geometry, (S.K. Ghosh)


• Optimization and Game Theory, (S.K. Juneja)

• Foundations of Program Verification: Theory and Practice, (P.K. Pandya)

• Digital Communication, (V. Prabhakaran)

• Coding Theory (Reading Course) (V. Prabhakaran)


• Boolean Circuit Complexity, (A. Chattopadhyay)

• Systemic Risk and Random Graphs (Reading Course), (S.K. Juneja)



• Foundations of Program Verification, (P.K. Pandya)

146

ph.d. programme

• Introduction to Probability, (V. Prabhakaran)

• Topics in Network Coding and Network Information Theory (Reading Course), (V. Prabhakaran)

• Mathematical Foundations for Computer Science (Linear Algebra), (J. Radhakrishnan/P.G.D. Sen)

• Algorithms at IISER, Pune, (J. Radhakrishnan/P.G.D. Sen)

• Outline of Logic, (N. Raja)

• Recursive Function Theory (Reading Course), (N. Raja)

• Mathematical Foundations for Systems Science (Linear Algebra), (N. Sharma/P.G.D. Sen)

• Information Theory, (R. Vaze)

• Stochastic Geometry: Applications to Wireless Networks (Reading Course), (R. Vaze)


• Approximation Algorithms for Graph and Geometric Problems, (S.K. Ghosh)


• Approximation Algorithms Using SDP Hierarchies (Reading Course), (P. Harsha)

• Advanced Probability, (S.K. Juneja)

• Topics in Combinatorial Optimization, (T. Kavitha)

• Foundations of Program Verification, (P.K. Pandya)

• Network Information Theory, (V. Prabhakaran)

• Semantics of Computation, (N. Raja)

• Quantum Computation and Information, (P.G.D. Sen)

• Wireless Communication, (R. Vaze)


• Boolean Circuit Complexity, (A. Chattopadhyay)

• Mathematical Foundations for Computer Science, (M. Gopalkrishnan)

• Probability and Computing, (P. Harsha)

• Recent Results in Theoretical Computer Science (Reading Course), (P. Harsha)

• Monte Carlo Methods and Rare Events, (S.K. Juneja)


• Automata and Computability, (P.K. Pandya/J. Radhakrishnan)

• Outline of Logic, (N. Raja)

• Introduction to Term Rewriting (Reading Course), (N. Raja)

• Topics in Quantum Information Theory, (N. Sharma)


• Fourier Analysis and Circuit Complexity (Reading Course), (A. Chattopadhyay)

• Abstract Nonsense and its Application, (M. Gopalkrishnan)

• Verification: Theory and Practice, (A. Gupta/P.K. Pandya)

• PCPs and Limits of Approximation, (P. Harsha)

147

teaching

• Financial Mathematics, (S.K. Juneja)

• Advanced Information Theory (at IIT Bombay), (V. Prabhakaran)

• Computational Complexity, (P.G.D. Sen)


• Algorithmic Game Theory, (U. Bhaskar)

• Communication Complexity, (A. Chattopadhyay)

• Mathematical Logic, (A. Gupta)

• Mathematical Structures for Computer Science, (P. Harsha/J. Radhakrishnan)

• Mathematical Structures in Systems Sciences (Real Analysis), (S.K. Juneja)



• Network Information Theory, IIT Bombay, (co-taught with Prof. Nikhil Karamchandani), (V. Prab-hakaran)

• Probability, (V. Prabhakaran)

• Foundational Models for Computing, (N. Raja)

• Communications, (R. Vaze)


• Linear Programming and Approximation Algorithms, (U. Bhaskar)

• Computational Complexity, (A. Chattopadhyay/P. Kurur)

• Automated Reasoning and Program Verification, (A. Gupta)

• Expander Graphs: Constructions and Applications, (P. Harsha, co-taught with Anish Ghosh)

• Advanced Probability, (S.K. Juneja)



• Online Algorithms, (R. Vaze)


• Algorithmic Game Theory (Reading Course), (U. Bhaskar)

• Automata, (A. Chattopadhyay)

• Communication Complexity, (A. Chattopadhyay)

• Mathematical Logic, (A. Gupta)

• A mini course on Coding Theory, (P. Harsha)

• Mathematical Structures in Systems Sciences, (S.K. Juneja)

• Stochastic Calculus and Mathematical Finance (Reading Course), (S.K. Juneja)


• Advanced Information Theory, (V. Prabhakaran)

• Mathematical Structures, (J. Radhakrishnan)

• Concurrency Theory, (N. Raja)

148

ph.d. programme

• Probability Theory, (P.G.D. Sen)


• Computational Complexity, (A. Chattopadhyay)


• Convex Optimization, (V. Prabhakaran)

• Randomness and Computation (Reading Course), (J. Radhakrishnan)


• Distributed Algorithms (Reading Course), (N. Raja)

• Algebra and Computation, (R. Saptharishi)

• Automata Theory Using Algebra and Logic (Reading Course), (A. Chattopadhyay and P.K.Pandya)

• Polynomial Methods in Combinatorics (Reading Course), (R. Saptharishi and J. Radhakrishnan)

• Information Theory, (P.G.D. Sen)

• Quantum Computation and Information, (P.G.D. Sen)

• Online Algorithms, (R. Vaze)


• Algorithms, (U. Bhaskar)

• Game Theory (Reading Course), (U. Bhaskar)

• Analysis of Boolean Functions, (P. Harsha)

• Game Theory (Reading Course jointly with U. Bhaskar), (S.K. Juneja)

• High Dimensional Geometry, (H. Narayanan)

• Automata, (P.K. Pandya)

• Probability, (V. Prabhakaran)


• Interactive Proof Check, (N. Raja)

• Algebraic Circuit Complexity, (R. Saptharishi)

• Analysis, (P.G.D. Sen)

• Analysis of Markov Chains, (P. Srivastava)

• Learning Theory, (R. Vaze)


• Concrete Lower Bounds, (A. Chattopadhyay)


• Probabilistically Checkable Proofs (Reading Course), (P. Harsha)

• Advanced Probability Including Large Deviations and Stochastic Calculus, (S.K. Juneja)

• Combinatorial Optimization, (T. Kavitha/U. Bhaskar)


• Privacy (Reading Course), (V. Prabhakaran)

149

teaching

• Quantum Computation and Information, (J. Radhakrishnan)

• Ideals, Varieties and Algorithms (Reading Course), (R. Saptharishi)

• Numerical Algorithms, (P. Srivastava)

150

Publications from the last ten years

Items marked with ‘?’ are from theperiod before the members involvedjoined TIFR.

In 2008-09

Journals

1. Agarwal M., Borkar V.S. and Karandikar A., Structural Properties of Optimal Transmission Policies Overa Randomly Varying Channel, IEEE Transactions on Automatic Control, 53(6), pp. 1476-1491, 2008.

2. Bassamboo A., Juneja S.K. and Zeevi A., Portfolio Credit Risk with Extremal Dependence, OperationsResearch, 56(3), pp. 593-606, 2008.

3. Basu A. and Borkar V.S., Stochastic Control with Imperfect Models, SIAM Journal of Control and Opti-mization, 47(3), 1274-1300, 2008.

4. Basu A., Bhattacharya T. and Borkar V.S., A Learning Algorithm for Risk-sensitive Cost, Mathematics ofOperations Research, 33(4), pp. 880-898, 2008.

?5. Bhaskar U., Fleischer L., Darrell H., Hoy D. and Huang C-C., Equilibria of Atomic Flow Games are notUnique, Symposium on Discrete Algorithms (SODA), pp. 748-757, 2009.

6. Barman K. and Borkar V.S., A Note on Linear Function Approximation Using Random Projections, Systemsand Control Letters, 57(9), pp. 784-786, 2008.

7. Bhattacharjee A.K. and Shyamasundar R.K., Activity Diagrams: A Formal Framework to Model BusinessProcesses and Code Generation, Journal of Object Technology, 8(1), pp. 189-220, 2009.

8. Borkar V.S. and Manjunath D., Distributed Topology Control of Wireless Networks, Wireless Networks15, pp. 1022-1038, 2008.

9. Borkar V.S., Cooperative Dynamics and Wardrop Equilibria, Systems and Control Letters, 58(2), pp. 91-93, 2009.

10. Borkar V.S., Pinto J. and Prabhu T., A New Learning Algorithm for Optimal Stopping, Discrete EventDynamical Systems, 19(1), pp. 91-113, 2009.

11. Borkar V.S. and Das D.J., A Novel ACO Scheme for Emergent Optimization via Reinforcement and InitialBias, Swarm Intelligence 3(1), pp. 3-34, 2009.

12. Chaudhury B.B., Das C.R. and Sen A., A Phonetic Dictionary and Grapheme-phoneme Rule Based BanglaTTS system, International Journal of Computer Sciences and System Analysis, ISSN: 0973-7448, 2(1),pp. 29-36, 2008.

13. Dabeer O. and Masry E., Multivariate Signal Parameter Estimation Under Dependent Noise From 1-bitDithered Quantized Data, IEEE Transactions on Information Theory, 54(4), pp. 1637-1654, 2008.

14. Dabeer O., Improved Capacity and Grade-of-Service in 802.11-Type Cell with Frequency Binning, IEEETransactions on Wireless Communications, 7(11), pp. 4176-4184, 2008.

publications from the last ten years

15. Das Soumyendu, Das Subhendu, Bandyopadhyay B. and Sanyal S., Steganography and Steganalysis:Different Approaches, International Journal of Computers, Information Technology and Engineering(IJCITAE), 2(1), 2008.

16. Dey S., Abraham A., Bandyopadhyay B. and Sanyal S., Data Hiding Techniques Using Prime and NaturalNumbers, Journal of Digital Information Management, ISSN 0972-7272, 6(6), pp. 463-485, 2008.

17. Di Pietro R., Mancini L., Mei A., Panconesi A., Radhakrishnan J., Redoubtable Sensor Networks. ACMTrans. Inf. Syst. Secur. 11(3): 13:1-13:22, 2008.

18. Dutta C., Kanoria Y., Manjunath D., Radhakrishnan J., A tight lower bound for parity in noisy communi-cation networks, Symposium on Discrete Algorithms (SODA), pp 1056–1065, 2008.

19. Friedl K., Magniez F., Santha M. and Sen P.G.D., Quantum Testers for Hidden Group Properties, Funda-menta Informaticae, 91(2), pp. 325-340, 2009.

20. Ghosh S.K., Burdick J.W., Bhattacharya A. and Sarkar S., Online Algorithms with Discrete Visibility:Exploring Unknown Polygonal Environments, Special issue on Computational Geometry approaches inPath Planning, IEEE Robotics & Automation Magazine, 15(2), pp. 67-76, 2008.

?21. Hariharan R., Kavitha T. and Mehlhorn K., Faster Algorithms for Minimum Cycle Basis in DirectedGraphs, SIAM Journal on Computing, 38(4), pp. 1430-1447, 2008.

?22. Harsha P., Lachish O. and Matsliah A., Sound 3-query PCPPs are Long, ACM T. Computational Theory,1(2), pp.1-49, (Preliminary version in 35th ICALP, 2008), 2009.

23. Jagadish S., Chung L. and Shyamasundar R.K: cmUML Based Framework for Formal Specification ofConcurrent, Reactive Systems, Journal of Object Technology, 7(8), pp. 187-207, 2008.

24. Jiang Y., Ashikhmin A. and Sharma N., LDPC Codes for Flat Rayleigh Fading Channels with Channel SideInformation, IEEE Transactions on Communications, 56, pp. 1207-1213, 2008.

25. Juneja S.K. and Kalra H., Variance Reduction Techniques for Pricing American Options Using FunctionApproximations, The Journal of Computational Finance, 12(3), pp.79-102, 2009.

?26. Kavitha T. and Krishna K.V., An Improved Heuristic for Computing Short Integral Cycle Bases, ACMJournal on Experimental Algorithms, 13, 2008.

?27. Kavitha T., Mehlhorn K., Michail D. and Paluch K., An O(m2n) Algorithm for Minimum Cycle Basis ofGraphs, Algorithmica, 52(3), pp. 333-349, 2008.

28. Mei A., Panconesi A., Radhakrishnan J., Unassailable sensor networks, SecureComm 2008: 26.

?29. Prabhakaran V., Puri R. and Ramchandran K., Colored Gaussian Source-Channel Broadcast for Hetero-geneous (Analog/Digital) Receivers, IEEE Transactions on Information Theory, 54(4), pp. 1807-1814,2008.

30. Raja N., Interactive Theorem Proving and Verification, Sadhana, 34(1), 2009.

31. Salodkar N., Bhorkar A., Karandikar A. and Borkar V.S., An On-line Learning Algorithm for EnergyEfficient Delay Constrained Scheduling Over a Fading Channel, IEEE Journal on Selected Areas in Com-munications, 26(4), pp. 732-742, 2008.

32. Sen P.G.D. and Venkatesh S., Lower Bounds for Predecessor Searching in the Cell Probe Model, Journal ofComputer and System Sciences, 74(3), pp. 364-385, 2008.

33. Sharma N. and Ashikhmin A., Iterative Channel Estimation and Decoding for the Multi- path Channelswith RAKE Reception, IEEE Transactions on Communications, 56, pp. 1398-1403, 2008.

34. Sharma N., Extensions of the Quantum Fano Inequality, Physical Review A, 78(1), 012322, 2008.

152

in 2008-09

International Proceedings

1. Agarwal S., Barik R. and Shyamasundar R.K., A Static Characterization of Affinity in a Distributed Pro-gram, IEEE International Conference on High Performance Computing and Communication, Dalian,China, 2008.

2. Barman K. and Dabeer O., Improving Capacity in MIMO Systems with Asynchronous PAM, Proceedingsof International Symposium on Information Theory and its Applications, pp. 847-852, 2008.

?3. Ben-Sasson E., Harsha P., Lachish O. and Matsliah A., Sound 3-query PCPPs are Long, In LucaAceto, Ivan Damgard, Leslie Ann Goldberg, Magnús M. Halldórsson, Anna Ingólfsdóttir, and IgorWalukiewicz, editors, Proceedings of the 35th International Colloquium of Automata, Languages andProgramming (ICALP), Part I, 5125 of LNCS, pp. 686-697, Springer, 2008.

?4. Bhalgat A., Hariharan R., Kavitha T. and Panigrahi D., Fast Edge Splitting and Edmonds’ Arbores-cence Construction for Unweighted Graphs, Proceedings of the 19th Symposium on Discrete Algorithms(SODA), pp. 455-464, 2008.

5. Bhattacharjee A.K. and Shyamasundar R.K., ScriptOrc: A language to Model Service Choreography, IEEEAsia-Pacific Services Computing Conference (IEEE APSCC 2008), Yilan, Taiwan, 2008.

6. Bhattacharjee A.K. and Shyamasundar R.K., Choreography = Orchestration with Scripts + Conversations,International IEEE Conference on Web Services (ICWS), Beijing, China, 2008.

7. Bhattacharjee A.K., Dhodapkar S.D., and Shyamasundar R.K., Environment for Modeling Communicat-ing Reactive Systems, 1st IEEE International Conference on Information Technology, Gdansk Univer-sity of Technology, Gdnask, Poland, 2008.

8. Blanchet J., Juneja S.K. and Nandayapa L.R., Efficient Tail Estimation for Sums of Correlated Lognormals,Proceedings of 2008 Winter Simulation Conference, IEEE Press. pp. 607-614, 2008.

9. Borkar V.S. and Meyn S.P., Oja’s Algorithm for Graph Clustering and Markov Spectral Decomposition, 3rdInternational Conference on Peformance Evaluation Methodologies and Tools, Athens, Greece, 2008.

10. Borkar V.S., Opportunistic Transmission Over Randomly Varying Channels, Conference on Network Con-trol and Optimization (NETCOOP), 2008.

11. Borkar V.S., Das D.J., Datta A., Banik A.D. and Manjunath D., A Learning Scheme for Stationary Prob-abilities with Applications, 46th Allerton Conference on Control, Communications and Computing,Monticello, Illinois, USA, 2008.

12. Dal D., Abraham S., Abraham A., Sanyal S. and Sanglikar M., Evolution Induced Secondary Immunity:An Artificial Immune System based Intrusion Detection System, 7th International Conference on Com-puter Information Systems and Industrial Management Applications (CISIM’08), Ostrava, The CzechRepublic, June 26-28, 2008, IEEE Computer Society press, USA, ISBN 978-0-7695-3184-7, pp. 61-66,2008.

?13. De A., Kurur P. P., Saha S., and Saptharishi R. Fast integer multiplication using modular arithmetic. Pro-ceedings of the 40th Annual ACM Symposium on Theory of Computing, Victoria, British Columbia,Canada, May 17-20, 2008. pages 499–506, 2008.

14. Dutta C. and Radhakrishnan J., Lower Bounds for Noisy Wireless Networks Using Sampling Algorithms,ACM Symposium on Foundations of Computers Science (FOCS), pp. 394-402, 2008.

15. Ghosh S.K., Goswami P.P., Maheshwari A., Nandy S.C., Pal S.P. and Sarvattomananda S., Algorithmsfor Computing Diffuse Reflection Paths in Polygons, Proceedings of the 3rd International Workshop onAlgorithms and Computations, LNCS, Springer-Verlag, 5431, pp. 47-58, 2009.

16. Glynn P.W. and Juneja S.K., A Large Deviations View of Asymptotic Efficiency for Simulation Estimators,Proceedings of 2008 Winter Simulation Conference, IEEE Pres. pp. 396-406, 2008.

153


17. Gopalan P. and Radhakrishnan J., Finding Duplicates in a Data Stream, Symposium on Discrete Algo-rithms, pp. 402-411, 2009.

?18. Haeupler B., Kavitha T., Mathew R., Sen S. and Tarjan R., Faster Algorithms for Online Topological Or-dering, Proceedings of the 35th International Colloquium on Automata, Languages and Programming(ICALP), pp. 421-433, 2008.

19. Hallgren S., Kolla A., Sen P.G.D. and Zhang S., Making Classical Honest Verifier Zero Knowledge Pro-tocols Secure against Quantum Attacks, 35th International Colloquium on Automata Languages andProgramming, LNCS, Springer-Verlag, 5126, pp. 592-603, 2008.

20. Harsha P., Hayes T., Narayanan H., Harald R. and Radhakrishnan J., Minimizing Average Latency inOblivious Routing, Proceedings of the 19th annual ACM-SIAM Symposium on Discrete Algorithms(SODA), pp. 200-207, 2008.

?21. Huang C.C., Kavitha T., Michail D. and Nasre M., Bounded Unpopularity Matchings, Proceedings ofthe 11th Scandinavian Workshop on Algorithm Theory (SWAT), pp. 127-137, 2008.

22. Juneja S.K., Optimizing Portfolio Tail Measures: Asymptotics and Efficient Simulation Optimization, Pro-ceedings of 2008 Winter Simulation Conference, IEEE Press. pp. 621-628, 2008.

?23. Kavitha T., On a Special Co-cycle Basis of Graphs, Proceedings of the 11th Scandinavian Workshop onAlgorithm Theory (SWAT), pp. 343-354, 2008.

?24. Kavitha T., Dynamic Matrix Rank With Partial Look-ahead., Proceedings of the 28th International Con-ference on the Foundations of Software Technology and Theoretical Computer Science (FSTTCS),2008.

25. Kundu A.K., Bandopadhyay B. and Sanyal S., An Interative Algorithm for Microwave Temography UsingModified Gauss-Newton Method, 4th Kuala Lumpur International Conference on Biomedical Engineeer-ing, (BIOMED 2008), Proceedings 21, ISSN 1680-0737; ISBN 13 978-3 540-69138-9, Springer-Verlag,pp. 511-514, 2008.

?26. Liu T., Prabhakaran V. and Vishwanath S., The Secrecy Capacity of a Class of Non-degraded ParallelGaussian Compound Wiretap Channels, Proceedings of the IEEE Symposium on Information Theory(ISIT), Toronto, Canada, pp. 116-120, 2008.

27. Lodaya K., Pandya P.K. and Shah S.S., Marking the Chops: An Unambiguous Temporal Logic, Proceedingsof the 5th IFIP International Conference on Theoretical Computer Science (IFIP TCS 2008), Milano,2008.

28. Mohalik S., Rajeev A.C., Dixit M.G., Ramesh S., Suman P.V., Pandya P.K. and Jiang S., Model CheckingBased Analysis of end-to-end Latency in Embedded, Real-time Systems with Clock Drifts, Proceedings of theDesign Automation Conference (DAC 2008), Anaheim, California, ACM Press, 2008.

?29. Narayanan H., Distributed Averaging in the Presence of a Sparse Cut, ACM Symposium on Principles ofDistributed Computing (PODC), 2008.

?30. Narayanan H. and Niyogi P., Sampling Hypersurfaces Through Diffusion, 12th International Workshopon Randomization and Computation (RANDOM), 2008.

31. Pandya P.K., A Sampling Approach to the Analysis of Metric Temporal Logic, Perspectives in ConcurrencyTheory, A Festschrift for P.S. Thiagarajan, University Press (India) Pvt Ltd, 2008.

?32. Prabhakaran V., Eswaran K. and Ramchandran K., Secrecy via Sources and Channels - A Secret Key- Secret Message Rate Trade-off Region, Proceedings of the IEEE Symposium on Information Theory(ISIT), Toronto, Canada, pp. 1010-1014, 2008.

?33. Prabhakaran V., Eswaran K. and Ramchandran K., Secret Communication Using Sources and Channels’,Proceedings of the Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA,

154

in 2008-09

USA, pp. 671-675, 2008.

?34. Prabhakaran V. and Viswanath P., Interference Channel with Source/Destination Cooperation, Proceedingsof the Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, pp. 707-710, 2008.

35. Raghunathan V., Borkar V.S., Min C and Kumar P.R., Index Policies for Real-time Multicast schedulingfor Wireless Broadcast systems, INFOCOM 2008, pp. 1570-1578, 2008.

?36. Raja A., Prabhakaran V. and Viswanath P., The Two User Gaussian Compound Interference Channel,Proceedings of the IEEE Symposium on Information Theory (ISIT), Toronto, Canada, pp. 569-573,2008.

37. Raja N., A Critique of Logic in Cognitive Science, Proceedings of the Workshop on Logic and Cognition,Kolkata, 2008.

38. Raja N., Philosophy of Software Artefacts, Proceedings of the Fourth Asia-Pacific Computing and Phi-losophy Conference, NIAS, Bangalore, 2008.

39. Raja N., Aspects and Objects, Proceedings of the National Conference on Frontiers in IT, Perambalur,2009.

40. Raja N., Frameworks for Mobile Computing, Proceedings of the National Conference on AdvancedComputing and Communication Technologies, Trichy, 2009.

41. Raja N., Special Purpose Logics for Cognitive Science, Proceedings of the Workshop on ComputationalLogic and Cognitive Science, Technische Universität Dresden, Germany, 2008.

42. Sharma N. and Shamai S., Characterization of the Discrete Capacity-Achieving Distribution when MassPoints Increase, International Symposium on Information Theory and Applications (ISITA), Auckland,New Zealand, 2008.

43. Singh J., Dabeer O. and Madhow U., Capacity of the Discrete-time AWGN Channel under Output Quan-tization, Proceedings of IEEE International Symposium on Information Theory, pp. 1218 - 1222, 2008.

44. Suman P.V., Pandya P.K., Krishna S.N. and Manasa L., Integral Reset Timed Automata: Langauge Inclu-sion and Expressiveness, Proceedings of the Formal Modelling and Analysis of Timed Systems (FOR-MATS 2008), Saint Malo, France, LNCS 5215, Springer-Verlag, 2008.

45. Suman P.V. and Pandya P.K., Timed and Hybrid Automata in SAL, Proceedings of the Second Inter-national Workshop on Real Time and Embedded Systems (RTES 2008), Timisoara, Romania, IEEEComputer Society Press, 2008.

?46. Vaze R. and Heath R.W. Jr., To Code or Not to Code in Multi-Hop Relay Channels, IEEE in Proceedingsof the Communication Theory Workshop (CTW), US, Virgin Islands, 2008.

?47. Vaze R. and Heath R.W. Jr., Maximizing Reliability in Multi-Hop Wireless Networks, Proceedings of theIEEE international Symposium on Information Theory (ISIT), Toronto, Canada, 2008.

?48. Vaze R. and Robert W. Heath Jr., End-to-end Antenna Selection Strategies for Multi-hop Relay Channels,Proceedings of the IEEE Asilomar Conference On Signals Systems and Computers, pp. 1506-1510,2008.

?49. Venkat C., Srebro N. and Harsha P., Complexity of Inference in Graphical Models, Proceedings of the24th Conference on Uncertainty in Artificial Intelligence (UAI), 2008.

Web Publications

• Aditya T., Dabeer O. and Dey B., Admissible Cluster Sizes in Recommendation Systems, Workshop onInformation Theory and Its Applications, University of California San Diego, USA, Feb 2009,http://ita.ucsd.edu/workshop/09/talks/

155


Books

1. V.S. Borkar, Stochastic Approximation: A Dynamical Systems Viewpoint, Hindustan Publishing Agency,New Delhi, and Cambridge University Press, Cambridge, UK, 2008.

Book Chapters

1. P.G.D. Sen, Quantum Algorithm for the Discrete Logarithm Problem, Encyclopedia of Algorithms, 2008.

2. Animesh Trivedi, Rajan Arora, Rishi Kapoor, Sudip Sanyal, Ajith Abraham and Sugata Sanyal, MobileAd Hoc Network Security Vulnerabilities, Encyclopedia of Information Science and Technology, SecondEdition, Idea Group Inc. Publishers, USA, ISBN 978-1-60566-027-1, Edited by Mehdi Khosrow-Pour,Information Resources Management Association, USA, October, 2008, 5, pp. 2557-2561.

Technical Reports

1. “Objective Measure of Accent Difference for Better Accent Adaptation”, by Nagesha, SamudravijayaK. and Hemantha Kumar G., April 15, 2008.

In 2009-10

Journals

1. Abraham S., Sanyal S. and Sanglikar M., Particle Swarm Optimization Based Diophantine Equation Solver,International Journal of Bio-Inspired Computation (IJBIC), ISSN (Online): 1758-0374;ISSN (Print):1758-0366, Editor-In-Chief: Zhihua Cui; 2(2), pp. 100-114, 2010. DOI: 10.1504/IJBIC.2010.032126.

2. Al-Qaheri H., Dey S. and Sanyal S., Hiding Inside HTML and Other Source Codes, International Journalon Image Processing and Communications, Poland, Editor-in-Chief: R. S. Choras; 14(2-3), pp. 59-68,2009.

3. Asmussen S., Blanchet J., Juneja S.K. and Rojas-Nandayapa L., Efficient Simulation of Tail Probabilities ofSums of Correlated Lognormals, Annals of Operations Research, 2009 (DOI 10.1007/s10479-009-0658-5).

4. Asperti A., Geuvers H. and Natarajan R., Social Processes, Program Verification and All That, Mathemat-ical Structures in Computer Science, 19(5), pp. 877-896, 2009.

5. Barman K., and Dabeer O., Capacity Improvement of MIMO Systems with Asynchronous PAM, IEEETransactions on Communications, 57(11), pp. 3366-3375, 2009.

6. Ben-Sasson E., Kopparty S. and Radhakrishnan J., Subspace Polynomials and Limits to List Decoding ofReed-Solomon Codes, IEEE Transactions on Information Theory, 56(1), pp. 113-120, 2010.

7. Biswas A. and Borkar V.S., Small Noise Limits for Invariant Densities for a Class of Diffusions: A ControlTheoretic View, Journal of Mathematics Analysis and Applications, 360, pp. 476-484, 2009.

8. Borkar V.S., Ejov V. and Filar J., On the Hamiltonicity Gap and Doubly Stochastic Matrices, RandomStructures and Algorithms, 34(4), pp. 502-519, 2009.

9. Ghosh S.K., Approximation Algorithms for Art Gallery Problems in polygons, Discrete Applied Mathe-matics, 158, pp. 718-722, 2010.

10. Harsha P., Jain R., McAllester D. and Radhakrishnan J., The Communication Complexity of Correlation,IEEE Transactions on Information Theory, 56(1), pp. 438-449, 2010.

11. Jain R., Radhakrishnan J. and Sen P.G.D., A Property of Quantum Relative Entropy with an Application to

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Privacy in Quantum Communication, Journal of the Association for Computing Machinery, 56(6), 2009.

?12. Kavitha T., Liebchen C., Mehlhorn K., Michail D., Rizzi R., Ueckerdt T. and Zweig K.A., Cycle Basesin Graphs: Characterization, Algorithms, Complexity, and Applications, Computer Science Review, 3(4),pp. 199-243, 2009.

?13. Kavitha T. and Nasre M., Optimal Popular Matchings, Discrete Applied Mathematics, 157(14), pp.3181-3186, 2009.

14. Kulkarni A. and Borkar V.S., Finite Dimensional Approximation and Newton-based Algorithm for StochasticApproximation in Hilbert Space, Automatica, 45, pp. 2815-2822, 2009.

15. Kulkarni K., Sudip S., Al-Qaheri H. and Sanyal S., Dynamic Reconfiguration of Wireless Sensor Networks,International Journal of Computer Science and Applications, Editor-in-Chief: R. Akerkar. Publishedby: Tehnomathematics Research Foundation, ISSN 0972-9038, 6(4), pp. 16-42, 2009.

?16. Lalley S., Lawler G. and Narayanan H., Geometric Interpretation of Halfplane Capacity, Electronic Com-munications in Probability, 14, pp. 566-571, 2009.

17. Natarajan R., Reasoning about Synchronization Protocols, The Bulletin of Symbolic Logic, 16(1), 2010.

18. Radhakrishnan J., Rötteler M. and Sen P.G.D., Random Measurement Bases, Quantum State Distinctionand Applications to the Hidden Subgroup Problem, Algorithmica, 55(3), pp. 490-516, 2009.

?19. Raja A., Prabhakaran V. and Viswanath P., The Two User Gaussian Compound Interference Channel, IEEETransactions on Information Theory, 55(11), pp. 5100-5120, 2009.

20. Singh J., Dabeer O. and Madhow U., Transceiver Design with Low-Precision Analog-to-Digital Conversion:An Information-Theoretic Perspective, IEEE Transactions on Communications, 57(12), pp. 3629-3639,December 2009.

?21. Vaze R. and Heath, R.W. Jr., To Code in Space and Time or Not in a Multi-Hop Relay Channels, IEEETransactions on Signal Processing, 57, pp. 2736-2747, 2009.

22. Vaze R. and Heath R.W. Jr., End-to-End Joint Antenna Selection and Distributed Compress and ForwardStrategy for The Multi-Hop Relay Channel, special issue on Cooperative Communication in EurasipJournal on Wireless Communication and Networking, 2009.

23. Vaze R. and Heath R.W. Jr., To Code in Space and Time or Not in a Multi-Hop Relay Channels, IEEETransactions on Signal Processing, 57, pp. 3736-2747, 2009.


1. Aditya T., Dabeer O. and Dey B., A Channel Coding Perspective of Recommendation Systems, Proceedingsof the IEEE International Symposium on Information Theory, 1, pp. 319-323, Seoul, Korea, June 28-July 02, 2009.

2. Agarwal S., Narang A„ and Shyamasundar R.K., Distributed Scheduling of Parallel Hybrid Computations,20th International Symposium on Algorithms and Computations, Hawaii, LNCS 5878, Springer Ver-lag, pp. 1144-1154, 2009.

3. Agarwal S. and Shyamasundar R.K., Debugging PGAS Languages: Challenges and Approaches, Work-shop on Asynchrony in the PGAS Programming Model, International Conference on Supercomput-ing, New York, 2009.

4. Altman E., Kamble V. and Borkar V.S., Convergence of Population Dynamics in Symmetric Routing Gameswith a Finite Number of Players, GAMENETS 2009, Istanbul, pp. 668-672, May 03-15, 2009.

5. Azad A.P., Alouf S., Altman E., V.S. Borkar and Paschos G., Vacation Policy Optimization with Ap-plications to 802.16e Power Saving Mechanism, IFIP Wireless Days, Paris, France, pp. 1-7, December2009.

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6. Azad A.P., Alouf S., Altman E., Borkar V.S. and Paschos G., Optimal Sampling for State Change Detectionwith Application to Control of Sleep Mode, IEEE Conference on Decision and Control, Shanghai, China,pp. 1645-1650, December 2009.

?7. Bidokthi S.S., Diggavi S., Fragouli C. and Prabhakaran V., On Degraded Two Message Set Broadcasting,Proceedings of the IEEE Information Theory Workshop (ITW), Taormina, pp. 406-410, 2009.

?8. Chattopadhyay A., Torán J. and Wagner F., Graph Isomorphism is not AC0 Reducible to Group Isomor-phism, Proceedings of the 30th Foundations of Software Technology and Theoretical Computer Sci-ence (FSTTCS), Chennai, India, pp. 317-326, 2010.

?9. Chattopadhyay A. and Wigderson A., Linear Systems Over Composite Moduli, Proceedings of the 50thIEEE Symposium on Foundations of Computer Science (FOCS), Atlanta, USA, pp. 43-52, 2009.

10. Dabeer O„ Optimal Transmitters for Hypothesis Testing Over a Rayleigh Fading MAC, Proceedings of theIEEE International Conference on Communications, pp. 01-05, Dresden, Germany, June 14-18, 2009.

11. Diwan A.A., Ghosh S.K., Goswami P.P. and Lingas A., On Joint Triangulation of Two Sets of Points in thePlane, Proceedings of the India-Taiwan Conference on Discrete Mathematics, Taipei, pp. 34-43, 2009.

12. Freris N.M., Borkar V.S. and Kumar P.R., A Model Based Approach to Clock Synchronization, IEEE Con-ference on Decision and Control, Shanghai, China, pp. 5744-5749, December 2009.

13. Ghosh S.K. and Goswami P.P., Unsolved Problems in Visibility Graph Theory, Proceedings of the India-Taiwan Conference on Discrete Mathematics, Taipei, pp. 44-54, 2009.

14. Ghosh S.K., Approximation Algorithms for Art Gallery Problems in Polygons and Terrains, (Survey Paper)Proceedings of the Forth International Workshop on Algorithms and Computations, Dhaka, LNCS5942, pp. 21-34, Springer Verlag, 2010.

?15. Narayanan H. and Niyogi P., On the Sample Complexity of Learning Smooth Cuts on a Manifold, 22ndAnnual Conference on Learning Theory (COLT), 2009.

16. Hong J. and Juneja S.K., Estimating the Mean of a Nonlinear Function of a Conditional Expectation, Pro-ceedings of the Winter Simulation Conference, IEEE Press, pp. 1223-1236, 2009.

17. Hou Hoing-I, Borkar V.S. and Kumar P.R., A Theory of QoS for Wireless, INFOCOM 2009, Rio deJaneiro, pp. 486-494, April 19-25, 2009.

18. Juneja S.K. and Jain R., The Concert/Cafeteria Queuing Problem: A Game of Arrivals, ICST Fourth Inter-national Conference on Performance Evaluation Methodologies and Tools, 2009,(10.4108/ICST.VALUETOOLS2009.7624).

19. Juneja S.K. and Ramprasath L., Nested Simulation for Estimating Portfolio Losses within a Time Horizon,Proceedings of the Winter Simulation Conference, IEEE Press, pp. 434-443, 2009.

?20. Kannan R. and Narayanan H., Random Walks on Polytopes and an Affine Interior Point Method for LinearProgramming, 41st ACM Symposium on Theory of Computing (STOC), 2009.

?21. Kavitha T. and Mestre J., Max-coloring Paths: Tight Bounds and Extensions, Proceedings of the 20thInternational Symposium on Algorithms and Computation (ISAAC), pp. 87-96, 2009.

?22. Kavitha T., Mestre J. and Nasre M., Popular Mixed Matchings, Proceedings of the 36th InternationalColloquium on Automata, Languages and Programming (ICALP), pp. 574-584, 2009.

?23. Kavitha T. and Nasre M., Popular Matchings with Variable Job Capacities, Proceedings of the 20th Inter-national Symposium on Algorithms and Computation (ISAAC), pp. 423-433, 2009.

?24. Khude N., Prabhakaran V., and Viswanath P., Harnessing Bursty Interference, Proceedings of the IEEEInformation Theory Workshop (ITW), Volvos, pp. 13-16, 2009.

?25. Khude N., Prabhakaran V. and Viswanath P., Opportunistic Interference Management, Proceedings ofthe IEEE Symposium on Information Theory (ISIT), Seoul, pp. 2076-2080, 2009.

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?26. Lawler G. and Narayanan H., Mixing Times and `p Bounds for Oblivious Routing, Workshop on AnalyticAlgorithmics and Combinatorics (ANALCO), 2009.

27. Natarajan R., Reasoning about Synchronization Protocols, Proceedings of Logic Colloquium, Sofia, Bul-garia 2009.

28. Natarajan R., The Impact of Computers on the Notion of Proof, Proceedings of the Workshop on Logic,Kolkata, 2009.

29. Natarajan R., Machine-Supported Mathematics, Proceedings of the Special Session on Logic, Fifty thirdConference of the Australian Mathematical Society, Adelaide, Australia, 2009.

30. Natarajan R., The Computer and the Grelling’s Paradox, Proceedings of the Conference on Logic and SetTheory, Kolkata, 2009.

31. Natarajan R., Formal Logic as a Methodology for Communicating Mathematics, Proceedings of the FormalTheories of communication, Lorentz Center, Leiden, The Netherlands 2010.

?32. Prabhakaran V. and Kumar P.R., Communication by Sleeping: Optimizing Relay Channels Under Wakeand Transmit Power Costs, Proceedings of the IEEE Symposium on Information Theory (ISIT), Seoul,pp. 859-863, 2009.

?33. Prabhakaran V. and Viswanath P., Interference Management Through Cooperation, Proceedings of theIEEE Symposium on Information Theory (ISIT), Seoul, pp. 2071-2075, 2009.

?34. Raja A., Prabhakaran V. and Viswanath P., Reciprocity in Linear Deterministic Networks Under LinearCoding, Proceedings of the IEEE Information Theory Workshop (ITW), Volvos, pp. 321-325, 2009.

?35. Saha C., Saptharishi S., and Saxena N.. The power of depth 2 circuits over algebras. IARCS AnnualConference on Foundations of Software Technology and Theoretical Computer Science, FSTTCS,pages 371–382, 2009.

36. Sanyal S., Bhadauria R. and Ghosh C., Secure Communication in Cognitive Radio Networks, 4th In-ternational Conference on Computers & Devices for Communication, CODEC-2009, Organized byInstitute of Radio Physics & Electronics, University of Calcutta, Kolkata, December 2009.

37. Shah H., R.K. Shyamasundar and Varma P., Concurrent SSA for Concurrent Generalized Barrier Synchro-nization Languages, IEEE IPDPS 2009, Rome, 2009.

38. Sharma N. and Shamai S., Transition Points in the Capacity-Achieving Distribution for Free-Space OpticalIntensity Channels, IEEE Information Theory Workshop (ITW), Cairo, Egypt, Jan. 2010.

39. Sharma N., Equality Conditions for the Quantum f-relative Entropy and Generalized Data Processing In-equalities, IEEE International Symposium on Information Theory (ISIT), Austin, TX, USA.

40. Shyamasundar R.K. (with Bhattacharjee A.K.), Activity Diagrams: A Formal Framework to Model Busi-ness Processes and Code Generation, Journal of Object Technology, 8(1), pp. 189-220, 2009.

41. Shyamasundar R.K., Shah H. and Narendra Kumar N.V., Malware: >From Modelling to Practical Detec-tion, ICDCIT 2010, LNCS 5966, Springer Verlag, pp. 21-29, 2010.

42. Shyamasundar R.K. (with Jain S. and Ghosh R.K.), Location Based Pathfinder Service on Mobiles, IEEEInternational conference on COMSNETS, Bangalore, 2010.

43. Shyamasundar R.K. (with Varma P. and Shah H.), Backward-compatible Constant-time Exception-protectedMemory, International Conference on Foundations of Software Engineering, ACM SIGSOFT Sympo-sium on the Foundations of Software Engineering (FSE), Amsterdam, The Netherlands, 2009.

44. Shyamasundar R.K. and Agarwal S., Distributed Phase Synchronization of Dynamic Set of Processes,Twenty-Eighth Annual ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing,Calgary, Canada, 2009.

45. Suman V. and Pandya P.K., Determinization and Expressiveness of Integer Reset Timed Automata with

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Silent Transitions, Proceedings of the third International Workshop on Languages and Automata The-ory and Applications (LATA 2009), Tarragona, Spain, LNCS 5457, Springer-Verlag, 2009.

?46. Vaze R. and Heath R.W. Jr., Transmission Capacity of Multiple Antenna Ad-Hoc Networks without ChannelState Information at the Transmitter and Interference Cancelation at the Receiver, Proceedings of the 43rdAsilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, 2009.

47. Vaze R. and Heath R.W. Jr., Optimal Amplify and Forward Strategy for Two-Way Relay Channel withMultiple Relays, Proceedings of the IEEE Information Theory Workshop, Volos, Greece, June 2009.

48. Vaze R. and Heath R.W. Jr., How to Use Multiple Antennas in an Ad-Hoc Wireless Network, Proceedingsof the IEEE Signal Processing Advances in Wireless Communications, 2009, Perugia, Italy.

Book Chapters

1. Borkar V.S., Reinforcement Learning - A Bridge Between Numerical Methods and Markov Chain MonteCarlo, Perspectives in Mathematical Sciences I: Probability and Statistics, (N.S.N. Sastry, T.S.S.R.K.Rao, Mohan Delampady and B. Rajeev, eds.), World Scientific, pp. 71-91, 2009.

2. M. Gordy and S. Juneja, Full Monte Carlo simulation of CDO portfolios, Encyclopedia of QuantitativeFinance, Ed. Rama Cont, Wiley. 2010.

3. Ayu Tiwari, Sudip Sanyal, Sugata Sanyal, A Multifactor Secure Authentication System for Wireless Pay-ment, Emergent Web Intelligence: Advanced Information, Retrieval Book Series: Advanced Informationand Knowledge Processing, Ed: Chbeir Richard et al., First Edition, 2010, Chapter 13, pp. 341-369,XVI, ISBN: 978-1-84996-073-1 (Print); 978-1-84996-074-8 (Online), Springer Verlag London Limited,2010, DOI: 10.1007/978-1-84996-074-8_13.

4. Sandipan Dey, Hameed Al-Qaheri, Sugata Sanyal, Embedding Secret Data in HTML Web Page, Im-age Processing & Communications Challenges, Ed. Ryszard S. Choras and Antoni Zabtudowski,Academy Publishing House EXIT, Warsaw 2009, pp. 474-481. ISBN: 978-83-60434-62-8.

5. S. Ramesh, R.K. Shyamsundar, Real Time Programming Languages: Specification and Verification, WorldScientific Publishers, Singapore, November 2009, ISBN-13 978-981-02-2566-7, ISBN-10 981-02-2566-0.

In 2010-11

Journals

1. Abraham D.J. and Kavitha T., Voting Paths, SIAM Journal on Discrete Mathematics, 24(2), pp. 520-537,2010.

2. Arapostathis A. and Borkar V.S., Uniform Recurrence Properties of Controlled Diffusions and Applicationsto Optimal Control, SIAM J. Control Optim., 48(7), pp. 4181-4223, 2010.

3. Baswana S. and Kavitha T., Faster Algorithms for All-pairs Approximate Shortest Paths in UndirectedGraphs, SIAM Journal on Computing, 39(7), pp. 2865-2896, 2010.

4. Baswana S., Kavitha T., Mehlhorn K., and Pettie S., Additive Spanners and (alpha, beta)-spanners, ACMTransactions on Algorithms, 7(1): 5, 2010.

5. Ben-Sasson E., Harsha P., Lower Bounds for Bounded Depth Frege Proofs via Pudlk- Buss Games, ACMTransactions on Computational Logic, 11(3), 117, 2010.

?6. Bhaskar U., Fleischer L. and Anshelevich E., A Stackelberg Strategy for Routing Flow Over Time, Sym-posium on Discrete Algorithms (SODA), pp. 192-201, 2011.

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?7. Bhaskar U., Fleischer L. and Huang C-C., The Price of Collusion in Series-Parallel Networks, IntegerProgramming and Combinatorial Optimization (IPCO), pp. 313-326, 2010.

8. Biswas A., Borkar V.S and Suresh Kumar K., Risk-Sensitive Control with Near-Monotone Cost, AppliedMathematics and Optimization 62(2), pp. 145-163, 2010.

9. Biswas A. and Borkar V.S., On a Controlled Eigenvalue Problem, Systems and Control Letters, 59(11),pp. 734-735, 2010.

10. Borkar V.S. and Suresh Kumar K., McKean-Vlasov Limit in Portfolio Optimization, Stochastic Analysisand Applications, 28(5), pp. 884-906, 2010.

11. Borkar V.S. and Suresh Kumar K., A New Markov Selection Procedure for Degenerate Diffusions, Journalof Theoretical Probability, 23(3), pp. 729-747, 2010.

12. Borkar V.S. and Suresh Kumar K., Singular Perturbations in Risk-Sensitive Stochastic Control, SIAM J.Control Optim., 48(6), pp. 3675-3697, 2010.

13. Borkar V.S., Ghosh M.K. and Rangarajan G., Application of Nonlinear Filtering to Credit Risk, OperationsResearch Letters, 38(6), pp. 527-532, 2010.

14. Garg N., Kavitha T., Kumar A., Mehlhorn K. and Mestre J., Assigning Papers to Referees, Algorithmica,58(1), pp. 119-136, 2010.

15. Ghosh S.K. and Klein R., Online Algorithms for Searching and Exploration in the Plane, Computer ScienceReview, 4, pp. 189-201, 2010.

16. Gopalkrishnan M., Catalysis in Reaction Networks, Bull. Math Biol., pp. 1-21, 2011.

17. Gordy M. and Juneja S.K., Nested Simulation in Portfolio Risk Measurement, Management Science,56(10), pp. 1833-1848, 2010.

18. Hallgren S., Moore C., Rotteler M., Russell A., and Sen P.G.D., Limitations of Quantum Coset States forGraph Isomorphism, Journal of the Association for Computing Machinery, 57(6), 2010.

19. Jain R., Juneja S.K. and Shimkin N., The Concert Queuing Problem: To Wait or To Be Late, Discrete EventsDynamic Systems, 21, pp. 103-138, 2011.

20. Kavitha T. and Nasre M., Popular Matchings with Variable Item Copies, Theoretical Computer Science,412(24) pp. 2679-2690, 2011.

21. Kundu A.K., Bandopadhyay B. and Sanyal S., A Microwave Imaging And Enhancement Technique FromNoisy Synthetic Data, International Symposium on Advanced Engineering and Applied Management- 40th Anniversary in Higher Education (1970-2010), University Politehnica Timisoara, Faculty ofEngineering, Hunedoara, Romania, 2010.

22. Salodkar N., Karandikar A. and Borkar V.S., A Stable Online Algorithm for Energy-Efficient MultiuserScheduling, IEEE Trans. on Mobile Computing, 9(10), pp. 1391-1406, 2010.

23. Sharma N. and Shamai S. (Shitz), Transition Points in the Capacity-achieving Distribution for the Peak-power Limited AWGN and Free-space Optical Intensity Channels, Prob. Inf. Trans., 46(4), pp. 283-299,2010.

24. Sharma N., Equality Conditions for the Quantum f -relative Entropy and Generalized Data Processing In-equalities, Quantum Inf. Process., pp. 1-24, 2011.


1. Abraham S., Kiss I, Sanyal S. and Sanglikar M., Steepest Ascent Hill Climbing for a Mathematical Problem,International Symposium on Advanced Engineering and Applied Management - 40th Anniversary inHigher Education (1970-2010), University Politehnica Timisoara, Faculty of Engineering, Hunedoara,Romania, ISBN 978-973-0-09340-7, DBLP CoRR abs/1010.0298, 2010.

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2. Barman K. and Dabeer O., Local Popularity Based Collaborative Filters, IEEE International Symposiumon Information Theory, pp. 1668-1672, Austin, USA, 2010, Digital Object Identifier: 10.1109/ISIT.2010.5513326,Available online at http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5513326.

3. Borkar V.S. and Jain R., Risk-constrained Markov Decision Processes, 49th IEEE Conference on Decisionand Control, Atlanta, Georgia, USA, 2010.

?4. Chattopadhyay A., Gavaldà R., Hansen K.A. and Thérien D., Learning Read-constant Polynomials ofConstant Degree Over Arbitrary Moduli, Proceedings of the 6th International Computer Science Sym-posium in Russia (CSR), St-Petersburg, Russia, pp. 29-42, 2011.

?5. Chattopadhyay A. and Lovett S., Linear Systems Over Finite Abelian Groups, Proceedings of the 26thIEEE Conference on Computational Complexity (CCC), San Jose, USA, pp. 300-308, 2011.

6. Dabeer O. and Madhow U., Channel Estimation with Low-precision ADC, IEEE International Conferenceon Communications, pp. 01-06, Cape Town, 2010, Digital Object Identifier: 10.1109/ICC.2010.5501995,Available online at http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5501995.

7. Dey S. and Juneja S.K., Multidimensional Fourier Inversion using Importance Sampling with Application toOption Pricing, Proceedings of Winter Simulation Conference, IEEE Press, pp. 2801-2809, 2010.

8. Diakonikolas I., Harsha P., Klivans A., Meka R., Raghavendra P., Servedio R.A. and Tan L.Y., Boundingthe Average Sensitivity and Noise Sensitivity of Polynomial Threshold Functions, Proceedings of 42nd ACMSymposium on Theory of Computing (STOC), pp. 543-552, 2010.

9. Emek Y., Halldórsson M., Mansour Y., Patt-Shamir B., Radhakrishnan J. and Rawitz D., Online SetPacking and Competitive Scheduling of Multi-part Tasks, PODC, pp. 440-449, 2010.

10. Harsha P., Klivans A. and Meka R., An Invariance Principle for Polytopes, Proceedings of 42nd ACMSymposium on Theory of Computing (STOC), pp. 543-552, 2010.

11. Jain R., Juneja S.K. and Shimkin N., Queuing for Timely Service: Equilibrium Analysis and Social Effi-ciency, INFORMS Manufacturing and Services Operations Management Society (MSOM) Conference,2010.

12. Juneja S.K., Monte Carlo Methods in Finance: An Introductory Tutorial, Proceedings of Winter SimulationConference, IEEE Press, pp. 95-103, 2010.

13. Kavitha T., Nasre M. and Nimbhorkar P., Popularity at Minimum Cost, Proceedings of the InternationalSymposium on Algorithms and Computation (ISAAC), LNCS 6506, pp. 145-156, 2010.

14. Lodaya K., Pandya P.K. and Shah S., Around Dot-depth Two, Proceedings of the 14th Conference onDevelopment in Language Theory (DLT2010), London, Canada, LNCS 6224, Springer-Verlag, 2010.

?15. Narayanan H. and Mitter S., Sample Complexity of Testing the Manifold Hypothesis, 24th Annual Con-ference on Neural Information Processing Systems (NIPS), 2010.

?16. Narayanan H. and Rakhlin S., Random Walk Approach to Regret Minimization, 24th Annual Conferenceon Neural Information Processing Systems (NIPS), 2010.

17. Narendra Kumar N.V., Shah H. and Shyamasundar R.K., Benchmarking Program Behaviour for DetectingMalware Infection, European Institute of Anti Virus Research (EICAR) Conference, pp. 69-91, 2010.

18. Narendra Kumar N.V., Shyamasundar R.K., Goswami V. and Bansal R., Metamorphic Virus: Is itAmenable for Algorithmic Detection, Association of anti Virus Asia Researchers, AVAR InternationalConference, Bali, pp. 42-47, 2010.

19. Narendra Kumar N.V., Shah H.J., and Shyamasundar R.K., Can we Certify Systems for Freedom fromMalware, International Conference on Software Engineering, ICSE, NIAR 2, pp. 175-178, 2010.

20. Nayak C.K., Surendran J., Merchant S., Desai U.B. and Sanyal S., Error Concealment of H.264 EncodedVideo through a Hybrid Scheme, International Conference on ACM Management of Emergent Digital

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EcoSystems (MEDES 2010) in cooperation with ACM SIGAPP and IFIP WG 2.6, National Electronicsand Computer Technology Center Thailand (NECTEC) and Kasetsart University, Bangkok, Thailand,2010.

21. Pandya P.K. and Shah S., Unambiguity in Timed Regular Langauges: Automata and Logics, Proceedings ofFormal Modelling and Anaylsis of Timed Systems (FORMATS 2010), Austria, LNCS 6246, Springer-Verlag, 2010.

?22. Prabhakaran V. and Prabhakaran M., Assisted Common Information with Applications to Secure Two-PartyComputation, Proceedings of the IEEE Symposium on Information Theory (ISIT), Austin, USA, pp.2602-2606, 2010.

23. Radhakrishnan J., Shah S. and Shannigrahi S., Data Structures for Storing Small Sets in the BitprobeModel, ESA (2), pp. 159-170, 2010.

24. Raja N., The Platonist versus Formalist Divide in Computer Science, Proceedings of the Annual Confer-ence on Logic, Kolkata, 2010.

25. Raja N., Computer-Aided Proofs, Proceedings of International Congress of Mathematicians (ICM’10),Hyderabad, 2010.

26. Raja N., Interactive Proof Systems, Proceedings of World Congress on Universal Logic (UNILOG’10),Lisbon, Portugal, 2010.

27. Raja N., The Impact of Computers On the Notion of Proof, Proceedings of the ASL North AmericanAnnual Conference, Berkeley, USA, 2011.

28. Raja N., Computer Algebra Systems Versus Interactive Theorem Provers, Proceedings of Applications ofAlgebra in Logic and Computer Science, Zakopane, Poland, 2011.

?29. Rui W., Prabhakaran V. and Viswanath P., Interference Channels with Half-duplex Cooperation, Proceed-ings of the IEEE Symposium on Information Theory (ISIT), Austin, USA, pp. 375-379, 2010.

?30. El Rouayheb S., Prabhakaran V. and Ramchandran K., Secure Distributive Storage of Decentralized Data:Can Interaction Help?, Proceedings of the IEEE Symposium on Information Theory (ISIT), Austin,USA, pp. 1953-1957, 2010.

31. Samudravijaya K., Spoken Disfluencies in Multilingual Spoken Corpora, Proceedings of Oriental CO-COSDA, Kathmandu, Nepal, 2010.

32. Sanyal S., Shelat A. and Gupta A., New Frontiers in Network Security Threats Within, 2010 SecondVaagdevi International Conference on Information Technology for Real World Problems, VaagdeviCollege of Engineering, Warangal, 2010,

33. Sharma N., Equality Conditions for the Quantum f -relative Entropy and Generalized Data Processing In-equalities, Proceedings of IEEE International Symposium Information Theory (ISIT), Austin, TX, 2010.

34. Shyamasundar R.K. and Goel N., Automatic Monitoring of SLAs for Web Services, 5th IEEE APSCC2010, Hangzhou, China, pp 99-106, 2010.

35. Shyamasundar R.K., Agarwal S. and Joshi S., Distributed Generalized Dynamic Barrier Synchronization,International Conference on Distributed Computing and Networks, LNCS 6522, Springer Verlag,2011.

36. Shyamasundar R.K. and Narang A., Affinity Driven Distributed Scheduling Algorithm for Parallel Com-putations, International Conference on Distributed Computing and Networks, LNCS 6522, SpringerVerlag, 2011.

37. Vaze R., Throughput-Delay-Reliability Tradeoff in Ad Hoc Networks, SpaSWiN 2010, Sixth Workshop onSpatial Stochastic Models for Wireless Networks (SpaSWiN), Avignon, France, 2010.

38. Vaze R., Truong K., Weber S., and Heath R.W. Jr., Two-Way Transmission Capacity of Wireless ad-hoc

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Networks, Proceedings of IEEE International Symposium on Information Theory (ISIT 2010), Austin,Texasx, USA 2010.

Workshop Proceedings:

1. Dinur I. and Harsha P., Composition of Low-Error 2-Query PCPs Using Decodable PCPs. Property Testing- Current Research and Surveys, (outgrow of a workshop at the Institute for Computer Science (ITCS)at Tsinghua University, January 2010), LNCS 6390 Springer Verlag, 280-288, 2010.

Books

1. N. Raja and Adegboyega Ojo (Editors), Distributed Computing and Internet Technology, Lecture Notesin Computer Science, 6536, ISBN: 978-3-642-19055-1, Springer, 2011.

2. R.K. Shyamasundar and M.A. Pai (editors), Homi Bhabha and the Computer Revolution, Oxford Univer-sity Press, February 2011.

3. R.K. Shyamasundar and Lokendra Shastry (editors), ACM Bangalore Compute 2011, ACM Digital Li-brary, http://portal.acm.org/citation.cfm?id=1980422, March 2011.

Book Chapters

1. S.S. Agrawal, K.K. Arora, S. Arora, Samudravijaya K., Text and Speech Corpora Development in IndianLanguages, Eds. S.Itahashi and C.Tseng, Consideration Books, Los Angeles, pp. 94-97, 2010.

2. Borkar V.S. and Kuri J., Optimal Distributed Uplink Channel Allocation: A Constrained MDP Formulation,Advances in Dynamic Games (M. Breton and K. Szajowski, eds.) Birkhäuser, New York, pp. 425-445,2010.

3. N.V. Narendra Kumar, H.J.Shah and R.K. Shyamasundar, Towards Checking Tampering of Software,Cyber Security, Cyber Crime and Cyber Forensics: Applications and Perspectives, pp. 204-219, IGIGlobal, 2010.

4. K. Samudravijaya, Multilingual telephony speech corpora of Indian languages, Computer Processing ofAsian Spoken Languages, Eds. S.Itahashi and C.Tseng, Consideration Books, Los Angeles, pp. 189-193, 2010.

5. R.K. Shyamasundar, Software Engineering: Craft-to-discipline-to-Science, Homi Bhabha and the Com-puter Revolution, Oxford University Press, pp. 232-256.

6. Ganesh Sivaraman, Swapnil Mehta, Neeraj Nabar and Samudravijaya K., Higher Accuracy of HindiSpeech Recognition Due to Online Speaker Adaptation, Technology Systems and Management, Eds.K.Shah et al., pp. 233-238, Springer Berlin Heidelberg, 2011.

Invited Surveys

?1. Chattopadhyay A. and Pitassi T., The Story of Set-Disjointness, ACM Special Interest Group in Algo-rithms and Computation Theory (SIGACT) News, 41(3), 2010.

?2. Chattopadhyay A., Multilinear Polynomials Modulo Composites, Bulletin of the European Associationfor Theoretical Computer Science (EATCS), Computational Complexity Column, No. 100, 2010.

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Journals

1. Abraham S., Kiss I., Sanyal S. and Sanglikar M., Finding Numerical Solutions of Diophantine EquationsUsing Steepest Ascent Hill Climbing, Annals of Faculty Engineering Hunedoara - International Journalof Engineering (ANNALS-2011-2-15), 9(2), pp. 91-96, (ISSN 1584-2665), (2011).

2. Aditya S., Dabeer O. and Dey B.K., A Channel Coding Perspective of Collaborative Filtering, IEEE Trans-actions on Information Theory, 57(4), pp. 2327-2341, (2011).

3. Banik S„ Roy B., Dey P., Chaki N. and Sanyal S., QoS Routing Using OLSR With Optimization forFlooding, International Journal of Information and Communication Technology Research, 1(4), pp.164-168, ISSN: 2223-4985, (2011).

4. Bhattasali T., Chaki R. and Sanyal S., Sleep Deprivation Attack Detection in Wireless Sensor Network, In-ternational Journal of Computer Applications, 40(15), pp. 19-25, ISBN: 978-93-80866-55-8, Publishedby Foundation of Computer Science, New York, USA, DOI: 10.5120/5056-7374, (2012).

5. Cui Z., Yang C. and Sanyal S., Training Artificial Neural Networks using APPM, International Journalof Wireless and Mobile Computing, Editor-in-Chief: Zhihua Cui; 5(2), pp. 168-174, ISSN (Online):1741-1092; ISSN (Print): 1741-1084, (2012).

6. Dabeer O. and Chaudhuri S., Analysis of an Adaptive Sampler Based on Weber’s Law,, IEEE Transactionson Signal Processing, 59(4), pp. 1868-1878, (2011).

7. Dey A., Chaki N. and Sanyal S., Modeling Smart Grid Using Generalized Stochastic Petri Net, Journalof Convergence Information Technology (JCIT), 6(11), pp. 104-114, ISSN: 1975-9320 (Print), ISSN:2233:9299 (Online).doi:10.4156/jcit.vol6.issue11.13, (2011).

8. Goel N. and Shyamasundar R.K., An Executional Frameowrk for BPMN Using Orc, FTRA Journal ofConvergence, 3(1), (2012).

9. Gopalkrishnan M., Catalysis in Reaction Networks, Bulletin of Mathematical Biology, pp. 01-21 (2011)

10. Hauepler B., Kavitha T., Mathew R., Sen S. and Tarjan R.E., Incremental Cycle Detection, TopologicalOrdering, and Strong Component Maintenance, ACM Transactions on Algorithms 8(1) pp. 3 (2012).

11. Huang C.-C., Kavitha T., Michail D. and Nasre M., Bounded Unpopularity Matchings, Algorithmica61(3), pp. 738-757 (2011).

12. Kavitha T., Mestre J. and Nasre M., Popular Mixed Matchings, Theoretical Computer Science 412(24),issue consisting of selected papers from ICALP 2009: pp. 2679-2690 (2011).

13. Kavitha T., Mehlhorn K. and Michail D., New Approximation Algorithms for Minimum Cycle Bases ofGraphs, Algorithmica 59(4), pp. 471-488 (2011).

14. Kavitha T., Properties of Gomory-Hu Co-cycle Bases, Theoretical Computer Science 420, pp. 48-55 (2012).

15. Kavitha T., Faster Algorithms for All-Pairs Small Stretch Distances in Weighted Graphs, Algorithmica63(1-2), pp. 224-245 (2012).

16. Narang A. and Shyamasundar R.K., Performance Driven Distributed Scheduling of Parallel Hybrid Com-putations, Theoretical Computer Science, 412(32), pp. 4212-4225 (2011).

17. Narang A., Srivastava A., Katta N.P.K. and Shyamasundar R.K., Performance Driven Multi-objectiveDistributed Scheduling for Parallel Computations, Operating Systems Review, 45(2), pp. 14-27 (2011).

18. Pal S., Khatua S., Chaki N. and Sanyal S., A New Trusted and Collaborative Agent Based Approach for En-suring Cloud Security, Annals of Faculty Engineering Hunedoara International Journal of Engineering,10(1), pp. 71-78. ISSN: 1584-2665, (2012).

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?19. Prabhakaran V., Puri R. and Ramchandran K., Hybrid Digital-Analog Codes for Source-Channel Broadcastof Gaussian Sources Over Gaussian Channels, IEEE Transactions on Information Theory, 57(7), pp. 4573-4588, 2011.

?20. Prabhakaran V. and Viswanath P., Interference Channel with Source Cooperation, IEEE Transactions onInformation Theory, 57(1), pp. 156-186, 2011.

?21. Prabhakaran V. and Viswanath P., Interference Channel with Destination Cooperation, IEEE Transactionson Information Theory, 57(1), pp. 187-209, 2011.

22. Roy B., Banik S., Dey P., Sanyal S. and Chaki N., Ant Colony Based Routing for Mobile Ad-Hoc NetworksTowards Improved Quality of Services, Journal of Emerging Trends in Computing and InformationSciences, 3(1), pp. 10-14, (E-ISSN 2218-6301/ ISSN 2079-8407), (2012).

23. Sharma N., Equality Conditions for the Quantum f -relative Entropy and Generalized Data Processing In-equalities, Quantum Information Processing, 11, pp. 137-160, (2012).

24. Thakur M.R., Khilnani D.R., Gupta K., Jain S., Agarwal V., Sane S., Sanyal S. and Dhekne P.S., Detec-tion and Prevention of Botnets and Malware in an Enterprise Network, International Journal of Wirelessand Mobile Computing, Editor-in-Chief: Zhihua Cui, 5(2), pp. 144-153, ISSN (Online): 1741-1092;ISSN (Print): 1741-1084, (2012).

25. Vaze R., Truong K., Weber S. and Heath R.W. Jr., Two-Way Transmission Capacity of Wireless Ad-hocNetworks, IEEE Transactions on Wireless Communications, 10(6), pp. 1966-1975, (2011).

26. Vaze R. Heath R.W. Jr., On the Capacity and Diversity-Multiplexing Tradeoff of the Two-Way Relay Channel,IEEE Transactions on Information Theory, 57(7), pp. 4219-4234, (2011).

27. Vaze R., Throughput-Delay-Reliability Tradeoff with ARQ in Wireless Ad Hoc Networks, IEEE Transactionson Wireless Communications, 10(7), pp. 2142-2149, (2011).

28. Vaze R., Transmission Capacity of Spectrum Sharing Ad Hoc Networks with Multiple Antennas, IEEE Trans-actions on Wireless Communications, 10(7), pp. 2334-2340, (2011).

29. Vaze R. and Heath R.W. Jr. Transmission Capacity of Ad-hoc Networks with Multiple Antennas using Trans-mit Stream Adaptation and Interference Cancelation, IEEE Transactions on Information Theory, 58(2), pp.780-792, (2012).


1. Bondale N. and Gupta G., Augmenting Language Tools for Effective Communications, Proceedings ofIndo-Japan Conference on Perception and Machine Intelligence, (PerMin 12), Springer LNCS 7143,pp. 122-128 (2012).

2. Busch C., Dutta C., Radhakrishnan J., Rajaraman R., Srivathsan S. Split and Join: Strong Partitions andUniversal Steiner Trees for Graphs. Foundations of Computer Science (FOCS) pp 81–90, 2012.

?3. Chattopadhyay A., Edmonds J., Ellen F. and Pitassi T., A Little Advice Can Be Very Helpful, Proceedingsof the 23rd Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), Kyoto, Japan, pp. 615-625, 2012.

4. Chien S., Harsha P., Sinclair A. and Srinivasan S., Almost Settling the Hardness of NoncommutativeDeterminant, Proceedings of the 43rd ACM Symposium on Theory of Computing (STOC), San Jose,California, pp. 499-508, (2011).

5. Czap L., Prabhakaran V., Fragouli C. and Diggavi S., Secret Message Capacity of Erasure BroadcastChannels with Feedback, Proceedings of 2011 IEEE Information Theory Workshop, pp. 65-69 (2011).

6. Dabeer O., Mehendale P., Karnik A. and Saroop A., Timing Tweets to Increase Effectiveness of InformationCampaigns, Fifth AAAI International Conference on Weblogs and Social Media, Barcelona, Spain, (pa-

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per available at http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/view/2771) (2011).

7. Dey S. and Juneja S., Efficient Estimation of Density and Probability of Large Deviations of Sum of IIDRandom Variables, Proceedings of Winter Simulation Conference, IEEE Press, pp. 3805-3816, (2011).

8. Glynn P.W. and Juneja S., Ordinal Optimization: A Nonparametric Framework, Proceedings of WinterSimulation Conference, IEEE Press, pp. 4062-4069 (2011).

9. Godambe T. and Samudravijaya K., Speech Data Acquisition for Voice Based Agricultural InformationRetrieval, 39th All India DLA Conference, (2011).

10. Goel N., Narendra Kumar N.V. and Shyamasundar R.K., SLA Monitor: A System for Dynamic Moni-toring of Adaptive Web Services, 2011 IEEE Ninth European Conference on Web Services, ECOWS, pp.109-116, (2011).

11. Goel N. and Shyamasundar R.K., An Executional Framework for BPMN Using Orc., 2011 IEEE Asia-Pacific Services Computing Conference, APSCC, pp. 29-36 (2011).

12. Huang C.-C. and Kavitha T., Popular Matchings in the Stable Marriage Problem, Proceedings of theInternational Colloquium on Automata, Languages and Programming (ICALP) 1, pp. 666-677 (2011).

13. Huang C.-C. and Kavitha T., Near-Popular Matchings in the Roommates Problem, Proceedings of theEuropean Symposium on Algorithms (ESA), pp. 167-179 (2011).

14. Huang C.-C. and Kavitha T., Efficient Algorithms for Maximum Weight Matchings in General Graphs withSmall Edge Weights, Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA), pp.1400-1412 (2012).

15. Hussain T. and Samudravijaya K., Comparison and Usefulness of ASR11 Scheme Overprevious Schemesfor Transliteration and Label Set Purposes for Indian Languages, 39th All India DLA Conference, (2011).

16. Jimenez-Pacheco A. and Dabeer O., A Novel Conflict-Free Memory and Processor Architecture for DVB-T2 LDPC Decoding, 3rd International Congress on Ultra Modern Telecommunications and ControlSystems and Workshops (ICUMT), pp. 01-07, 5-7, Budapest, Hungary (2011).

17. Kavitha T., Popularity vs Maximum Cardinality in the Stable Marriage Setting, Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 123-134 (2012).

18. Kini D., Krishna S.N. and Pandya P.K., On Construction of Safety Signal Automata for MITL[UI , SI ] usingTemporal Projections, Proceedings of the Formal Modelling and Anaylsis of Timed Systems (FORMATS2011), Aalborg, Denmark, LNCS 6919, Springer, (2011).

19. Narendra Kumar N.V., Shyamasundar R.K., Sebastian G. and Yashashwee S., Algorithmic Detection ofMalware via Semantic Signatures, European Association of Anti Virus Researchers, pp. 49-73, Krems,Austria, (2011).

20. Pande A., Kimbahune S., Bondale N., Shinde R. and Shanbhag S., Distributed Processing and InternetTechnology to Solve Challenges of Primary Healthcare in India, Proceedings of International Conferenceon Distributed Computing and Internet Technology, (ICDCIT 2012), Springer LNCS 7154, pp. 188-199

(2012).

21. Pandya P.K. and Shah S.S., On Expressive Powers of Timed Logics: Comparing Boundedness, Non-punctualityand Deterministic Freezing, Proceedings of the 22nd International Conference on Concurrency Theory(CONCUR 2011), Aachen, Germany, LNCS 6901, Springer (2011).

?22. Prabhakaran V. and Prabhakaran M., Assisted Common Information: Further Results, Proceedings of theIEEE Symposium on Information Theory (ISIT), St. Petersburg, pp. 2861-2865, 2011.

23. Radhakrishnan J. and Shannigrahi S., Streaming Algorithms for 2-Coloring Uniform Hypergraphs, WADS2011 pp. 667-678, (2011).

24. Raja N., Machine-Supported Mathematics, Proceedings of the Seventh International Conference on In-

167


dustrial and Applied Mathematics ICIAM 2011, Vancouver, Canada (2011).

25. Raja N., Higher-dimensional Type Theory and the Univalence Axiom, Proceedings of the Eighth Confer-ence on Mathematics: Algorithms and Proofs MAP 2011, Leiden, The Netherlands (2011).

26. Raja N., Abstract Machines for the Geometry of Interaction, Proceedings of Logic and Interaction, Mar-seille, France (2012).

27. Raja N., From Combinatory Algebras to Algebraic Structures, Proceedings of Applications of Algebra inLogic and Computer Science, Zakopane, Poland (2012).

28. Ranjani H.G., Bondale N. and Sreenivas T.V, On Stochastic Approaches to Speech and Music SignalAnalysis, Proceedings of the International Symposium on Frontiers of Research on Speech and Music(FRSM 2012), pp. 01-05, (2012).

29. Sarkar B.B., Sanyal S. and Chaki N., A Distributed Framework for Tele Health Monitoring System, IEEE-ACM Proceedings of the International Conference on Wireless Technologies for Humanitarian Relief(ACWR2011), Amrita University, Amritapuri Campus, Amrita Vishwa Vidyapeetham, Clappana P.O., Kollam - 690525, ACM New York, NY, USA@2011, pp. 411-416, (2011).

30. Savov I., Fawzi O., Wilde M., P.G.D. Sen and Hayden P., Quantum Interference Channels, 49th AnnualAllerton Conference, Allerton, USA, (2011).

31. Sharma N., Das S, and Muthukrishnan S., Entropy Power Inequality for a Family of Discrete Random Vari-ables, Proceedings of the IEEE International Symposium Information Theory (ISIT), St. Petersberg,Rusia, (2011).

32. Sharma N., Das S. and Muthukrishnan S., On Some Special Cases of the Entropy Photon-Number Inequal-ity, to be presented at the 7th Conference on Theory of Quantum Computation, Communication andCryptography (TQC), Tokyo, Japan (2012).

33. Shekhar S., Ghosh R.K. and Shyamasundar R.K., Postorder Based Routing and Transport Protocol forWSNs, ICDCN 2012, Hongkong, Jan 2012, LNCS 7129, pp. 281-294, Springer-Verlag (2012).

?34. Sinclair A., Srivastava P. and Thurley M., Approximation Algorithms for Two-state Anti-ferromagneticSpin Systems on Bounded Degree Graphs, Proceedings of the ACM-SIAM Symposium on Discrete Al-gorithms (SODA), pp. 941-953, 2012.

35. Vaze R., Percolation and Connectivity on the Signal to Interference Ratio Graph, IEEE International Con-ference on Computer Communication (INFOCOM 2012), Orlando, Florida, (2012).

36. Vaze R. and Ganapathy H., Sub-modularity and Antenna Selection in MIMO Systems, (Invited Paper),Information Theory and Applications, San Diego, (2012).

Web Publications:

1. Bondale N., Basu J. and Bepari M.S, ASR Consortium Pronunciation Specification for Lexicon Development,Workshop on Standardization of Pronunciation Lexicon in Indian Languages, New Delhi,http://www.w3cindia.in/presentation.aspx (2011).

Book Chapters

1. S. Juneja, An Introduction to Financial Mathematics, Math Unlimited: Essays in Mathematics, Editors:H.N. Ramaswamy, C.S. Yogananda, R. Sujatha, Science Publishers, pp. 191-223, 2011.

2. Kroese D.P., Shimkin N., Kreimer J. and Juneja S., Preface, Annals of Operations Research, 189(1), pp.01-3, (2011).

?3. Narayanan H., “Sample Complexity in Manifold learning”, in Manifold Learning, Theory and Applica-tions, Chapter 4, Edited by Yunqian Ma and Yun Fu, CRC Press, 2011.

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4. R.K. Shyamasundar and L. Shastry (editors), ACM Pune Chapter, COMPUTE 2012, ACM Digital Li-brary, January 2012.

In 2012-13

Journals

1. Barman K. and Dabeer O., Analysis of a Collaborative Filter Based on Popularity Amongst Neighbors, IEEETransactions on Information Theory, 58(12), pp.7110-7134, (doi: 10.1109/TIT.2012.2216980), (2012).

2. Dabeer O., Joint Probability Mass Function Estimation From Asynchronous Samples, IEEE Transactions onSignal Processing, 61(2), pp.355-364, (doi: 10.1109/TSP.2012.2225050), (2013).

?3. Dixit N. M., Srivastava P. and Vishnoi N. K., A Finite Population Model of Molecular Evolution: Theoryand Computation, Journal of Computational Biology, 19(10), pp. 1176-1202, 2012.

4. Emek Y., Halldórsson M.M., Mansour Y., Patt-Shamir B., Radhakrishnan J. and Rawitz D., Online SetPacking, SIAM Journal of Computing, 41(4) pp. 728-746, (2012).

5. Gopalkrishnan M., Miller E. and Shiu A., A Projection Argument for Differential Inclusions, with Applica-tions to Persistence of Mass-Action Kinetics, SIGMA (Symmetry, Integrability, and Geometry: Methodsand Applications), 9(25), 25 pages, (2013).

6. Ghosh S.K., Goswami P.P., Maheshwari A., Nandy S.C., Pal S.P. and Sarvattomananda S., Algorithmsfor Computing Diffuse Reflection Paths in Polygons, The Visual Computer, 28(12), pp. 1229-1237, (2012).

7. Harsha P., Klivans A. and Meka R., An Invariance Principle for Polytopes, Journal of the ACM, 59(6),pp. 29, (2012).

8. Huang C.-C. and Kavitha T., Popular Matchings in the Stable Marriage Problem, Information and Com-putation, 222 pp. 180-194, (2013).

9. Huang C.-C. and Kavitha T., Near-Popular Matchings in the Roommates Problem, SIAM Journal onDiscrete Mathematics, 27(1) pp. 43-62, (2013).

10. Juneja S.K. and Shimkin N., The Concert Queuing Game: Strategic Arrivals with Waiting and TardinessCosts, Queuing Systems. DOI 10.1007/s11134-012-9329-3, (2012).

?11. Kannan R. and Narayanan H., Random Walks on Polytopes and an Affine Interior Point Method for LinearProgramming, Mathematics of Operations Research, 37(1), pp. 1-20, 2012.

?12. Mulmuley K., Narayanan H. and Sohoni M., Geometric Complexity Theory III: On Deciding Non-vanishing of a Generalized Littlewood-Richardson Coefficient, Journal of Algebraic Combinatorics, 36(1),pp. 103-110, 2012.

13. Panconesi A. and Radhakrishnan J., Expansion Properties of (Secure) Wireless Networks, ACM Transac-tions on Algorithms 8(3), pp. 21, (2012).

14. Prabhakaran V.M., Eswaran K. and Ramchandran K., Secrecy via Sources and Channels, IEEE Transac-tions on Information Theory, 58(11) pp. 6747-6765, (2012).

15. Raja N., Vicious Queues and Vicious Circles, Asia Pacific Mathematics Journal, 3(1), pp. 12-16, (2013).

16. Sharma N. and Warsi N.A., Fundamental Bound on the Reliability of Quantum Information Transmission,Phys. Rev. Lett., p. 080501, 110(8), (2013).

17. Vaze R. and Ganapathy H„ Sub-modularity and Antenna Selection in MIMO systems, IEEE Communi-cations Letters, (99), (2012).

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?1. Ada A., Chattopadhyay A., Cook S.A., Fontes L., Koucký M. and Pitassi T., The Hardness of Being Pri-vate, Proceedings of the 27th IEEE Conference on Computational Complexity (CCC), Porto, Portugal,2012.

?2. Ada A., Chattopadhyay A., Fawzi O. and Nguyen P., The NOF Multiparty Communication Complexityof Composed Functions, Proceedings of the 39th International Colloquium on Automata, Languagesand Programming (ICALP), Warwick, UK, 2012.

?3. Agrawal M., Saha C., Saptharishi R., and Saxena N. Jacobian hits circuits: hitting-sets, lower bounds fordepth-d occur-k formulas & depth-3 transcendence degree-k circuits. Proceedings of the 44th Symposiumon Theory of Computing Conference, STOC pages 599–614., 2012.

?4. Azar Y., Bhaskar U., Fleischer L. and Panigrahi D., Online Mixed Packing and Covering, Symposiumon Discrete Algorithms (SODA), pp. 85-100, 2013.

5. Bai T., Vaze R. and Heath R.W. Jr., Using Random Shape Theory to Model Blockage in Random CellularNetworks, invited paper, IEEE SPCOM 2012, IISc. Bangalore, (2012).

6. Bondale, N., Kimbahune, S. and Pandey, A., mHEALTH-PHC: A Community Informatic Tool for PrimaryHealthcare in India, Proceedings of IEEE Conference on Technology and Society in Asia (T&SA), pp.1-6, (2012).URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6397994&isnumber=6397962.

7. Bondale N. and Sreenivas T., Emotiphons: Emotion Markers in Conversational Speech - Comparison AcrossIndian Languages, Proceedings of the 2nd Workshop on Sentiment Analysis Where AI Meets Psychol-ogy (SAAIP 2012), pp. 73-79, The COLING 2012 Organizing Committee, (2012).URL: http://www.aclweb.org/anthology/W12-5308.

8. Buyukalp Y., Maatouk G., Prabhakaran V.M. and Fragouli C., Untrusting Network Coding, Interna-tional Symposium on Network Coding (NETCOD), (2012).

9. Chattopadhyay A. and Santhanam R., Lower Bounds on Interactive Compressibility by Constant-depthCircuits, Proceedings of IEEE Annual Symposium on Foundations of Computer Science (FOCS),New Brunswick, NJ, USA, pp. 619-628, (2012).

10. Cygan M., Grandoni F. and Kavitha T., On Pairwise Spanners, Proceedings of the 30th InternationalSymposium on Theoretical Aspects of Computer Science (STACS), pp. 209-220, (2013).

11. Czap L., Prabhakaran V.M., Fragouli C. and Diggavi S., Broadcasting Private Messages Securely, IEEEInternational Symposium on Information Theory (ISIT), (2012).

12. Czap L., Prabhakaran V.M., Fragouli C. and Diggavi S. On Interactive Message Secrecy Over ErasureNetworks, International Symposium on Communications, Control, and Signal Processing (ISCCSP),(2012).

13. Das S., Sharma N. and Muthukrishnan S., On Some Special Cases of the Entropy Photon-Number Inequal-ity, Proceedings of Theory of Quantum Computation, Communication, and Cryptography (TQC),Tokyo, Japan, May 2012, also in LNCS 7582, pp. 116-127, (2013).

14. Dutta C. and Radhakrishnan J., More on a Problem of Zarankiewicz, ISAAC 2012, pp. 257-266, (2012).

15. Disser Y., Ghosh S.K., Mihalak M. and Widmayer P., Mapping a Polygon with Holes Using a Compass,Proceedings of the Eight International Symposium on Algorithms for Sensor Systems, Wireless AdHoc Networks and Autonomous Mobile Entities, LNCS 7718, pp. 78-89, Springer, (2012).

16. Joshi S.B. and Shyamasundar R.K., A New Method of MHP Analysis for Languages with Dynamic Barriers,IPDPS Workshop on High-Level Parallel Programming Models and Supportive Environments (HIPS),Shangai, pp. 514-521 (2012).

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17. Juneja S.K., Raheja T. and Shimkin N., The Concert Queuing Game with Random Arrival Volume, SixthInternational ICST Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS2012), pp. 317-325, (Best paper award), (2012).

18. Karthyek R.A.M. and Juneja S.K., State-independent Importance Sampling for Estimating Large Devia-tion Probabilities in Heavy-tailed Random Walks, Sixth International ICST Conference on PerformanceEvaluation Methodologies and Tools (VALUETOOLS 2012), pp. 127-135, (2012).

19. Narang A., Shrivastav A. and Shyamasundar R.K., DynamicDistributed Scheduling Algorithm for StateSpace Search„ Europar, (2012).

20. Pandya P.K. and Shah S.S., The Unary Fragments of Metric Interval Temporal Logic: Bounded VersusLower Bound Constraints, 10th International Symposium on Automated Technology for Verificationand Analysis (ATVA 2012), Trivendrum, LNCS 7561, Springer, (2011).

21. Prabhakaran M.M. and Prabhakaran V.M., On Secure Multiparty Sampling For More than Two Parties,IEEE Information Theory Workshop (ITW), (2012).

22. Prabhakaran V.M. and Prabhakaran M.M., Bounds for Secure Two-Party Sampling from a Generalizationof Common Information, International Conference on Information Theoretic Security (ICITS), (2012).

23. Raja N., Circularity, Paradoxes, and Proofs, Arnold Beckmann and Benedict Löwe (Editors), Proceedingsof Semantics and Syntax: A legacy of Alan Turing, Isaac Newton Institute for the MathematicalSciences (2013).

24. Raja N., Higher-dimensional Type Theory, Proceedings of Sixteenth Conference on Applications of Al-gebra in Logic and Computer Science, Zakopane, Poland (2013).

25. Raja N., Computer-Assisted Mathematics, Proceedings of Fourth World Congress on Universal Logic(UNILOG’13), Rio de Janeiro, Brazil (2013).

26. Reijsbergen D.P., de Boer, P.T., Scheinhardt W.R.W. and Juneja S.K., Some Advances in Importance Sam-pling of Reliability Models Based on Zero Variance Approximation, Proceedings of the Ninth InternationalWorkshop on Rare Event Simulation, RESIM 2012, Trondheim, Norway. pp. 30-35, (2012).

27. Saeedi S., Prabhakaran V.M. and Diggavi S., On Multicasting Nested Message Sets Over CombinationNetworks, ITW, (2012).

28. Saeedi S., Prabhakaran V.M., and Diggavi S., Is Non-unique Decoding Necessary?, ISIT, (2012).

29. Sen, P.G.D., Achieving the Han-Kobayashi Inner Bound for the Quantum Interference Channel, Proceedingsof the IEEE International Symposium on Information Theory, pp. 736-740, (2012).

30. Shah T. and Dabeer O., Subcarrier Power Allocation in OFDM with Low Precision ADC at Receiver,IEEE Vehicular Technology Conference (VTC Fall), Quebec City, Canada, (Digital Object Identifier:10.1109/VTCFall.2012.6399178), (2012).

31. Sharma N. and Warsi N.A., Non-Asymptotic Information-Theoretic Bounds for Some Multi-Party Scenar-ios, Proceedings of Allerton Conference Communication Control, Computing, Monticello, IL, USA,(2012).

32. Vaze R. and Gupta P., Bounds on Minimum Number of Anchors for Iterative Localization and its Connectionsto Bootstrap Percolation, invited paper, IEEE SPCOM 2012, IISc. Bangalore, (2012).

33. R.K. Shyamasundar., Data Intensive Computing: A Technology Challenger and A Computing ParadigmChanger, IEEE IPDPS Workshop on High Performance Data Intensive Computing (HPDIC), Shangai,(2012).

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Edited Volumes

1. S.K. Ghosh and T. Tokuyama (editors), WALCOM: Algorithms and Computation, Proceedings of theSeventh International Workshop on Algorithms and Computations, LNCS 7748, Springer (2013).

Book Chapters

1. Vishwas Patil, R.K. Shyamasundar and Alessandro Mei, Security and Privacy in a Collaborative Envi-ronment: PKI and Access Control, VDM Verlag, November 2012.

2. Davide Frey, Michel Raynal, R.K. Shyamasundar et al., Distributed Computing and Networking, Pro-ceedings of ICDCN 2013, LNCS 7730, Springer Verlag, January 2013.

3. T.S. Deepthi, R.K. Shyamasundar and N. Gopalakrishna Kini, May-Happen-in-Parallel Analysis of JavaPrograms, LAP, February 2013.

4. P.K. Pandya and P.V. Suman An Introduction to Timed Automata, in Modern Applications of AutomataTheory, IISc Research Monograph Series, World Scientific (2012).

5. Ajesh Babu and P.K. Pandya, Chop Expressions and Discrete Duration Calculus, Modern Applications ofAutomata Theory, IISc Research Monograph Series, World Scientific (2012).

In 2013-14

Journals

1. Ada A., Chattopadhyay A., Cook S.A., Fontes L., Koucky M. and Pitassi T., The Hardness of BeingPrivate, ACM Transactions on Computation Theory (TOCT), 6(1), (2014).

2. Agarwal A., Dey S. and Juneja S., Efficient Simulation of Large Deviation Events for Sums of RandomVectors Using Saddle-point Representations, Journal of Applied Probability, 50(3), pp. 703-720, (2013).

?3. Belkin M., Narayanan H. and Niyogi P., Heat Flow and a Faster Algorithm to Compute the Surface Areaof a Convex Body, Random Structures and Algorithms, 43(4), pp. 407-428, 2013.

4. Bondale N., Kimbahune S. and Pande A., mHEALTH-PHC: An ICT Tool for Primary Healthcare in India,IEEE Technology and Society Magazine, 32(3), (2013).

5. Babu A., Limaye N., Radhakrishnan J. and Varma G., Streaming Algorithms for Language RecognitionProblems, Theoretical Computer Science, 494, pp. 13-23, (2013).

6. Babu A., Radhakrishnan J., An entropy-based proof for the Moore bound for irregular graphs. Perspectivesin Computational Complexity: The Somenath Biswas Anniversary Volume, Progess in ComputerScience and Applied Logic (vol 26) (M. Agrawal and V. Arvind, Eds), Birkhäuser, 173–182, 2014.

7. Chattopadhyay A., Toran J. and Wagner F., Graph Isomorphism is not AC0-reducible to Group Isomor-phism, ACM Transactions on Computation Theory (TOCT), 5(4), (2013).

?8. De A., Kurur P. P., Saha C., and Saptharishi R. Fast integer multiplication using modular arithmetic.SIAM J. Comput., 42(2):685–699, 2013.

9. Dinur I. and Harsha P., Composition of Low-error 2-query PCPs Using Decodable PCPs, SIAM Journal ofComputing, 42(6), pp. 2452-2486, (special issue for FOCS 2009; Preliminary version in 51st FOCS,2009), (2013).

10. Ghosh S.K. and Haxell P.E., Packing and Covering Tetrahedra, Discrete Applied Mathematics, 16(9), pp.1209-1215, (2013).

11. Ghosh S.K. and Goswami P.P., Unsolved Problems in Visibility Graphs of Points, Segments and Polygons,

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ACM Computing Surveys, 46(2), pp. 22:1-22:29, (2013).

12. Ghosh S.K., Guest Editorial: Selected Papers from WALCOM 2013, Journal of Graph Algorithms andApplications, 17(6), pp. 597-598, (2013).

13. Glynn P. W. and Juneja S., Asymptotic Simulation Efficiency Based on Large Deviations, ACM Transactionson Modeling and Computer Simulation (TOMACS), 23 (3), (2013).

14. Harsha P., Klivans A. and Meka R., Bounding the Sensitivity of Polynomial Threshold Functions, Theoryof Computing, 10(1), pp. 1-24, (special Issue on Analysis of Boolean Functions) (2014).

15. Juneja S. and Mandjes M., Overlap Problems on the Circle, Advances in Applied Probability, 45(3), pp,773-790, doi:10.1239/aap/1377868538, (2013).

16. Kavitha T., A Size-popularity Tradeoff in the Stable Marriage Problem, SIAM Journal on Computing, 43(1),pp. 52-71, (2014).

17. Khan A., Bhattacharjee A.K. and Shyamasundar R.K., Trends in the Design of End-to-End Secure Systems:A Perspective, CSI Journal of Computing, 2(1-2), pp. 1:1-10, (2013).

18. Maraninchi, F., Halbwachs N., Raymond P., Parent C. and Shyamasundar R.K., Specification and Val-idation of Embedded Systems: A Case Study of a Fault-Tolerant Data Acquisition System with Lustre Pro-gramming Environment, CSI Journal of Computing, 4(7), pp. 69-85, (2013).

?19. Saha C., Saptharishi R., and Saxena N. A case of depth-3 identity testing, sparse factorization and duality.Computational Complexity, 22(1):39–69, 2013.

20. Shekhar S., Ghosh R.K. and Shyamasundar R.K., Postorder Based Routing and Transport Protocol forWSNs, Journal of Pervasive and Mobile Computing Journal, 11, pp. 229-243, (2014).

21. Vaze R., Garg R. and Pathak N., Dynamic Power Allocation For Maximizing Throughput in Energy Har-vesting Communication System, IEEE/ACM Transaction on Networking, 99, (2013).

22. Vaze R. and Heath R.W. Jr., Cascaded Orthogonal Space-Time Block Codes for Wireless Multi-Hop RelayNetworks, Eurasip Journal on Wireless Communications and Networking, (2013).

23. Wu R., Prabhakaran V., Viswanath P. and Wang Y., Interference Channels With Half-Duplex SourceCooperation, IEEE Transactions on Information Theory, 60(3), pp. 1753-1781, (2014).


?1. Barman S., Bhaskar U., Echenique F. and Wierman A., The Empirical Implications of Rank in BimatrixGames, Electronic Commerce (EC), pp. 55-72, 2013.

2. Bondale N., Surve V., Nadkarni M., Parkhi O., Joshi P. and Pandey A., Issues in Developing Pronuncia-tion Lexicon for Marathi, Proceedings of O-COCOSDA 2013, KIIT, New Delhi (2013).

3. Agarwal A. and Juneja S., Comparing Optimal Convergence Rate of Stochastic Mesh and Least SquaresMethod for Bermudan Option Pricing, Proceedings of the 2013 Winter Simulation Conference, IEEEPress, pp. 701-712, (2013).

4. Belachew M. and Shyamasundar R.K., Public Private Partnerships (PPP) in E-Government Initiatives forDeveloping Nations: The Case of Ethiopia, Proceedings ICGEV’2013, (2013).

5. Bidokhti S., Prabhakaran V. and Diggavi S., A Block Markov Encoding Scheme for Broadcasting NestedMessage Sets, ISIT, (2013).

6. Chattopadhyay A., Grenet B., Koiran P., Portier N. and Strozecki Y., Factoring Bivariate LacunaryPolynomials Without Heights, Proceedings of International Symposium on Symbolic and AlgebraicComputation (ISSAC), Boston, USA, pp. 141-148, (2013).

7. Czap L., Fragouli C., Prabhakaran V, and Diggavi S., Secure Network Coding with Erasures and Feedback,

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Allerton Conference on Communication, Control and Computing, (2013).

8. Czap L., Prabhakaran V., Diggavi S. and Fragouli C., Exploiting Common Randomness: A Resource forNetwork Secrecy, IEEE Information Theory Workshop (ITW), (2013).

9. Czap L., Prabhakaran V., Diggavi S. and Fragouli C., Securing Broadcast Against Dishonest Receivers,International Symposium on Network Coding (NETCOD), (2013).

10. Data D. and Prabhakaran V., Communication Requirements for Secure Computation, Allerton Conferenceon Communication, Control and Computing, (2013).

11. Ghosh S.K. and Roy B., Problems on Plane Triangulations of Points, (Survey Paper), Proceedings ofIndia-Taiwan Conference on Discrete Mathematics, Hsinchu, Taiwan, pp. 57-60, (2013).

12. Ghosh S.K. and Roy B., Some Results on Point Visibility Graphs, Proceedings of the 8th InternationalWorkshop on Algorithms and Computations, Chennai, LNCS 8344, pp. 163-175, Springer, (2014).

?13. Gupta A., Kamath P., Kayal N., and Saptharishi R. Approaching the chasm at depth four. In Proceedingsof the 28th Conference on Computational Complexity, CCC, pages 65–73, 2013.

?14. Gupta A., Kamath P., Kayal N., and Saptharishi R. Arithmetic circuits: A chasm at depth three. In 54thAnnual IEEE Symposium on Foundations of Computer Science, FOCS, pages 578–587 2013.

15. Harsha P. and Jain R., A Strong Direct Product Theorem for the Tribes Function Via the Smooth-rectangleBound, in Anil Seth and Nisheeth K. Vishnoi, editors, Proceedings of the IARCS Annual Conferenceon Foundations of Software Technology and Theoretical Computer Science (FSTTCS), 24 of LeibnizInternational Proceedings in Informatics, pp. 141-152, Schloss Dagstuhl, (2013).

16. Huang C.-C., Kavitha T., Mehlhorn K. and Michail D., Fair Matchings and Related Problems, Proceed-ings of the 33rd IARCS Annual Conference on Foundations of Software Technology and TheoreticalComputer Science (FSTTCS), pp. 339-350, (2013).

17. Kavitha T. and Varma N.M., Small Stretch Pairwise Spanners, Proceedings of the 40th InternationalColloquium on Automata, Languages, and Programming (ICALP), pp. 601-612, (2013).

18. Kumar A. and Prabhakaran V., Estimation of Bandlimited Signals from the Signs of Noisy Samples, IEEEInternational Conference on Acoustics, Speech, and Signal Processing (ICASSP), (2013).

19. Martens J., Chattopadhyay A., Pitassi T. and Zemel R., On the Expressive Power of Restricted BoltzmannMachines, Proceedings of 27th Annual Conference on Neural Information Processing Systems (NIPS),Lake Tahoe, Nevada, USA, pp. 2877-2885, (2013).

20. Mishra S., Fragouli C., Prabhakaran V. and Diggavi S., Using Feedback for Secrecy Over Graphs, ISIT,(2013).

21. Moka S.B and Juneja S., Regenerative Simulation for Multiclass Open Queuing Networks, Proceedings of2013 Winter Simulation Conference, IEEE Press, pp. 643-654, (2013).

22. Murthy K.R., Juneja S. and Blanchet J., Optimal Rare Event Monte Carlo for Markov Modulated RegularlyVarying Random Walks, Proceedings of the 2013 Winter Simulation Conference, IEEE Press, pp. 564-576, (2013).

23. Narang A., Shrivastav A. and Shyamasundar R.K., High Performance Adaptive Distributed SchedulingAlgorithm, IEEE PDPS 2013 Workshop on Large-Scale Parallel Processing (LSPP), (2013).

24. Narang A. and Shyamasundar R.K., Online Distributed Scheduling for Parallel Computations, The Societyfor Modeling and Simulation International (SCS) Conference, San Diego, USA, (2013).

25. Pananjady A., Bagaria V. and Vaze R., Maximizing Utility Among Selfish Users in Social Groups, Pro-ceedings of the National Conference on Communication, Indian Institute of Technology, Kanpur(2014).

26. Pandya P.K. and Shah S.S., Deterministic Logics for UL, 10th International Colloquium on Theoretical

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Aspects of Computing (ICTAC 2013), Shanghai, LNCS 8049, Springer, (2013).

27. Prabhakaran V. and Sarwate A., Assisted Sampling of Correlated Sources, IEEE International Symposiumon Information Theory (ISIT), (2013).

28. Santhana B.K., Vaze R. and Manjunath D., On White-Space Detection, Localization and Coverage, Pro-ceedings of the National Conference on Communication, Indian Institute of Technology, Kanpur(2014).

29. Raja N., Description Logics and Web Ontology, Proceedings of Semantic Web - Ontology Languagesand Their Use, Dresden, Germany (2013).

30. Raja N., Computer-Assisted Proofs, Proceedings of the Thirteenth Asian Logic Conference (ALC’13),Guangzhou, China (2013).

31. Raja N., Software Tools for Formal Proofs, Proceedings of Mathematics for Scientific Programming,Oberwolfach, Germany (2013).

32. Sharma N., A Gambling Interpretation of Some Quantum Information-theoretic Quantities, Asian QuantumInformation Science (AQIS), Chennai, (2013).

33. Sharma N., Equality Conditions for Quantum Quasi-Entropies Under Monotonicity and Joint-Convexity,National Conference on Communications (NCC), Kanpur, (2014).

34. Shyamasundar R.K., Security and Protection of SCADA: A Bigdata Algorithmic Approach, Proceedings of7th ACM Security in Information and Networks (SIN 2013), 2013.

?35. Sinclair A. and Srivastava P., Lee-Yang Theorems and the Complexity of Computing Averages, Proceedingsof the ACM Symposium on Theory of Computing (STOC), pp. 625-634, Presented as an invitedtutorial at the IEEE FOCS Workshop on “Zeros of Polynomials and their Applications”, 2013.

?36. Sinclair A., Srivastava P. and Yin Y., Spatial Mixing and Approximation Algorithms for Graphs withBounded Connective Constant, Proceedings of the IEEE Symposium on the Foundations of ComputerScience (FOCS), pp. 300-309, 2013.

37. Vaze R., Transmission Capacity of Wireless Ad Hoc Networks With Energy Harvesting Nodes, Proceedingsof IEEE Globalsip, Austin, Texas, USA (2013).

38. Vaze R., Online Power Allocation For Maximizing Mutual Information in Cognitive Radio System, Proceed-ings of IEEE Wireless Communications and Networking Conference (WCNC), Shanghai (2013).

39. Vaze R., Competitive Ratio Analysis of Online Algorithms to Minimize Data Transmission Time in EnergyHarvesting Communication System, Proceedings of the IEEE International Conference on ComputerCommunications (IEEE INFOCOM 2013), Turin (2013).

Invited Paper

1. R. Vaze and Murthy C.R., On Whitespace Identification Using Randomly Deployed Sensors, Proceedingsof the IEEE Comsnets, Bangalore (2014).

Book Chapters

1. T.S. Deepthi, R.K. Shyamasundar and N.G.Kini, May-Happen-in-Parallel Analysis of Java Programs, LAP,December 2013.

2. N. Raja (Editor), Distributed Computing and Internet Technology, Lecture Notes in Computer Science,8337, ISBN: 978-3-319-04482-8, Springer-Verlag (2014).

3. N. Bondale, S. Kimbahune and A. Pande, Rural Community Health in India: Problems & Solutions, inthe book “Mobile Health (mHealth) - Multidisciplinary Verticals”, under publication by CRC Press.

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Special Issues/Sessions

1. R.K. Shyamasundar, Cyber Security and Privacy, Special Session at the 79th Annual Meeting of IASc,November 2013.

In 2014-15

Journals

1. Agarwal A. and Juneja S., Nearest Neighbor Based Estimation Technique for Pricing Bermudan Options,International Game Theory Review, 17 (1), 1540002 (31 pages), 2015.

2. Anderson D.F., Craciun G., Gopalkrishnan M. and Wiuf C., Lyapunov Functions, Stationary Distribu-tions, and Non-equilibrium Potential for Chemical Reaction Networks, Bulletin of Mathematical Biology,15 pages + 5 pages SI.

3. Bai T., Vaze R. and Heath, R.W., Analysis of Blockage Effects on Urban Cellular Networks, IEEE Transac-tions on Wireless Communications, 13 (9), pp. 5070-5083, 2014.

4. Bidokhti S. and Prabhakaran V., Is Non-unique Decoding Necessary?, IEEE Transactions on InformationTheory, 60(5), pp. 2594-2610, May 2014.

5. Chattopadhyay A., Gavalda R., Hansen K.A. and Therien D., Learning Read-constant Polynomials ofConstant Degree Modulo Composites, Theory of Computing Systems, 55 (2), pp. 404-420, 2014.

6. Deshpande A. and Gopalkrishnan M., Autocatalysis in Reaction Networks, Bulletin of MathematicalBiology, 76 (10), 26 pages, October 2014.

7. Dey S., Juneja S. and Murthy K.R.A., Incorporating Views on Marginal Distributions in the Calibration ofRisk Models, Operations Research Letters, 43 (1), pp. 46-51, 2015.item Disser Y., Ghosh S.K., Mihalák M. and Widmayer P.„ Mapping a Polygon with Holes Using aCompass, Theoretical Computer Science, 553, pp. 106-113, 2014.

8. Garg M. and Radhakrishnan J., Set Membership with a Few Bit Probes, ACM Symposium on DiscreteAlgorithms, pp. 776-784, January 2015.

9. Ghosh S.K. and Tokuyama T., Guest Editorial: Special Issue on Algorithms and Computation, TheoreticalComputer Science, 555, pp. 1, 2014.

10. Ghosh S.K. and Roy B., Some Results on Point Visibility Graphs, Theoretical Computer Science, 575, pp.17-32, 2015.

11. Gopalkrishnan M., Miller E. and Shiu A., A Geometric Approach to the Global Attractor Conjecture, SIAMJournal on Applied Dynamical Systems, 13 (2), 40 pages, 2014.

?12. Gupta A., Kamath P., Kayal N., and Saptharishi R. Approaching the chasm at depth four. J. ACM,61(6):33:1–33:16, 2014.

13. Hong J., Juneja S. and Luo J., Estimating Sensitivities of Portfolio Credit Risk Using Monte Carlo, IN-FORMS Journal of Computing, 26 (4), pp. 848-865, 2014.

14. Iyer S. and Vaze R., Percolation on the Information Theoretically Secure Signal to Interference Ratio Graph,Journal of Applied Probability, 51 (4), December 2014.

15. Juneja S. and Raheja T., The Concert Queueing Game: Fluid Regime with Random Order Service, Interna-tional Game Theory Review, 17 (2), 1540012 (15 pages), 2015.

16. Kavitha T., Dynamic Matrix Rank with Partial Lookahead, Theory of Computing Systems, 55, pp. 229-249, 2014.

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17. Kavitha T., Nasre M. and Nimbhorkar P., Popularity at Minimum Cost, Journal of Combinatorial Opti-mization, 27 (1), pp. 574-596, 2014.

18. Murthy K.R.A., Juneja S. and Blanchet J., State-independent Importance Sampling for Random Walks withRegularly Varying Increments, Stochastic Systems, 4 (2), pp. 321-374, 2014.

?19. Narayanan H. and Niyogi P., Language Evolution, Coalescent Processes and the Consensus Problem on aSocial Network, Journal of Mathematical Psychology, 61, pp. 19-24, 2014.

20. Narendra Kumar N.V. and Shyamasundar R.K., Towards An Executable Declarative Specification of AccessControl, CSI Journal of Computing, 2 (4), pp. 55-65, 2015.

21. Prabhakaran V. and Prabhakaran M., Assisted Common Information with an Application to Secure Two-Party Sampling, IEEE Transactions on Information Theory, 60(6), pp. 3413-3434, June 2014.

22. Shekhar S., Ghosh R.K. and Shyamasundar R.K., Postorder Based Routing and Transport Protocol forWSNs, Special issue of Pervasive and Mobile Computing Journal, (11), pp. 229-243, 2014.

23. Shyamasundar R.K., The Computing Legacy of Alan M. Turing, Current Science, 102 (12), June 2014.

?24. Sinclair A. and Srivastava P., Lee-Yang Theorems and the Complexity of Computing Averages, Communi-cations in Mathematical Physics, 329(3), pp. 827–858, 2014, Extended abstract in Proceedings of ACMSTOC, 2013.

?25. Sinclair A., Srivastava P. and Thurley M., Approximation Algorithms for Two-state Anti-ferromagnetic SpinSystems on Bounded Degree Graphs, Journal of Statistical Physics, 155(4), pp. 666–686, 2014, Extendedabstract in Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA), 2012.

26. Thangaraj A. and Vaze R., Online Algorithms for Basestation Allocation, IEEE Transactions on WirelessCommunications, 13 (5), pp. 2966-2975, 2014.


1. Ajaykrishnan N., Prem N., Prabhakaran V. and Vaze R., Critical Database Size for Effective Caching,National Conference on Communications (NCC), 2015.

2. Baertschi A., Tschager T., Ghosh S.K., Mihalák M. and Widmayer P., Improved Bounds for the Conflict-free Chromatic Art Gallery Problem, Proceedings of the 30th ACM Annual Symposium on Computa-tional Geometry, Japan, pp. 144-153, 2014.

?3. Barman S., Bhaskar U., Echenique F. and Wierman A., On the Existence of Low-Rank Explanations forMixed Strategy Behavior, Web and Internet Economics (WINE), pp. 447-452, 2014.

?4. Bhaskar U., Ligett K. and Schulman L.J., Network Improvement for Equilibrium Routing, Integer Pro-gramming and Combinatorial Optimization (IPCO), pp. 138-149, 2014.

?5. Bhaskar U., Ligett K., Schulman L.J. and Swamy C., Achieving Target Equilibria in Network RoutingGames Without Knowing the Latency Functions, Foundations of Computer Science (FOCS), pp. 31-40,2014.

6. Bhattacharya P., Ghosh S.K. and Roy B., Vertex Guarding in Weak Visibility Polygons, Proceedings ofthe 1st International Conference on Algorithms and Discrete Applied Mathematics, Kanpur, LNCS,8959, pp. 45-57, Springer, 2015.

7. Budkuley A., Dey B. and Prabhakaran V., Correlated Jamming in a Joint Source Channel CommunicationSystem, IEEE International Symposium on Information Theory (ISIT), July 2014.

8. Budkuley A., Dey B. and Prabhakaran V., Writing on a Dirty Paper in the Presence of Jamming, IEEEInternational Symposium on Information Theory (ISIT), July 2014.

9. Chattopadhyay A. and Saks M.E., The Power of Super-logarithmic Number of Players, Proceedings ofthe Approximation, Randomization, and Combinatorial Optimization, Algorithms and Techniques

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(APPROX/RANDOM), pp. 596-603, Barcelona, Spain, September 04-06, 2014.

10. Chattopadhyay A., Radhakrishnan J. and Rudra A., Topology Matters in Communication, Proceedingsof the 55th IEEE Annual Symposium on Foundations of Computer Science (FOCS), Philadelphia,USA, pp. 631-640, October 18–21, 2014.

11. Chattopadhyay A. and Mukhopadhyay S., Tribes is Hard in the Message Passing Model, Proceedings ofthe 32nd International Symposium on Theoretical Aspects of Computer Science (STACS), Garching,Germany, pp. 224-237, March 04-07, 2015.

12. Czap L., Prabhakaran V., Diggavi S. and Fragouli C., Triangle Network Secrecy, IEEE InternationalSymposium on Information Theory (ISIT), July 2014.

13. Data D., Dey B., Mishra M. and Prabhakaran V., How to Securely Compute the Modulo-two Sum of BinarySources, IEEE Information Theory Workshop (ITW), Hobart, November 2014.

14. Data D., Prabhakaran M. and Prabhakaran V., On the Communication Complexity of Secure Computation,CRYPTO 2014, 34th Annual Cryptology Conference, August, 2014.

15. Dinur I., Harsha P., Srinivasan S. and Varma G., Derandomized Graph Product Results Using the LowDegree Long Code, in Ernst W. Mayr and Nicolas Ollinger, editors, Proceedings of the 32nd AnnualSymposium on Theoretical Aspects of Computer Science (STACS), 30 of Leibniz International Proceed-ings in Informatics, pp. 275-287, Schloss Dagstuhl, 2015.

16. Doshi J. and Vaze R., Long Term Throughput and Approximate Capacity of Transmitter-Receiver EnergyHarvesting Channel with Fading, Proceedings of IEEE ICCS 2014, Macau (Invited).

?17. Forbes M., Saptharishi R., and Shpilka A. Hitting sets for multilinear read-once algebraic branching pro-grams, in any order. Symposium on Theory of Computing, STOC, pages 867–875, 2014.

18. Gopalkrishnan M., A Cost/ Speed/ Reliability Trade-off in Erasing a Bit, 14th International Conference onUnconventional Computation and Natural Computation, 8 pages.

19. Gopalkrishnan M., On the Lyapunov Function for Complex-balanced Mass-action Systems, 21st Interna-tional Symposium on Mathematical Theory of Networks and Systems (MTNS 2014).

20. Guruswami V., Harsha P., Håstad J., Srinivasan S. and Varma G., Super-polylogarithmic HypergraphColoring Hardness via low-degree long codes, Proceedings of the 46th ACM Symposium on Theory ofComputing (STOC), pp. 614–623, 2014.

21. Huang C.-C. and Kavitha T., An Improved Algorithm for the Stable-Marriage Problem with One-SidedTies, Proceedings of the 17th International Conference on Integer Programming and CombinatorialOptimization (IPCO), pp. 297-308, 2014.

22. Kavitha T., New Pairwise Spanners, Proceedings of the 32nd International Symposium on TheoreticalAspects of Computer Science (STACS), pp. 513-526, 2015.

?23. Kayal N., Saha C., and Saptharishi R. A super-polynomial lower bound for regular arithmetic formulas.Symposium on Theory of Computing, STOC, pages 146–153, 2014.

24. Madnani K., Krishna S.N. and Pandya P.K., On Unary Fragments of MTL and TPTL Over Timed Words,Theoretical Aspects of Computing (ICTAC 2014), LNCS, Springer, 2014.

25. Madnani K., Krishna S.N. and Pandya P.K., Partially Punctual Metric Temporal Logic is Decidable, 21stInternational Symposium on Temporal Representation and Reasoning (TIME 2014), 2014.

26. Mishra M., Dey B., Prabhakaran V. and Diggavi S., On the Oblivious Transfer Capacity Region of theBinary Erasure Broadcast Channel, IEEE Information Theory Workshop (ITW), Hobart, November 2014.

27. Mishra M., Dey B., Prabhakaran V. and Diggavi S., The Oblivious Transfer Capacity of the WiretappedBinary Erasure Channel, IEEE International Symposium on Information Theory (ISIT), July 2014.

?28. Narayanan H., Estimating Deep Littlewood-Richardson Coefficients, 26th International Conference on

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Formal Power Series and Algebraic Combinatorics (FPSAC), 2014.

29. Narendra Kumar N.V. and Shyamasundar R.K., Realizing Purpose-Based Privacy Policies Succinctly viaInformation-Flow Labels, 3rd IEEE International Symposium on Privacy and Security in Cloud and BigData, (PriSec 2014) , Sydney, December 2014.

30. Prakash A. and Shyamasundar R.K., Information System Security, Proceedings of the 10th InternationalConference on ICISS 2014, LNCS 8880, December 2014.

31. Radhakrishnan J., Sen P. and Warsi N., One-shot Marton Inner Bound for Classical-quantum BroadcastChannel, (Poster) 18th Conference on Quantum Information Processing, January 2015.

32. Raja N., Novel Abstract Machines for the Geometry of Interaction, Proceedings of the Conference onSemantics of Proofs and Programs, Paris, France (2014).

33. Raja N., Judgment Aggregation using Non-classical Logics, Proceedings of the Conference on Logics forSocial Behaviour, Leiden, The Netherlands (2014).

34. Raja N., Tailoring Description Logics for Specifying Web Ontologies, Proceedings of the Nineteeth Con-ference on Applications of Algebra in Logic and Computer Science, Zakopane, Poland (2015).

35. Rao S. and Prabhakaran V., A New Upperbound for the Oblivious Transfer Capacity of Discrete MemorylessChannels, IEEE Information Theory Workshop (ITW), Hobart, November 2014.

?36. Sinclair A., Srivastava P., Štefankovic D. and Yin Y., Spatial Mixing and the Connective Constant: OptimalBounds, Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 1549-1563,2015.

37. Vaze R. and Jagannathan K., Finite-Horizon Optimal Transmission Policies for Energy Harvesting Sensors,Proceedings of IEEE ICASSP 2014, Florence Italy.

Books

1. N. Raja, G. Barua and M.R. Patra (editors), Distributed Computing and Internet Technology, LectureNotes in Computer Science, Vol. 8956, ISBN: 978-3-319-14976-9, Springer-Verlag (2015).

2. R. Vaze, Random Wireless Networks, Cambridge University Press, 2015.

Book Chapters

1. Nandini Bondale, Sanjay Kimbahune and Arun Pande, Rural Community Health in India: Problems &Solutions, in the book Mobile Health (mHealth) - Multidisciplinary Verticals’, CRC Press, November2014.

Surveys

?1. Saptharishi R. Recent progress on arithmetic circuit lower bounds. Bulletin of the EATCS, 114, 2014.

In 2015-16

Journals

1. Ada A., Chattopadhyay A., Fawzi O. and Nguyen P., The NOF Multiparty Communication Complexityof Composed Functions, Computational Complexity, 24(3), pp. 645–694, 2015.

2. Agarwal A., Juneja S. and Sircar R., American Options under Stochastic Volatility: Control Variates,

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Maturity Randomization & Multiscale Asymptotics, Quantitative Finance, 16(1), pp. 17-30, 2016.

3. Aggarwal S., Kuri J. and Vaze R., Social Optimum in Social Groups with Give-and-Take Criterion, Inter-national Journal of Communication Systems, pp. 1099-1131, 2015.

4. Bhaskar U., Fleischer L. and Anshelevich E., A Stackelberg Strategy for Routing Flow Over Time. Gamesand Economic Behavior 92, pp. 232-247, 2015.

5. Bhaskar U., Fleischer L., Hoy D. and Huang C-C., On the Uniqueness of Equilibrium in Atomic SplittableRouting Games, Mathematics of Operations Research, 40(3), pp. 634-654, 2015.

6. Czap L., Prabhakaran V., Fragouli C. Diggavi S., Secret Communication Over Broadcast Erasure Channelswith State-feedback, IEEE Transactions on Information Theor, 61(9), pp. 4788–4808, 2015.

7. Czap L., Fragouli C., Prabhakaran V. and Diggavi S., Secure Network Coding With Erasures and Feedback,IEEE Transactions on Information Theory, 61(4), pp. 1667–1686, 2015.

8. Foss S., Juneja S., Mandjes M.R.H. and Moka S.B., Spatial Loss Systems: Exact Simulation and Rare EventBehavior, SIGMETRICS Performance Evaluation Review, 43(2), pp.3-6, 2015.

9. Fragouli C., Prabhakaran V., Czap L. and Diggavi S., Wireless Network Security: Building on Erasures,Proceedings of the IEEE, 103(10), pp. 1826–1840, 2015.

10. Giacobbe M., Guet C., Gupta A., Henzinger T., Paixao T. and Petrov T., Model Checking Evolution ofGene Regulatory Networks, TACAS’15, Special Issue of Acta Informatica.

?11. Harman G., Kulkarni S. and Narayanan H., sin(ωx) can Approximate Almost Every Finite Set of Samples,Constructive Approximation, 42(2), pp. 303-311, 2015.

12. Huang C.-C., Kavitha T., Mehlhorn K. and Michail D., Fair Matchings and Related Problems, Algorith-mica, 74(3), pp. 1184-1203, 2016.

13. Huang C.-C. and Kavitha T., Improved Approximation Algorithms for Two Variants of the Stable MarriageProblem with Ties, Mathematical Programming, 154(1-2), pp. 353-380, 2015.

14. Iyer S.K., Vaze R. and Narasimha D., Autoregressive Cascades on Random Networks, Physica A: StatisticalMechanics and its Applications, 447, pp. 345-354, 2016.

15. Kavitha T. and Varma N.M., Small Stretch Pairwise Spanners and Approximate D-Preservers, SIAM Jour-nal on Discrete Mathematics, 29(1), pp. 2239-2254, 2015.

16. Kiran T., Thangaraj A. and Vaze R.,Combinatorial Resource Allocation Using Sub-modularity of Waterfill-ing, IEEE Transactions on Wireless Communications, 15(1), pp. 206-216, 2016.

17. Moka S.B. and Juneja S., Regenerative Simulation for Queueing Networks with Exponential or Heavier TailArrival Distributions, ACM Transactions on Modeling and Computer Simulation (TOMACS), 25(4),pp. 1-22, 2015.

18. Radhakrishnan J., Sen P. and Warsi N., One-Shot Marton Inner Bound for Classical-Quantum BroadcastChannel, IEEE Transactions on Information Theory, 62(5), pp. 2836-2848, 2016.


1. Bagaria V., Pananjady A. and Vaze R., The Online Disjoint Set Cover Problem and its Applications, Pro-ceedings of the IEEE INFOCOM, Hong Kong, 2015.

?2. Belloni A., Liang T., Narayanan H. and Rakhlin A., Escaping the Local Minima via Simulated Annealing:Optimization of Approximately Convex Functions, 28th Annual Conference on Learning Theory (COLT),2015.

3. Bhangale A., Harsha P. and Varma G., A Characterization of Hard-to-cover CSPs, Proceedings of the30th Computational Complexity Conference, pp. 280–303, 2015.

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4. Bhangale A., Saptharishi R., Varma G. and Venkat R., On Fortification of Projection Games, In NaveenGarg, Klaus Jansen, Anup Rao, and José D. P. Rolim, editors, Proceedings of the 19th InternationalWorkshop on Randomization and Computation (RANDOM), 40, LIPIcs, Schloss Dagstuhl, pp. 497–511, 2015.

5. Bhattacharya S., Hoefer M., Huang C.-C., Kavitha T. and Wagner L., Maintaining Near-Popular Match-ings, Proceedings of the 42nd International Colloquium on Automata, Languages, and Programming(ICALP), Part II, pp. 504-515, 2015.

6. Budkuley A., Dey B. and Prabhakaran V., Dirty Paper Arbitrarily Varying Channel with a State-AwareAdversary, Proceedings of the IEEE Information Theory Workshop (ITW), Jeju, 2015.

7. Chattopadhyay A. and Rudra A., The Range of Topological Effects on Communication, Proceedings of42nd International Colloquium on Automata, Languages and Programming (ICALP), Kyoto, Japan,pp. 540–551, 2015.

8. Cummings R., Ligett K., Radhakrishnan J., Roth A. and Wu Z.S., Coordination Complexity: SmallInformation Coordinating Large Populations, Proceedings of the 2016 ACM Conference on Innovationsin Theoretical Computer Science, ITCS 2016, pp. 281-290, 2016.

9. Cseh Á., Huang C.-C, and Kavitha T., Popular Matchings with Two-Sided Preferences and One-SidedTies, Proceedings of the 42nd International Colloquium on Automata, Languages, and Programming(ICALP), Part I, pp. 367-379, 2015.

10. Daca P., Gupta A. and Henzinger T., Abstraction-driven Concolic Testing, VMCAI 2016.

11. Data D. and Prabhakaran V., On Coding for Secure Computing, Proceedings of the IEEE InternationalSymposium on Information Theory (ISIT), Hong Kong, 2015.

12. Dinur I., Harsha P. and Kindler G., Polynomially Low Error PCPs with Polyloglog n Queries via ModularCcomposition, Proceedings of the 47th ACM Symposium on Theory of Computing (STOC), pp. 267–276, 2015.

13. Iyer S. and Vaze R., Achieving Non-Zero Information Velocity in Wireless Networks, Proceedings of theIEEE SPASWIN, 2015.

14. Kumar A., Pillai S., Vaze R. and Gopalan A., Optimal WiFi Sensing via Dynamic Programming, Proceed-ings of the IEEE RAWNET, Mumbai, 2015.

15. Mishra M., Sharma T., Dey B. and Prabhakaran V., Private Data Transfer over a Broadcast Channel,Proceedings of the IEEE International Symposium on Information Theory (ISIT), Hong Kong, 2015.

16. Mishra M., Dey B., Prabhakaran V. and Diggavi S., On the Oblivious Transfer Capacity of the DegradedWiretapped Binary Erasure Channel, Proceedings of the IEEE International Symposium on InformationTheory (ISIT), Hong Kong, 2015.

17. Mukhopadhyay S. and Sanyal S., Towards Better Separation Between Deterministic and Randomized QueryComplexity, In Prahladh Harsha and G. Ramalingam, editors, Proceedings of the 35th IARCS AnnualConference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS), 45,LIPIcs. Schloss Dagstuhl, pp. 206-220, 2015.

?18. Panageas I., Srivastava P. and Vishnoi N.K., Evolutionary Dynamics in Finite Populations Mix Rapidly,Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 480-497, 2016.

19. Pillai S. and Prabhakaran V., On the Noisy Feedback Capacity of Gaussian Broadcast Channels, Proceedinsof the IEEE Information Theory Workshop (ITW), Jerusalem, 2015.

20. Raja N., Condorcet Paradox and Program Logics, Proceedings of the Fifth World Congress on UniversalLogic (UNILOG’15), Istanbul, Turkey, 2015.

21. Raja N., Judgment Aggregation and Stability of Programs, Proceedings of the Computational Logic and

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Reasoning, Dresden, Germany, 2015.

22. Raja N., On a Correspondence Between Voting theory and Denotational Semantics, Proceedings of theTwentieth Conference on Applications of Algebra in Logic and Computer Science, Zakopane, Poland,2016.

23. Rajakrishnan S., Rajan S. and Prabhakaran V., Lower Bounds for Interactive Function Computation viaWyner Common Information, National Conference on Communications (NCC), 2016.

24. Sanyal S., Near-optimal Upper Bound on Fourier Dimension of Boolean Functions in Terms of Fourier Spar-sity, In Magnús M. Halldórsson, Kazuo Iwama, Naoki Kobayashi, and Bettina Speckmann, edi-tors, Proceedings of the 42nd International Colloquium of Automata, Languages and Programming(ICALP), Part I, 9134, LNCS, Springer, pp. 1035–1045, 2015.

25. Satpathi S., Nagda R. and Vaze R., Optimal Offline and Competitive Online Strategies for Transmitter-Receiver Energy Harvesting, Proceedings of the IEEE International Conference on CommunicationsICC, London, 2015.

?26. Schulman L.J., Sinclair A. and Srivastava P., Symbolic Integration and the Complexity of ComputingAverages, Proceedings of the IEEE Symposium on the Foundations of Computer Science (FOCS), pp.1231-1245, 2015.

27. Sridhar A., Karamchandani N. and Prabhakaran V., Coded Caching in Hybrid Networks, National Con-ference on Communications (NCC), 2016.

28. Subramanian A., Kanth G.S., Moharir S. and Vaze R., Online Incentive Mechanism Design for Smart-phone Crowd-sourcing, Proceedings of the IEEE WIOPT, Mumbai, 2015.

Book chapters

1. Harsha P., Locally Testable Codes. Encyclopedia of Algorithms 2016: 1152–1156.

2. Abhishek Kr. Singh and N. Raja, Separation Logic to Meliorate Software Testing, Trends in SoftwareTesting, ISBN: 978-981-10-1414-7, Springer-Verlag, 2016.

In 2016-17

Journals

?1. Agrawal M., Saha C., Saptharishi R., and Saxena N.. Jacobian hits circuits: Hitting sets, lower bounds fordepth-d occur-k formulas and depth-3 transcendence degree-k circuits. SIAM J. Comput., 45(4):1533–1562,2016.

2. Bidokhti S., Prabhakaran V. and Diggavi S., Capacity Results for Multicasting Nested Message Sets OverCombination Networks, IEEE Transactions on Information Theory, 62(9), pp. 4968-4992, September2016.

3. Chattopadhyay A., Edmonds J., Ellen F. and Pitassi T., Upper and Lower Bounds on the Power of Advice,SIAM Journal of Computing 45(4), pp. 1412-1432, (2016).

4. Chattopadhyay A., Green F. and Straubing H., Circuit Complexity of Powering in Fields of Odd Charac-teristic, Chicago Journal of Theoretical Computer Science (2016).

5. Czap L., Prabhakaran V., Fragouli C. and Diggavi S., An LP Characterization of the Secret-messageCapacity of Three Erasure Networks with Feedback, IEEE Transactions on Information Theory, 62(5), pp.2430-2480, May 2016.

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6. Data D., Prabhakaran V. and Prabhakaran M., Communication and Randomness Lower Bounds for SecureComputation, IEEE Transactions on Information Theory, 62(7), pp. 3901-3929, July 2016.

7. Deshmukh A. and Vaze R., Online Energy-Efficient Packet Scheduling for a Common Deadline With andWithout Energy Harvesting, IEEE Journal on Selected Areas in Communications, bf 34(12), pp. 3661-3674, Dec. 2016.

?8. Fefferman C., Mitter S. and Narayanan H., Testing the Manifold Hypothesis, Journal of the AmericanMathematical Society, 29, pp. 983-1049, 2016.

9. Giacobbe M., Guet C.C., Gupta A., Henzinger T.A., Paixao T. and Petrov T., Model Checking theEvolution of Gene Regulatory Networks, Acta Informatica (2016).

?10. Gupta A., Kamath P., Kayal N., and Saptharishi R. Arithmetic circuits: A chasm at depth 3. SIAM J.Comput., 45(3):1064–1079, 2016.

11. Guruswami V., Harsha P., Håstad J., Srinivasan S, and Varma G., Super-polylogarithmic HypergraphColoring Hardness via Low-degree Long Codes, SIAM Journal of Computing, (Preliminary Version in46th STOC, 2014), 46(1), pp. 132-159, 2017.

?12. Narayanan H., Randomized Interior Point Methods for Sampling and Optimization, Annals of AppliedProbability, 26(1), pp. 597-641, 2016.

13. Iyer S. and Vaze R., Achieving Non-Zero Information Velocity in Wireless Networks, Annals of AppliedProbability, 27(1) , pp. 48-64, 2017.

14. Juneja, S. and Manjunath D., To Lounge or to Queue Up, ACM SIGMETRICS Performance EvaluationReview, 44(2), pp. 39-41, 2016.

15. Mishra M., Dey B., Prabhakaran V. and Diggavi S., Wiretapped Oblivious Transfer, IEEE Transactionson Information Theory, 63(4), pp. 2560-2595, April 2017.

16. Pananjady A., Bagaria V.K. and Vaze R.,Optimally Approximating the Coverage Lifetime of Wireless SensorNetworks, IEEE/ACM Transactions on Networking, 25(1), pp. 98-111, Feb. 2017.

?17. Sinclair A., Srivastava P., Štefankovic D. and Yin Y., Spatial Mixing and the Connective Constant: Op-timal Bounds, Probability Theory & Related Fields, 188(1-2), pp. 153-197, 2017, Extended abstractsof different parts of this paper appeared in Proceedings of the IEEE Symposium on the Founda-tions of Computer Science (FOCS), 2013 and Proceedings of the ACM-SIAM Symposium on DiscreteAlgorithms (SODA), 2015.

18. Volk B.L., Saptharishi R. and Shpilka A., Efficiently Decoding Reed-Muller Codes From Random Errors,IEEE Transactions on Information Theory, 63(4), pp. 1954–1960, 2016.

19. Vaze R. and Murthy C.R., Multiple Transmitter Localization and Whitespace Identification Using RandomlyDeployed Binary Sensors, IEEE Transactions on Cognitive Communications and Networking, 2(4), pp.358-369, Dec. 2016.


?1. Anderson M., Forbes M., Saptharishi R., Shpilka A., and Volk B.L. Identity testing and lower bounds forread-k oblivious algebraic branching programs. Conference on Computational Complexity, CCC, pages30:1–30:25, 2016.

2. Bakshi M. and Prabhakaran V., Plausible Deniability Over Broadcast Channels, Proceedings of the IEEEInternational Symposium on Information Theory (ISIT), Barcelona, 2016.

3. Barhate S., Kshirsagar S., Sanghvi N., Sabu K., Rao P. and Bondale N., Prosodic Features of MarathiNews Reading Style, 2016 IEEE Region 10 Conference (TENCON), Singapore, pp. 2215-2218, 2016.doi: 10.1109/TENCON.2016.7848421

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4. Bhaskar U., Jalal A. and Vaze R., The Adwords Problem with Strict Capacity Constraints, Proceedings ofthe 36th IARCS Annual Conference on Foundations of Software Technology and Theoretical Com-puter Science (FSTTCS), Chennai, India, 65, pp. 30:1-30:14, 2016.

5. Bhaskar U., Cheng Y., Ko Y.K. and Swamy C., Hardness Results for Signaling in Bayesian Zero-Sum andNetwork Routing Games, EC 2016, pp. 479-496, 2016.

6. Bhrushundi A., Harsha P. and Srinivasan S., On Polynomial Approximations Over Z/2kZ, In HeribertVollmer and Brigitte Vallée, editors, Proceedings of the 34th Symposium on Theoretical Aspects ofComputer Science (STACS), Hannover, Germany, 66 of Leibniz International Proceedings in Infor-matics, pp. 12:1-–12:12. Schloss Dagstuhl, 2017.

7. Budkuley A., Dey B. and Prabhakaran V., Coding for Arbitrarily Varying Remote Sources, IEEE Interna-tional Symposium on Information Theory (ISIT) (to be presented), Aachen, 2017.

8. Censor-Hillel K., Kavitha T., Paz A. and Yehudayoff A., Distributed Construction of Purely AdditiveSpanners Proceedings of the 30th International Symposium on Distributed Computing (DISC), pp.129-142, 2016.

9. Chakraborty S., Gupta A. and Jain R., Matching Multiplications in Bit-Vector Formulas, VMCAI (2017).

10. Cseh Á and Kavitha T., Popular Edges and Dominant Matchings. Proceedings of the 18th InternationalConference on Integer Programming and Combinatorial Optimization (IPCO), pp. 138-151, 2016.

11. Chattopadhyay A., Langberg M., Li S. and Rudra A., Tight Network Topology Dependent Bounds onRounds of Communication, Proceedings of the ACM-SIAM Annual Symposium on Discrete Algorithms(SODA), 2017.

12. ChattopadhyayA., Dvorak P., Koucky M., Loff B. and Mukhopadhyay S. Lower Bounds on Eliminationvia Weak Regularity, Proceedings of the 34th Symposium on Theoretical Aspects of Computer Science(STACS), pp. 21:1-21:14, 2017.

13. Dalai M., Guruswami V., Radhakrishnan J. An improved bound on the zero-error list-decoding capacity ofthe 4/3 channel Proceedings of the IEEE Symposium on Information Theory (ISIT) 1658-1662, 2017.

14. Deshmukh A. and Vaze R., Online Energy Efficient Packet Scheduling for a Common Deadline With andWithout Energy Harvesting, Proceedings of RAWNET 2016, Tempe, Arizona, USA, 2016.

15. Deshpande A., Harsha P. and Venkat R., Embedding Approximately Low-dimensional `2 Metrics into`1, in Akash Lal, S. Akshay, Saket Saurabh, and Sandeep Sen, editors, Proceedings of the 36thIARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Sci-ence (FSTTCS), Chennai, India, 65 of Leibniz International Proceedings in Informatics, pp. 10:1—10:13. Schloss Dagstuhl, 2016.

16. Dinur I., Harsha P., Venkat R. and Yuen H., Multiplayer Parallel Repetition for Expander Games, inChristos Papadimitriou, editor, Proceedings of the 8th Innovations in Theoretical Computer Science(ITCS), Berkeley, USA, Leibniz International Proceedings in Informatics. Schloss Dagstuhl, 2017.

?17. Forbes M., Kumar M., and Saptharishi R. Functional lower bounds for arithmetic circuits and connectionsto boolean circuit complexity. Conference on Computational Complexity, CCC, pages 33:1–33:19, 2016.

18. Gajjar K., Radhakrishnan J. Distance-Preserving Subgraphs of Interval Graphs. European Symposium onAlgorithms (ESA), 39:1-39:13, 2017.

19. Garg M. and Radhakrishnan J., Set Membership with Non-Adaptive Bit Probes. STACS 2017, pp. 38:1-38:13, 2017.

20. Guruswami V. and Radhakrishnan J., Tight Bounds for Communication-Assisted Agreement Distillation,Conference on Computational Complexity 2016, pp. 6:1-6:17, 2016.

21. Harsha P., Jain R. and Radhakrishnan J., Partition Bound is Quadratically Tight for Product Distributions,

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in Ioannis Chatzigiannakis, Michael Mitzenmacher, Yuval Rabani, and Davide Sangiorgi, editors, Pro-ceedings of the 43rd International Colloquium of Automata, Languages and Programming (ICALP),Part III, Rome, Italy, 55 of Leibniz International Proceedings in Informatics, pp. 135:1-–135:13, SchlossDagstuhl, 2016.

22. Harsha P. and Srinivasan S., On Polynomial Approximations to AC0, in Klaus Jansen, Claire Mathieu,Jose D. P. Rolim, and Chris Umans, editors, Proceedings of the 20th International Workshop onRandomization and Computation (RANDOM), Paris, France, 60 of Leibniz International Proceedingsin Informatics, pp. 32:1—32:14, Schloss Dagstuhl, 2016.

23. Harsha P. and Srinivasan S., Robust Multiplication-based Tests for Reed-Muller Codes, in Akash Lal, S.Akshay, Saket Saurabh, and Sandeep Sen, editors, roceedings of the 36th IARCS Annual Conferenceon Foundations of Software Technology and Theoretical Computer Science (FSTTCS), Chennai, India,65, of Leibniz International Proceedings in Informatics, pp. 17:1-–17:14, Schloss Dagstuhl, 2016.

24. Huang C.-C. and Kavitha T., Popularity, Mixed Matchings, and Self-duality, Proceedings of the 28thSymposium on Discrete Algorithms (SODA), pp. 2294-2310, 2017.

25. Kavitha T., Popular Half-Integral Matchings, Proceedings of the 43rd International Colloquium onAutomata, Languages, and Programming (ICALP), pp. 22:1-22:13, 2016.

26. Kumar M. and Saptharishi R., Finer Separations Between Shallow Arithmetic Circuits, 36th IARCS An-nual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS2016), 65, pp. 38:1-38:12, 2016.

27. Prabhakaran M. and Prabhakaran V., Rényi Information Complexity and an Information Theoretic Char-acterization of the Partition Bound, Proceedings of the 43rd International Colloquium on Automata,Languages and Programming (ICALP), Rome, 2016.

28. Radhakrishnan J. and Sanyal S., The Zero-Error Randomized Query Complexity of the Pointer Function.FSTTCS 2016, Chennai, pp. 16:1-16:13, 2016.

29. Radhakrishnan J., Sen P. and Warsi N., One-shot Marton Inner Bound for Classical-quantum BroadcastChannel, IEEE Transactions on Information Theory, 62(5), pp. 2836–2848, 2016.

30. Raja N., Modularity and Proof Assistants, Proceedings of Current Issues in Interactive Theorem Proving,Toulouse, France, 2016.

31. Raja N., Oppositions in Pure Versus Applied Logics, Proceedings of the Fifth World Congress on theSquare of Opposition, Santiago, 2016.

32. Raja N., Modal Logics for Internet Protocols, Proceedings of the Conference on Assertoric and ModalLogics, Santiago, 2016.

33. Rajan S., Rajakrishnan S., Thangaraj A. and Prabhakaran V., Lower Bounds and Optimal Protocols forThree-Party Secure Computation, Proceedins of the IEEE International Symposium on Information The-ory (ISIT), Barcelona, July 2016.

34. Ravindrakumar V., Panda P., Karamchandani N. and Prabhakaran V., Fundamental Limits of Secre-tive Coded Caching, Proceedings of the IEEE International Symposium on Information Theory (ISIT),Barcelona, 2016.

?35. Saptharishi R., Shpilka A., and Volk B. L. Efficiently decoding reed-muller codes from random errors.Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing, STOC, pages227–235, 2016.

?36. Schulman L.J. and Srivastava P., Stability of Causal Inference, Proceedings of the Conference on Uncer-tainty in Artificial Intelligence (UAI), Best paper award, 2016.

37. Vaze R. and Moharir S., Paging with Multiple Caches, Proceedings of IEEE WiOpt 2016, Tempe, Ari-zona, USA, 2016.

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38. Vaze R. and Coupechoux M., Online Budgeted Truthful Matching, Proceedings of NetEcon, June 2016.

Book Chapters

1. S.K. Juneja, Dynamic Portfolio Credit Risk and Large Deviations, Econophysics and Sociophysics: RecentProgress and Future Directions (pp. 41-58). Springer, Cham., 2017.

2. N. Raja, Abhishek Kr. Singh, Separation Logic to Meliorate Software Testing, Trends in Software Testing,ISBN: 978-981-10-1414-7, Springer-Verlag (2016)

In 2017-18

In Journals

1. Anderson M., Forbes M., Saptharishi R., Shpilka A. and Volk B.L., Identity Testing and Lower Boundsfor Read-k Oblivious Algebraic Branching Programs, TOCT, 10(1):3:13:30, 2018.

2. Bakshi M. and Prabhakaran V., Plausible Deniability Over Broadcast Channels, IEEE Transactions onInformation Theory, April 2018.

3. Berthet Q., Rigollet P. and Srivastava P., Exact Recovery in the Ising Blockmodel, Annals of Statistics,accepted for publication.

4. Budkuley A., Dey B. and Prabhakaran V., Communication in the Presence of a State-Aware Adversary,IEEE Transactions on Information Theory, 63 (11), pp. 7396-7419, November 2017.

5. Cseh Á., Huang C.-C. and Kavitha T., Popular Matchings With Two-sided Preferences and One-sided Ties,SIAM Journal on Discrete Mathematics, 31(4), pp. 2348-2377, 2017.

6. Gupta A., Kamath P., Kayal N. and Saptharishi R., Unexpected Power of Low-depth Arithmetic Circuits,Commun. ACM, 60(6):93100, 2017.

7. Huang C.-C. and Kavitha T., New Algorithms for Maximum Weight Matching and a Decomposition Theo-rem, Mathematics of Operations Research, 42(2), pp. 411-426, 2017.

8. Hong L.J., Juneja S.K. and Liu G., Kernel Smoothing for Nested Estimation With Application to PortfolioRisk Measurement, Operations Research, 65(3), pp. 657-673, 2017.

9. Juneja S.K. and Shimkin N., On the Computation of Dynamic User Equilibrium in the Multiclass TransientFluid Queue, ACM SIGMETRICS Performance Evaluation Review, 45 (2), pp. 137-142, 2017.

10. Kavitha T., New Pairwise Spanners, Theory of Computing Systems, 61(4), pp. 1011-1036, 2017.

11. Moka S.B., Juneja S.K., and Mandjes M.R.H., Analysis of Perfect Sampling Methods for Hard-Sphere Mod-els, ACM SIGMETRICS Performance Evaluation Review, 45(2), pp. 69-75, doi: 10.1145/3199524.3199536,2017.

12. Narayanan H.and Rakhlin A., Efficient Sampling from Time-varying Log-concave Distributions, Journalof Machine Learning Research, 18 (112), pp. 1-29, 2017.

13. Ravindrakumar V., Panda P., Karamchandani N., and Prabhakaran V., Private Coded Caching, IEEETransactions on Information Forensics and Security, 13 (3), pp. 685-694, March 2018.

14. Saptharishi R., Shpilka A. and Volk B.L., Efficiently Decoding Reed-muller Codes from andom Errors, IEEETrans. Information Theory, 63(4):19541960, 2017.

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in 2017-18

In International Proceedings

1. Bondale N. and Deo A., The “Unseen" Body; Biofield Scanning for Detection and Prediction of HealthIssues, Proceedings of the ’5th International Conference Science and Scientist - 2017’, Kathmandu,Nepal, August 2017.

2. Bondale N. and Deo A., Interpretation of Biofield Scan Based on Vedic view of Human Body", Proceedingsof the 3rd World Congress of Vedic Sciences, Pune, India, January 2018.

3. Bhaskar U., Dani V. and Ghosh A., Truthful and Near-Optimal Mechanisms for Welfare Maximization inMulti-Winner Elections, Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence,pp. 925-932.

4. Brandl F. and Kavitha T., Popular Matchings With Multiple Partners, Proceedings of the 37th IARCS An-nual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS),2017.

5. Budkuley A., Dey B., and Prabhakaran V., Coding for Arbitrarily Varying Remote Sources, Proceedingsof the IEEE International Symposium on Information Theory (ISIT), Aachen, pp. 729-733, 2017.

6. Chakraborty S., Gupta A, and Unadkat D., Verifying Array Manipulating Programs by Tiling, SAS 2017.

7. Chattopadhyay A. and Mande N., A Lifting Theorem with Applications to Symmetric Functions, Pro-ceedings of the Foundations of Software Technology and Theoretical Computer Science (FSTTCS),23:1-23:14, 2017.

8. Dalai M., Guruswami V. and Radhakrishnan J., An Improved Bound on the Zero-error List-decodingCapacity of the 4/3 Channel, ISIT, pp. 1658-1662, 2017.

9. Data D. and Prabhakaran V., Secure Computation of Randomized Functions: Further Results, Proceedingsof the IEEE Information Theory Workshop, Kaohsiung, doi:10.1109/ITW.2017.8277968, 2017.

10. Gajjar K. and Radhakrishnan J., Distance-Preserving Subgraphs of Interval Graphs, ESA 2017: 39:1-39:13.

11. Gupta A., Shukla A., Srivas M. and Thattai M., SAT Solving for Vesicle Traffic Systems in Cells, SASB2017.

12. Harvey N.J.A., Srivastava P. and VondrÃ¡k J., Computing the Independence Polynomial: From the TreeThreshold Down to the Roots, Proceedings of the Annual ACM-SIAM Symposium on Discrete Algo-rithms, pp. 1557–1576, 2018.

13. Kumar M. and Saptharishi R., An Exponential Lower Bound for Homogeneous Depth-5 Circuits Over FiniteFields, 32nd Computational Complexity Conference, CCC 2017, Riga, Latvia, 79 pp. 31:131:30, 2017.

14. Kurri G., Prabhakaran V. and Sarwate A., Coordination Using Individually Shared Randomness, to bepresented at IEEE International Symposium on Information Theory (ISIT), Vail, 2018.

15. Kurri G., Ravi J. and Prabhakaran V., The Role of Interaction and Common Randomness in Two-User SecureComputation, to be presented at IEEE International Symposium on Information Theory (ISIT), Vail,2018.

16. Liu J., Sinclair A. and Srivastava P., The Ising Partition Function: Zeros and Deterministic Approximation,Proceedings of the 58th Annual IEEE Symposium on the Foundations of Computer Science, pp.986–997, 2017.

17. Narayanan V.. Ravi J., Mishra V., Dey B., Karamchandani N. and Prabhakaran V., Private Index Coding,to be presented at IEEE International Symposium on Information Theory (ISIT), Vail, 2018.

18. Marathe A., Raj S., Pillai B. and Vaze R.„ Opportunistic Scheduling in Two-Way Wireless CommunicationWith Energy Harvesting, Proceedings of the WiOpt, Paris, May2017.

19. Thaker P., Gopalan A. and Vaze R., When to Arrive in a Congested System: Achieving Equilibrium via

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Learning Algorithm, Proceedings of the 2017 International Workshop on Resource Allocation, Coop-eration and Competition in Wireless Networks (RAWNET),WiOpt 2017, Paris, France, 2017.

20. Sharma M., Murthy C.R. and Vaze R., On Distributed Power Control for Uncoordinated Dual EnergyHarvesting Links: Performance Bounds and Near-Optimal Policies, Proceedings of the WiOpt, Paris, May2017.

21. Vaze R., Chaudhari S., Choube A. and Aggarwal N., Energy-Delay-Distortion Problem, Proceedings ofthe National Conference on Communications, Indian Institute of Technology, Hyderabad, February2018.

22. Vaze R. and Iyer S., Capacity of Cellular Wireless Networks, Proceedings of the WiOpt, Paris, May 2017.

23. Vaze R., Online Knapsack Problem and Budgeted Truthful Bipartite Matching, Proceedings of the IEEEINFOCOM 2017, Atlanta, 2017.

188

Workshops Organised

2008-09

• Introduction to Geometric Algorithms, July 22-23, 2008.

• Indian Language Speech Recognition, November 29 - December 05, 2008.

• ICTS Workshop on Graphical Models, Statistical Inference and Algorithms, January 05-10, 2009.

• ICTS Workshop on Reinforced Random Walks and Random Walks in Random Environments, IndianStatistical Institute, Bangalore, December 05-08, 2008 (by Professor V.S. Borkar and Siva Athreya ofIndian Statistical Institute, Bangalore).

• Dr. Homi J. Bhabha Birth Centenary Workshop on Introduction to Graph and Geometric Algorithms,January 22-24, 2009.

• Workshop on Advances in Theory and Analysis of Timed and Hybrid Systems, February 11, 2009.

2009-10

• Spoken Language Processing, June 22, 2009. Technical programme consisted of 4 invited talks:

– Natural Language Processing: A Multi-lingual Word Sense Disambiguation Perspective by Pro-fessor Pushpak Bhattacharya, IIT Bombay.

– Singing Voice Processing by Professor Preeti Rao, IIT Bombay.

– Vocal Tract Shape Estimation for Speech Training Aids by Professor P.C. Pandey, IIT Bombay.

– mKRISHI: A Mobile Based Agro-advisory System: Relevance of Speech Technology by Dr. ArunPande, Tata Consultancy Services, Mumbai

• Dr. Homi J. Bhabha Birth Centenary Workshop on Introduction to Graph and Geometric Algorithms,Bangalore, July 15-18, 2009.

• Dr. Homi J. Bhabha Birth Centenary Workshop on Stochastic Methods: Analysis & Algorithms,September 02-05, 2009.

• Research Promotion Workshop on Introduction to Graph and Geometric Algorithms, Tiruchirapalli,January 07-09, 2010.

• Research Promotion Workshop on Introduction to Graph and Geometric Algorithms, Banaras HinduUniversity, January 27-29, 2010.

• Research Promotion Workshop on Introduction to Graph and Geometric Algorithms, National Insti-tute of Technology, Rourkela, March 25-27, 2010.

workshops organised

2010-11

• STCS Annual Symposium, April 08-09, 2010.

• Workshop on Concurrency, November 12, 2010.

• Workshop on Phonetics and Speech Technology, November 19, 2010.

• Workshop on Recent Trends in Social Networks: Algorithms, Models and Learning, January 03-05,2011.

• Workshop on Computing for Science Discovery and Innovations: A Roadmap, February 18-19, 2011.

• STCS Annual Symposium, March 24-25, 2011.

2011-12

• Organized on behalf of ICTS a school and workshop on “Mathematical Finance” from January 16 to27, 2012 (in collaboration with Freddy Delbaen ETH Zurich, Switzerland, Ronnie Sircar Princeton,USA and Srikanth Iyer Indian Institute of Science, Bangalore).

2012-13

• 32nd International Conference on Foundations of Software Technology and Theoretical ComputerScience, International Institute of Information Technology, Hyderabad, December 15-17, 2012 ( A.Chattopadhyay, P. Bhattacharya, K. Gajjar, M. Garg, P. Harsha, T. Kavitha, S. Mukhopadhyay, J.Radhakrishnan, S. Sarswat, S. Sanyal, G. Varma, N. Varma and Rakesh Venkat).

• 24th International Conference on Computational Linguistics (COLING 2012), Mumbai, December8-15, 2012 (N. Bondale).

• Fifth Indian Conference on Logic and its Applications, Chennai, India, January 2013 (N. Raja).

• N. Bondale presented paper in 2nd workshop on Sentiment Analysis where AI meets Psychology(SAAIP), Mumbai, December 15, 2012.

2013-14

• Workshop on Applications of Game Theory, May 03-04, 2013. (Organizer: S.K. Juneja)

• Introduction to Computational Geometry, Research Promotion Workshop on Introduction to Graphand Geometric Algorithms, National Institute of Technology, Warangal, October 23, 2013. (Organizer:S.K. Ghosh)

• Workshop on Energy Efficiency in Wireless Networks, January 16-17, 2014. (Organizer: R. Vaze)

• Introduction to Computational Geometry, Research Promotion Workshop on Introduction to Graph andGeometric Algorithms, Indian Institute of Information Technology and Management, Kerala, Trivan-drum, January 23, 2013. (Organizer: S.K. Ghosh)

• Recent Progress in Arithmetic Complexity, February 13-17, 2014. (Organizers: A. Chattopadhyay, P.Harsha, J. Radhakrishnan)

• Introduction to Approximation Algorithms, Research Promotion Workshop on Introduction to Graphand Geometric Algorithms, IIT Roorkee, March 06, 2014. (Organizer: S.K. Ghosh)

2014-15

• STCS Symposium 2014, September 29-30, 2014. (Organizer: R. Vaze & P. Harsha)

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• Research Promotion Workshop on “Introduction to Graph and Geometric Algorithms”, ManipalInstitute of Technology and Sikkim Government College, Sikkim, October 16-18, 2014. (Organizer:S.K. Ghosh)

• Principles of Programming Languages (POPL 2015), January 12-18, 2015. (Organizer: P.K. Pandya)

• Research Promotion Workshop on “Introduction to Graph and Geometric Algorithms”, NationalInstitute of Technology, Nagpur, January 15-17, 2015. (Organizer: S.K. Ghosh)

• Tutorial and Workshop on Learning and Related Probabilistic Applications, TIFR, February 25-26,2015. (Organizer: S.K. Juneja & R. Vaze)

• Research Promotion Workshop on “Introduction to Graph and Geometric Algorithms”, Universityof Kashmir, Srinagar, March 26-28, 2015. (Organizer: S.K. Ghosh)

2015-16

• Workshop on “Bombay Information Theory Seminar (BITS 2016)” jointly by IIT Bombay and TIFR,January 01-03, 2016. (Organizers: P. Harsha, V.M. Prabhakaran and J. Radhakrishnan)

2016-17

• First Indian SAT+SMT School, December 04-10, 2016.

• STCS Day 2017, March 06-07, 2017. (Organiser: U. Bhaskar)

• Workshop on Applied Probability, March 31 - April 2, 2017.

2017-18

• Second Indian SAT+SMT School, December 6 – 8, 2018. (Organizers: A. Gupta and S. Chakraborty[IIT Bombay])

• Workshop on “Bombay Information Theory Seminar (BITS 2018)” jointly by IIT Bombay and TIFR,January 11-14, 2018. (Organizers: V.M. Prabhakaran and J. Radhakrishnan)

• Probability Day, January 26, 2018 (Organizer: S.K Juneja)

• STCS Annual Day, February 15-16, 2018 (Organizer: R. Saptharishi)

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Former Members of the School

Over the years, a large and diverse group of researchers and have been associated with the school asgraduate students, post-doctoral fellows, adjunct faculty, and have contributed immensely to the growthof the school. Here we list them along with their last known affiliation.

Former faculty members

The following members were part of the STCS faculty during the period 1998 to present.

• Prof. Vivek S Borkar (from 1999 until 2011)At present at IIT Bombay

• Prof. Onkar Dabeer (from 2004 until 2015)At present at Amazon Inc, USA

• Prof. Manoj Gopalkrishnan (from 2009 until 2016)At present at IIT Bombay

• Prof. Subir K Ghosh (until 2015)At present at Ramakrishna Mission Vivekananda University, Kolkata

• Prof. Narendra K Karmarkar (from 1999 until 2006)

• Prof. V Ramasubramanian (until 1998)

• Prof. PVS Rao (until 1998)

• Prof. Sugata Sanyal (until 2012)At present adjunct faculty at IIT Guwahati

• Prof. Naresh Sharma (until 2016)

• Prof. RK Shyamasundar (until 2015)At present at IIT Bombay

• Mr PS Subramanian (until 1999)

Past PhD students

• Sagnik Mukhopadhyay (PhD, 2017)KTH Royal Institute of Technology, Sweden, postdoctoral researcherThesis Title: Communication Complexity Amplification by Function Composition, TIFRGuide: A. Chattopadhyay

• Deepesh Data (PhD, 2017)

former members of the school

University of California, Los Angeles, USA, postdoctoral researcherThesis Title: Communication Complexity and Characterization Results in Secure Computation, TIFRGuide: V. Prabhakaran

• Swagato Sanyal (PhD, 2017)IIT KharagpurThesis Title: Complexity Measures of Boolean Functions: Fourier Sparsity, Fourier Dimension and QueryComplexity, TIFRACM India Doctoral Dissertation Award 2017, Honourable MentionGuide: P. Harsha

• Rakesh Venkat (PhD, 2017)Hebrew University of Jerusalem, Israel, postdoctoral researcherThesis Title: On Sparsest Cut and Parallel Repetition, TIFRGuide: P. Harsha

• Sarat Babu Moka (PhD 2017)University of Queensland, Australia, postdoctoral researcherThesis Title: Invariant Measures for Queueing and Spatial Markov Processes: Algorithms and Analysis, TIFRGuide: S.K. Juneja.

• Mohit Garg (PhD, 2016)Open University of Israel, postdoctoral researcher.Thesis Title: The Bit-Probe Complexity of Set Membership, TIFRGuide: J. Radhakrishnan

• Girish R. Varma (PhD, 2016)International Institute of Information Technology, Hyderbad.Thesis Title: Hardness of Approximate Coloring, TIFRGuide: P. Harsha

• Bodhayan Roy (PhD, 2016)Masaryk University, Czechia, postdoctoral scholarThesis Title: Studying Triangulations and Visibility Graphs of Planar Point Sets, TIFRGuide: S.K. Ghosh

• Gugan Thoppe (PhD, 2016)Technion-Israel Institute of Technology, Haifa, Israel, postdoctoral researcherThesis Title: On Stochastic Approximation, Stochastic Algebraic Topology and Optimization in High Dimen-sions, TIFRGuide: V.S. Borkar and V. Prabhakaran

• Naqueeb A. Warsi (PhD, 2016)Indraprastha Institute of Information Technology, Delhi.Thesis Title: One-Shot Bounds in Classical and Quantum Information Theory, TIFRGuide: J. Radhakrishnan/P.G.D. Sen

• Tapan Shah (PhD, 2015)GE Global Research, BangaloreThesis Title: Signal Processing for Systems with Low Precision Quantization, TIFRGuide: O. Dabeer

• Ankush Agarwal (PhD, 2015)University of Glasgow, UKThesis Title: Monte Carlo Based Methods for Pricing American Options, TIFRGuide: S.K. Juneja

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past phd students

• Karthyek R.A. Murthy (PhD, 2015)Singapore University of Technology and Design, SingaporeThesis Title: Rare Events in Heavy-Tailed Stochastic Systems: Algorithms and Analysis, TIFRGuide: S.K. Juneja

• Santanu Dey (PhD, 2013)Thesis Title: Optimal Change of Measure for Model Selection and Efficient Simulation of Large DeviationProbability with Financial Applications, TIFRGuide: S.K. Juneja

• Simoni S. Shah (PhD, 2013)IIT BombayThesis Title: Unambiguity and Timed Languages: Automata, Logics, Expressiveness, TIFRGuide: P.K. Pandya

• Kishor K. Barman (PhD, 2012)Google, San Francisco Bay Area, USAThesis Title: Topics in Collaborative Estimation and MIMO Wireless Communication, TIFRGuide: O. Dabeer

• N.V. Narendrakumar (PhD, 2012)IDRBT, HyderabadThesis Title: Malware Detection by Behavioural Approach and Protection by Access Control, TIFRGuide: R.K. Shyamasundar

• Sameer Kamal (PhD, 2011)IIT GuwahatiThesis Title: Application and Analysis of Stochastic Approximation Algorithms, TIFRGuide: V.S. Borkar

• Saswata Shannigrahi (PhD, 2011)IIT RoparThesis Title: Coloring, Embedding, Compression, and Data Structure Problem on Uniform Hypergraphs,TIFRGuide: J. Radhakrishnan

• Benny K. George (PhD, 2011)IIT GuwahatiThesis Title: Solving Conjugacy Equation on Languages, TIFRGuide: N. Raja

• Vijay Suman (PhD, 2010)Intel, BangaloreThesis Title: Determinization, Clock Reduction and Experimental Analysis of Timed Systems, TIFRGuide: P.K. Pandya

• Chinmoy Dutta (PhD, 2010)Lyft, San Francisco, USAThesis Title: Lower Bounds for Noisy Computations, TIFRGuide: J. Radhakrishnan

• Shivali Agarwal (PhD, 2009)IBM India Research Laboratory, Bangalore.Thesis Title: Analysis, Scheduling and Reasoning in Distributed Shared Memory Programming Languages,TIFRGuide: R.K. Shyamasundar

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• Arnab Basu (PhD, 2007)Indian Institute of Management BangaloreThesis Title: Applications of Stochastic Optimisation: Models and Algorithms, TIFRGuide: V.S. Borkar

• S. Krishnan (PhD, 2006)IIT BombayThesis Title: Approximate and Parametric Solutions to Algorithmic Problems, TIFRGuide: R.K. Shyamasundar

• Rahul Jain (PhD, 2005)National University of Singapore, SingaporeThesis Title: Information Theoretic Problems in Computational Complexity Theory, University of MumbaiGuide: R.K. Shyamasundar/J. Radhakrishnan

• T. Kavitha (PhD, 2003)Tata Institute of Fundamental ResearchThesis Title: T. Kavitha, Algorithms for Computing Paths of Bounded Curvature in a Polygon, Universityof MumbaiGuide: S.K. Ghosh

• S. Venkatesh (PhD, 2002)University of Victoria, CanadaThesis Title: Combinatorial Problems in Data Structures, University of MumbaiGuide: R.K. Shyamasundar/J Radhakrishnan

• P.G.D. Sen (PhD, 2002)Tata Institute of Fundamental Research, MumbaiThesis Title: Algebraic Problems in Computational Complexity, University of MumbaiGuide: R.K. Shyamasundar/J. Radhakrishnan

• N. Raja (PhD, 1998)Tata Institute of Fundamental Research, MumbaiThesis Title: Combinators and Type Systems for Programming Languages, University of MumbaiGuide: R.K. Shyamasundar

• Ashok Khemka (PhD, 1997)Indian Administrative ServiceThesis Title: Real-Time Scheduling, University of MumbaiGuide: R.K. Shyamasundar

• K. Narayan Kumar (PhD, 1997)Chennai Mathematical Institute, ChennaiThesis Title: Models of Asynchronous Processes, University of MumbaiGuide: R.K. Shyamasundar/P.K. Pandya

• Basant Rajan (PhD, 1997)Coriolis Technologies Pvt Ltd, PuneThesis Title: Programming Languages: Design and Specification of Multiple-Clocked Systems, University ofMumbaiGuide: R.K. Shyamasundar

• R. Ravindran (PhD, 1996)Mercedes-Benz R & D India, BangaloreThesis Title: Speech Signal Processing & Recognition Using a Prediction Approach for Modelling the SpectralDynamics, University of Mumbai

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past phd students

Guide: P.V.S. Rao

• M.R.K. Krishna Rao (PhD, 1995)King Fahd University of Petroleum and Minerals, Saudi ArabiaThesis Title: Termination Characteristics of Logic Programs, University of MumbaiGuide: R.K. Shyamasundar

• K.V. Subrahmanyam (PhD,1995)Chennai Mathematical Institute, ChennaiThesis Title: Complexity of Threshold Problems in Monotone Contact Networks, University of MumbaiGuide: R.K. Shyamasundar/J. Radhakrishnan

• Anil Seth (PhD, 1995)IIT KanpurThesis Title: Complexity Theory of Higher Type Functionals, University of MumbaiGuide: R.K. Shyamasundar/P.S. Subramanian

• Sanjeev Saluja (PhD, 1995)Thesis Title: Counting Classes in Complexity Theory, University of MumbaiGuide: R.K. Shyamasundar/P.S. Subramanian

• S. Krishnan (PhD, 1995)Nuance, Sunnyvale, California, USAThesis Title: Speech Recognition by Computer: Spectral Temporal Redundancy and Stochastic SegmentalModels, University of MumbaiGuide: P.V.S. Rao

• Pinaki Poddar (PhD, 1993)Oracle, Redwood City, California, USAThesis Title: Speech Recognition: A Connectionist Approach, University of MumbaiGuide: P.V.S. Rao

• Anil K. Maheshwari (PhD, 1993)Carleton University, CanadaThesis Title: Parallel Algorithms for Minimum Link Path and Related Problem, University of MumbaiGuide: M.V. Pitke/S.K. Ghosh

• S. Sanyal (PhD, 1992)WRC Technologies Pvt Ltd., MumbaiThesis Title: Computer Architecture: Some Aspects of Fault Tolerance and Coding Techniques, University ofMumbaiGuide: P.V.S. Rao

• V. Ramasubramanian (PhD, 1992)PESIT, BangaloreThesis Title: Fast Algorithms for Nearest Neighbor Search & Application to Vector Quantization, Universityof MumbaiGuide: P.V.S. Rao

• Anjan Basu (PhD, 1991)Ubq Technologies Pvt Ltd

• S. Arun-Kumar (PhD, 1989)IIT DelhiThesis Title: A Real-time Calculus of Communicating Systems, University of MumbaiGuide: R.K. Shyamasundar

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• Ajit A. Diwan (PhD, 1989)IIT BombayThesis Title: A Study of Numerical VLSI Algorithms, University of MumbaiGuide: R.K. Shyamasundar

• Subir Kumar Ghosh (PhD, 1988)Ramakrishna Mission Vivekananda University, KolkataThesis Title: Problems in Computational Geometry, University of MumbaiGuide: R.K. Shyamasundar

• G. Venkatesh (PhD, 1988)Sasken Technologies Ltd, Bangalore/ChennaiThesis Title: Reasoning About Digital Systems Using Linear-Time Temporal Logic, University of MumbaiGuide: R.K. Shyamasundar

• Kamal Lodaya (PhD, 1988)Institute of Mathematical Sciences, ChennaiThesis Title: Proof Theory for Exception Handling in Distributed Programs, University of MumbaiGuide: R.K. Shyamasundar

• R. Ramanujam (PhD, 1988)Institute of Mathematical Sciences, ChennaiThesis Title: Theories and Models of Distributed Logic Programs, Universiy of MumbaiGuide: R.K. Shyamasundar

• P.K. Pandya (PhD, 1988)Tata Institute of Fundamental Research, MumbaiThesis Title: Compositional Verification of Distributed Programs, University of MumbaiGuide: R.K. Shyamasundar/M. Joseph

Past MSc Students

• Rahul Jain (MSc, 2017)Mathworks, Bangalore

• P. Bhattacharya (MSc, 2014)IIT Kharagpur, graduate studentThesis Title: Switching in Boolean Circuits and Modelling Cognition Through Neuroids,Guide: M. Gopalkrishnan

• Nithin M. Varma (MSc, 2014)Boston University, USA, graduate studentThesis Title: Small Stretch Pairwise Spanners and D SpannersGuide: T. Kavitha

• Ajesh Babu (MSc, 2010)Microsoft, BangaloreThesis Title: Some Extensions to the Theory of Regularity in Formal LanguagesGuide: P.K. Pandya

• Silky Arora (MSc, 2007)Immunetrics, Greater Pittsburgh AreaThesis Title: Synthesizing Runtime Monitors from Logical SpecificationsGuide: P.K. Pandya

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past post-doctoral fellows

Past Post-doctoral Fellows

• Jithin Ravi (2017)Universidad Carlos III de Madrid, Spain

• Sharayu Moharir (2015)IIT Bombay.

• Sreejith A V (2014)IIT Goa.

• Ratnik Gandhi (2012)Ahmedabad University

• Smarajit Das (2012)IIT Guwahati.

• Ashish Tendulkar (2012)Google AI, Hyderabad.

• Nutan Limaye (2010)IIT Bombay.

• Benoit Razet (2010)Blockchain Research, PokitDok, Charleston, USA.

• D J Das (2009)Hitech Robotic Systemz Ltd., Pune.

• A. D. Banik (2009)IIT Bhubaneswar.

Former Adjunct Faculty

Adjunct Faculty 2008-09: V. Anantharam (University of California, Berkeley, USA), D. Kapur (Univer-sity of New Mexico, USA) and R. Krishnamurti (Simon Fraser University, Canada).

Adjunct Faculty 2009-10: V. Anantharam (University of California, Berkeley, USA) and D. Kapur (Uni-versity of New Mexico, USA).




Visiting Professor 2013-14: S.P. Mudur (Up to May 2013) and D. Kapur (from December 2013 up toMarch 2014)

Adjunct Faculty 2014-15: No Adjunct Faculty during this period.

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Adjunct Faculty/Visiting Professor 2015-16: V.S. Borkar (IIT Bombay) (from April 01, 2015) and P.P. Kurur(Visiting Professor) (IIT Kanpur) (from January 2016).

Adjunct Faculty/Visiting Professor 2016-17: V.S. Borkar (IIT Bombay), P.P. Kurur (IIT Kanpur) (till June2016), V. Dani (University of New Mexico, USA) (from September 2016 till January 2017) and T.P. Hayes(University of New Mexico, USA) (from September 2016 till January 2017).

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Grants and Projects

Faculty Grants

Umang Bhaskar

• Ramanujan Fellowship, Department of Science and Technology, India.

• Part of a one year Open Science Collaborative Project with IBM IRL, Bengaluru, on “Strategy-proofDecentralized Mechanisms for Selection in Networked Multi-Agent Systems”.

Arkadev Chattopadhyay

• Ramanujan Fellowship, Department of Science and Technology, India.

Prahladh Harsha

• Israel-India ISF-UGC grant on “Two player games: hardness of approximation and communication”(joint project with Prof. Irit Dinur, Weizmann Institute) for 3 years (2014–2017).

• Indo-US Joint Center for Research on Pseudorandomness in Computer Science (joint project led byProf. A. Bhattacharyya (IISc, Bangalore) and Prof. S. Lovett (Univ. California, San Diego) for 2 years(2017–2019)).

• Swarnajayanti Fellowship for “Locally testable codes: constructions and limitations” DST fellowshipfor 5 years (2017–2022).

Sandeep K. Juneja

• Yahoo Academic Research Grant for the year 2009–2010.

• Consultancy with Capital Metrics and Risk Solutions, Pune, India. Sep 11 – July 12.

Kavitha Telikepalli

• Max Planck Society partner group on “Efficient Graph Algorithms” from 2008-2013. This had DSTfunding also till 2010 (while at IISc, Bangalore).

• Principal Investigator of the IMPECS (Indo-German Max Planck Centre for Computer Science) Re-search Group in Algorithms and Complexity at TIFR from 2010-2015.

grants and projects

Hariharan Narayanan

• NSF Grant on Fitting Manifolds to Noisy data, Sep 2016 to Aug 2019.

• Ramanujan Fellowship, Department of Science and Technology, India, July 2017 – June 2021.

Paritosh K. Pandya

Current External Projects

• Lead Investigator of the sub-project Requirement Modelling Formalisms CFDVS, IIT Bombay (2011-2016). Agency: BRNS. Overall Funding: 3.1 Crore. (February 2010 to March 2019).

• Principal Investigator, Centre of Formal Design and Verification of Software (CFDVS), IIT Bombay. Agency:BRNS (Since 1999-Present)

Past External Projects and Grants

• Principal Investigator, Design and implementation of DIFC security architecture for securing linux likeoperating systems. Agency: DRDO. Funding 44.80 Lakhs. (16/1/2015 to 16/3/2016)

• Principle Investigator, Laboratory for Construction, Analysis and Verification of Embedded Systems, XI Planproject, TIFR (2007-2012).

• Principal Investigator, SCADE Runtime Verification and Testing at CFDVS, IIT Bombay (2010-2013).Sponsors: BRNS. Funding Rs. 19 Lakh.

• Principle Investigator, Advanced Research on Formal Analysis of Hybrid Systems, Project sponsored byGeneral Motors India Science Lab, Bangalore, India(2006-2009). Funding 50,000 US$.

• Principle Investigator, Formal Sepcification and Analysis of Component Based Designs, Research Grant,Microsoft Research India, (2006-2009). Funding 10,000 US$.

Vinod M. Prabhakaran

• Ramanujan Fellowship, Department of Science and Technology, India, 2011 – 2016.

• Information Technology Research Academy (ITRA), India - Uncoordinated, Secure and Energy AwareAccess in Distributed Wireless Networks (with B. Dey, S. Bhaskaran, R. Vaze, S. Kundu, and S.Acharya), 2013-16.

• Indo-Israel Joint Research Cooperation Programme (with M. Prabhakaran, S. Agrawal, Y. Ishai, E.Kushilevitz, and A. Rosen), 2017-2019.

N. Raja

Current External Projects

• Principal Investigator, Centre for Formal Design and Verification of Software (CFDVS), IIT Bombay.Agency: BRNS (2001-Present).

Ramprasad Saptharishi

• Ramanujan Fellowship, Department of Science and Technology, India, 2017 – present.

Piyush Srivastava

• Ramanujan Fellowship, Department of Science and Technology, India, 2018 – present.

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student fellowships

Rahul Vaze

• Uncoordinated, secure and energy aware access in distributed wireless networks, DeiTy, Govt. ofIndia, 2014-17. Co-PI’s Prof. Bikash Dey, Dr. Sibi Pillai, IIT Bombay, Dr. Vinod Prabhakaran, TIFR-Mumbai, Dr. Sripati Acharya, NIT-Surathkal, Dr. Sumeet Kundu, NIT-Durgapur.

• Indian National Science Academy’s Young Scientist Award Grant: Design of Efficient Spatial WirelessNetworks using Stochastic Geometric tools, 2014-18

• CEFIPRA Indo-French joint program on D2D Communications for LTE-Advanced Cellular Networks.2015-18, co-PI’s Prof. Neelesh Mehta, Prof. Chandra Murthy, Indian Institute of Science, Prof. KetanRajawat, IIT Kanpur, Marceau Coupechoux, Telecom Paris Tech, Amira Alloum, Nokia Siemens,Cedric Adijh Inria.

Student Fellowships

• Nikhil Mande (2016-18), TCS Research Scholarship

• Deepesh Data (2014-17), Microsoft PhD Fellowship

• Sagnik Mukhopadhyay (2014-17), TCS Research Scholarship

• Gugan Thoppe (2013), IBM PhD Fellowship

• Karthyek R. A. Murthy (2013), IBM PhD Fellowship

• Ankush Agarwal (2012-15), TCS Research Scholarship

• Girish Varma (2012-16), Google PhD Fellowship

• Saswata Shannigrahi (2009), IBM PhD Fellowship

• Kishore Barman (2008-10), Infosys Fellowship

• Sameer Kamal (2007-09), Infosys Fellowship

• Shivali Agarwal (2005-07), IBM CAS Sponsorship

• Chinmoy Dutta (2006), IBM PhD Sudent Assistantship Award

• Arnab Basu (2005-07), Infosys Fellowship

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