MAURO MAGGIONI

Department of Mathematics, Department of Applied Mathematics and Statistics,Mathematical Institute for Data Sciences, Institute for Data Intensive Engineering and Sciences,Johns Hopkins University3400 N Charles St, Baltimore, MD,21218E-mail: mauromaggioniit@icloud.comHome page: https://mauromaggioni.duckdns.org

RESEARCH INTERESTS ———————————————————————–I am interested in a variety of problems in the mathematical foundations of Data Science, motivated bythe need to exploit data to advance scientific discovery and build generalizable, interpretable predictivemodels. These problems require ideas and techniques from a variety of areas, including HarmonicAnalysis, Approximation Theory, Probability and Statistics. Scalable algorithms are a requirement, andI often use multiscale techniques to develop near-linear time algorithms to implement efficiently theestimators developed in theory.

I apply these techniques to the study of physical systems, e.g. to the analysis of molecular dynamicsdata in order to automatically learn reduced models or speed up simulations, or to infer models ofcomplex agent-based systems (e.g. to model cell dynamics), to the study of hyperspectral images(e.g. for unsupervised segmentation or anomaly detection), to reinforcement learning (to learn optimalpolicies for automated agents).

POSITIONS HELD ———————————————————————–

2016- Bloomberg Distinguished Professor in the Departments of Mathematics and AppliedMathematics and Statistics, Johns Hopkins University.

2012-2016 Professor in Mathematics, Electrical and Computer Engineering, Computer Science,Duke University.

2006-2012 Assistant Professor in Mathematics and Computer Science, Duke University.2005-2006 Research Scientist, Applied Mathematics, Yale University.2004-2005 Gibbs Assistant Professor, Department of Mathematics, Yale University.2004 (Fall) Fellow, Institute Pure and Applied Mathematics, UCLA, Program in Multiscale

Geometry and Analysis in High Dimensions.2002-2004 Gibbs Instructor, Department of Mathematics, Yale Univ.. Mentor: R.R. Coifman.2002- Director, MPIM Consultants Limited, a U.K.-based scientific software company.2001-2002 Software Engineer and project team leader, Mettler-Toledo-Myriad, Melbourn,

U.K., design of software for robots for automated chemistry.2000-2001 Graduate Teaching Assistantship, Washington University, St.Louis.

EDUCATION ———————————————————————–

2002, May Ph.D. in Mathematics, Washington University, St.Louis, Discretization of continuouswavelet transforms, advisor: Prof. G. L. Weiss. Area: harmonic analysis.

2000, Dec M.Sc., Washington University, St. Louis.1999, July Laurea (M.Sc.) cum laude in Mathematics, Universita’ di Milano, Italy, M -band

compactly supported biorthogonal wavelets and Burt-Adelson wavelets, under thesupervision of Prof. P. M. Soardi.

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HONORS, AWARDS ———————————————————————–

2013- : Fellow of the American Mathematical Society.2012-2013: Thomas Langford Lecturship Award, Duke University.2008-2012: Sloan Fellowship.2007: Popov prize in approximation theory.1999-2000: The Albert and Judith Fay Ross Memorial Fellowships, Washington University, St.Louis.

GRANTS (RECENT) ———————————————————————–

2018-2021: NSF BIGDATA Compositional Learning, Maps and Transfer: Statistical and Machine Learning onCollections of Data Sets, PI.2017-2020: NSF ATD Estimation and Anomaly Detection for high-dimensional Data, Maps and Dynamic Pro-cesses, PI.2017-2020: AFOSR Information, Approximation, and Fast Algorithms for Data in High Dimensions, PI.2018-2019: ARO STTR, Phase II, lead by I-A-I Inc., Multiscale Fast and Distributed Data and Statistics Summa-rization, PI.2018-2019: JHU DISCOVERY award: Novel Methods for Non-Invasive Assessment of Myocardial Fibrosis Com-plexity and Disorganization to Predict Ventricular Arrhythmias, Co-PI.2017-2018: ARO STTR, Phase I, lead by I-A-I Inc., Multiscale Fast and Distributed Data and Statistics Summa-rization, PI.2017-2018: JHU DISCOVERY award: Anomaly Detection for Zero Shot Learning with Applications to Rare Myo-pathic Disease Diagnostics, PI.2015-2018: NSF BIGDATA: Collaborative Research: From Data Geometries to Information Networks, PI.2015-2018: NSF Statistical Learning for High-Dimensional Stochastic Dynamical Systems, PI.2014-2016: AFOSR Information, Approximation, and Fast Algorithms for Data in High Dimensions, PI.2013-2016: NSF Structured Dictionary Models and Learning for High Resolution Images, PI.2013-2016: NSF Collaborative Proposal: SI2-CHE: Extensible tools for advanced sampling and analysis, PI.2012-2016: NSF ATD: Online Multiscale Agorithms for Geometric Density Estimation in High-Dimensions andPersistent Homology of Data for Improved Threat Detection, PI.

PUBLICATIONS ———————————————————————–

P. Escande, M. Maggioni, Multiscale Decomposition of Transformations, in preparation, 2018.

M. Maggioni, J.M. Murphy, Learning by unsupervised nonlinear diffusion, submitted, 2018.

F. Lu, M. Zhong, S. Tang, M. Maggioni, Nonparametric inference of interaction laws in systems of agents from trajectory data,accepted for publication in P.N.A.S., 2019.

J.M. Murphy, M. Maggioni, Unsupervised Clustering and Active Learning of Hyperspectral Images With Nonlinear Diffusion,IEEE Trans. Geoscience and Remote Sensing. 2018. DOI: 10.1109/TGRS.2018.2869723.

W. Liao, M. Maggioni, S. Vigogna, Multiscale regression on unknown manifolds, submitted, 2018.

S. Vigogna, M. Maggioni, A. Lanteri, Conditional Regression for Single-Index Models, in preparation, 2018.

F. Lu, M. Maggioni, S. Tang, Learning governing laws in first order interacting agent systems: a Monte Carlo Approach, inpreparation, 2018

J. T. Vogelstein, E. W. Bridgeford, Q. Wang, C. E. Priebe, M. Maggioni, C. Shen, Discovering and Deciphering RelationshipsAcross Disparate Data Modalities, eLife 2019;8:e41690.

A. Little, M. Maggioni, J. Murphy, Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms,submitted, 2017.

J. M. Murphy, M. Maggioni, Diffusion geometric methods for fusion of remotely sensed data, Algorithms and Technologies forMultispectral, Hyperspectral, and Ultraspectral Imagery XXIV, 10644, 106440I. International Society for Optics and Photonics,2018.

J. M. Murphy, M. Maggioni, Nonlinear Unsupervised Clustering of Hyperspectral Images with Applications to Anomaly Detec-tion and Active Learning, accepted, IEEE Trans. Geosci. Rem. Sens., 2018.

J.T. Vogelstein, E. Bridgeford, M. Maggioni, M. Tang, D. Zheng, R. Burns, Geometric Dimensionality Reduction for SubsequentClassification, submitted, 2018.

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W. Liao and M. Maggioni, Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data, https://arxiv.org/abs/1611.01179, accepted for publication in Journ. Mach. Res..

S. Gerber and M. Maggioni, Multiscale Strategies for Discrete Optimal Transport, Journ. Mach. Learn. Res. 18, 2017: 1-32.

M. Bongini, M. Fornasier, M. Hansen, and M. Maggioni, Inferring Interaction Rules from Observations of Evolutive Systems I:The Variational Approach, Mathematical Models and Methods in Applied Sciences 27, no. 05 (May 2017): 909-951.

M. Maggioni, Geometric and Graph methods for High-dimensional Data, PCMI Lectures series, to appear in 2018.

M. Crosskey, M. Maggioni, ATLAS: A geometric approach to learning high-dimensional stochastic systems near manifolds,Multiscale Modeling & Simulation 15, no. 1, 2017: 110-156.

T. Tomita, M. Maggioni, J. Vogelstein, ROFLMAO: Robust Oblique Forests with Linear MAtrix Operations, Proc. SIAM DataMining, 2017.

W. Liao, M. Maggioni, S. Vigogna, Learning adaptive multiscale approximations to data and functions near low-dimensionalsets, IEEE Information Theory Workshop (ITW), 226-230, 2016.

W.N. Goetzmann, P.W. Jones, M. Maggioni, and J. Walden, Beauty is in the bid of the beholder: An empirical basis for style,Research in Economics 70.3: 388-402, 2016.

Y. (Grace) Wang, G. Chen, M. Maggioni, High Dimensional Data Modeling Techniques for Detection of Chemical Plumes andAnomalies in Hyperspectral Images and Movies, IEEE Journ. Selected Topics in Applied Earth Observations and RemoteSensing, 9(9), Sep 2016.

M. Maggioni, S. Minsker, N. Strawn, Dictionary Learning and Non-Asymptotic Bounds for Geometric Multi-Resolution Analysis,Jour. Mach. Lear. Res., 43-93, 2016.

S. Gerber, M. Maggioni, Multiscale dictionaries, transforms, and learning in high-dimensions, Proc. SPIE 8858, Wavelets andSparsity XV, 88581T, 2013

G. Chen, A.V. Little, M. Maggioni, Multi-Resolution Geometric Analysis for Data in High Dimensions, in Excursions in HarmonicAnalysis, Birkhaüser Boston, 2013

A. Coppola, B. Wenner, R. Stevens, O. Ilkayeva, M. Maggioni, T. Slotkin, E. Levin, C. Newgard, Branched-chain amino acidsalter neurobehavioral function in rats, Amer. Journ. Phys., Endoc. and Metab., 2013.

K. Krishnamurthy, A. Mrozack, M. Maggioni, and D. Brady, Multiscale, dictionary-based speckle denoising, in Proc. Compu-tational Optical Sensing and Imaging, 2013

M. A. Iwen and M. Maggioni, Approximation of Points on Low-Dimensional Manifolds Via Random Linear Projections Informa-tion and Inference, 2013.

M. Maggioni, Geometric Estimation of Probability Measures in High-Dimensions, Proc. IEEE. Asilomar conf., 2013.

J. Bouvrie and M. Maggioni, Multiscale Markov Decision Problems: Compression, Solution, and Transfer Learning, ArXiv,submitted, 2013

J. Bouvrie and M. Maggioni, Geometric Multiscale Reduction for Autonomous and Controlled Nonlinear Systems, Proc. Conf.Decision & Control, 2012.

J. Bouvrie and M. Maggioni, Machine Learning and Approximate Dynamic Programming; Optimization; Stochastic Systemsand Control, accepted, Annual Allerton Conference on Communication, Control, and Computing, 2012.

G. Chen, M. Iwen, S. Chin, M. Maggioni, A Fast Multiscale Framework for Data in High Dimensions: Measure Estimation,Anomaly Detection and Compressive Measurements, Proc. V.C.I.P. 2012.

M. Maggioni, What is. . .Data Mining?, in A.M.S. Notices, Apr. 2012.

A.V. Little, M. Maggioni and L. Rosasco, Multiscale Geometric Methods for Data Sets I: multiscale covariances of noisysamples and intrinsic dimension, under revision, 2011

G. Chen and M. Maggioni, Multiscale Geometric and Spectral Analysis of Plane Arrangements, Proc. CVPR, 2011

W. Zheng, M. A. Rohrdanz, M. Maggioni and C. Clementi, Polymer reversal rate calculated via locally scaled diffusion map inJourn. Chem. Phys., 134 2011: 144108.

M. A. Rohrdanz, W. Zheng, M. Maggioni and C. Clementi, Determination of reaction coordinates via locally scaled diffusionmap in Journ. Chem. Phys., 134 2011: 124116.

W.K. Allard, G. Chen and M. Maggioni, Multiscale Geometric Methods for Data Sets II: Geometric wavelets, Vol. 32(3),435-462, Appl. Comp. Harm. Anal., May 2012

G. Chen, A.V. Little, M. Maggioni and L. Rosasco, Some recent advances in multiscale geometric analysis of point clouds, inWavelets and Multiscale Analysis: Theory and Applications, Springer Verlag, 2011

E.E. Monson, G. Chen, R. Brady and M. Maggioni, Data representation and exploration with Geometric Wavelets, VisualAnalytics Science and Technology (VAST), 2010 IEEE Symposium, 243–244, 2010,

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G. Chen and M. Maggioni, Multiscale Geometric Dictionaries for Point-Cloud Data, Proc. SampTA, 2011

J. Lee and M. Maggioni, Multiscale Analysis of Time Series of Graphs, Proc. SampTA, 2011

A.V. Little, M. Maggioni and L. Rosasco, Multiscale Geometric Methods for Estimating Intrinsic Dimension, Proc. SampTA,2011

J Guinney, P Febbo, M. Maggioni, S Mukherjee Multiscale factor models for molecular networks, Proc. JSM, 2010.

E Monson, R Brady, G Chen, M. Maggioni Exploration & Representation of Data with Geometric Wavelets, Poster & shortpaper at Visweek, 2010.

G. Chen, M. Maggioni Multiscale Geometric Wavelets for the Analysis of Point Clouds, Proc. C.I.S.S., 2010.

A V Little, J Lee, YM Jung, M. Maggioni Multiscale Estimation of Intrinsic Dimensionality of Data Sets, Proc. A.A.A.I., Nov.2009.

R R Coifman, M. Maggioni Geometry analysis and signal processing on digital data, emergent structures, and knowledgebuilding, SIAM news, Dec. 2008.

P. W. Jones, M. Maggioni, R Schul. Universal local manifold parametrizations via heat kernels and eigenfunctions of theLaplacian, Ann. Acad. Scient. Fen., Vol. 35:1–44, 2010. http://arxiv.org/abs/0709.1975.

W. Willinger, R. Rejaie, M. Torkjazi, M. Valafar, and M. Maggioni. Research on Online Social Networks: Time to Face the RealChallenges, Proc. HotMetrics’09, 2009.

P. W. Jones, M. Maggioni, R Schul. Manifold parametrizations by eigenfunctions of the Laplacian and heat kernels, P.N.A.S.,Vol. 105, 6, Feb 2008.

R R Coifman, I G Kevrekidis, S Lafon, M. Maggioni, B. Nadler. Diffusion Maps, reduction coordinates and low dimensionalrepresentation of stochastic systems, SIAM J.M.M.S., 7(2): 842–864, 2008.

M Mahoney, M. Maggioni, and P Drineas. Tensor-CUR Decompositions for Tensor-Based Data. to appear in SIAM Jour. onMat. Anal. and Appl., 2007.

R. R. Coifman, S Lafon, M. Maggioni, Y Keller, A D Szlam, F J Warner, S W Zucker. Geometries of sensor outputs, inferenceand information processing, Proc. SPIE, Vol. 6232, 623204, May 2006.

R. R. Coifman, M. Maggioni, S W Zucker, and I G Kevrekidis. Geometric diffusions for the analysis of data from sensornetworks, Curr. Opin. Neurobiol., 15(5):576–84, Oct 2005.

M. Maggioni, J C Bremer Jr, R. R. Coifman, and A D Szlam. Biorthogonal diffusion wavelets for multiscale representations onmanifolds and graphs, Proc. SPIE Wavelet XI, Vol 5914, 59141M, Sep 2005.

M. Maggioni, A D Szlam, R. R. Coifman, and J C Bremer Jr. Diffusion-driven multiscale analysis on manifolds and graphs:top-down and bottom-up constructions. Proc. SPIE Wavelet XI, Vol 5914, 59141D, Sep 2005.

J C Bremer, R. R. Coifman, M. Maggioni, and A D Szlam. Diffusion wavelet packets. Appl. Comp. Harm. Anal., 15(1):95–112.

R. R. Coifman and M. Maggioni. Diffusion wavelets for multiscale analysis on graphs and manifolds. Proc. Conf. InteractionsSplines and Wavelets, Oct. 2005.

R. R. Coifman and M. Maggioni. Diffusion wavelets. Appl. Comp. Harm. Anal., 21(1):53–94, July 2006.

R. R. Coifman, S Lafon, A Lee, M. Maggioni, B Nadler, F J Warner, and S W Zucker. Geometric diffusions as a tool forharmonic analysis and structure definition of data. part i: Diffusion maps. Proc. of Nat. Acad. Sci., 102:7426–7431, May2005.

R. R. Coifman, S Lafon, A Lee, M. Maggioni, B Nadler, F J Warner, and S W Zucker. Geometric diffusions as a tool forharmonic analysis and structure definition of data. part ii: Multiscale methods. Proc. of Nat. Acad. Sci., 102:7432–7438,May 2005.

M. Maggioni Quantitative bounds on the perturbation of the eigenvalues and eigenfunctions of the Laplacian on graphs andmanifolds under rough isometries, in preparation.

M. Maggioni, H N Mhaskar Diffusion polynomial frames on metric measure spaces, Appl. Comp. Harm. Anal., 24(3): 329–353, 2007.

M. Maggioni. Wavelet frames on groups and hypergroups via discretization of Calderón formulas, Monats. Mat., (143):299–331, 2004.

N H Katz, E Krop, and M. Maggioni. On the box problem, Math. Research Letters, 4:515–519, 2002.

M. Maggioni. M-band Burt-Adelson wavelets, Appl. Comput. Harm. Anal., 3:286–311, 2000.

C K Chui, W Czaja, M. Maggioni, and G L Weiss. Characterization of tight wavelet frames with arbitrary matrix dilations andtightness preserving oversampling, J. Four. Anal. App., 8(2):173–200, 2002.

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M. Maggioni. Critical exponent of short even filters and biorthogonal Burt-Adelson wavelets, Monats. Math., 131(1):49–70,2000.

M. Maggioni, F J Warner, G L Davis, R. R. Coifman, F B Geshwind, A C Coppi, and R A DeVerse. Hyperspectral MicroscopicAnalysis of Normal, Adenoma and Carcinoma Microarray Tissue Sections, SPIE Optical Biopsy, 2006, Vol. 6091, 60910I

E Causevic, R. R. Coifman, R Isenhart, A Jacquin, E R John, M. Maggioni, L S Prichep, F J Warner, QEEG-based classifica-tion with wavelet packets and microstate features for triage applications in the ER, Proc. ICASSP, 2006.

Q. Wu, J. Guinney, M. Maggioni, S. Mukherjee, Learning Gradients: Predictive Models that Infer Geometry and StatisticalDependence, Jour. Mach. Learn. Res. 11: 2175-2198, 2010

A.D. Szlam, M. Maggioni, R R Coifman, Regularization on graphs with function adapted diffusion processes. Journ. Mach.Learn. 9(Aug):1711–1739, 2008

S. Mahadevan, M. Maggioni. Proto-value Functions: A Laplacian Framework for Learning Representation and Control inMarkov Decision Processes. Journ. Mach. Learn. (8): 2169–2231, 2007

S. Mahadevan, K Ferguson, S Osentoski and M. Maggioni. Simultaneous Learning of Representation and Control In Contin-uous Domains, Proc. AAAI 2006.

S. Mahadevan and M. Maggioni. Value function approximation with diffusion wavelets and Laplacian eigenfunctions, Univ. ofMassachusetts, Dept. of Computer Science TR-2005-38, and Proc. NIPS, 2005.

M. Maggioni and S. Mahadevan. Fast direct policy evaluation using multiscale analysis of Markov diffusion processes, Univ.of Massachusetts, Dept. of Computer Science TR-2005-39, and Proc. ICML 2005.

R J Cassidy, J Berger, M. Maggioni, and R. R. Coifman. Auditory display of hyperspectral colon tissue images using vocalsynthesis models, Proc. 2004 Intern. Conf. Auditory Display, 2004.

S Ferrari, M. Maggioni, and N A Borghese. Multi-scale approximation with hierarchical radial basis functions networks, IEEETrans. on Neural Networks, 15(1):178–188, January 2004.

Non-refereed publications

G. Chen, M. Maggioni, and Y. Wang, Improved Chemical Gas Detection in Hyperspectral Images and Movies, 2013.

A V Little, J Lee, YM Jung, M. Maggioni Estimation of intrinsic dimensionality of samples from noisy low-dimensional manifoldsin high dimensions with multiscale SV D, Proc. S.S.P., Sep. 2009.

G L Davis, M. Maggioni, F J Warner, F B Geshwind, A C Coppi, R A DeVerse, and R. R. Coifman. Hyper-spectral analysisof normal and malignant colon tissue microarray sections using a novel DMD system, Poster, Optical Imaging NIH workshop,Sep 2004.

M. Maggioni, F J Warner, G L Davis, R. R. Coifman, Frank B Geshwind, Andreas C Coppi, and Richard A DeVerse. Algo-rithms from signal and data processing applied to hyperspectral analysis: Application to discriminating normal and malignantmicroarray colon tissue sections, Technical Report 1311, Yale University, Dept. Comp. Sci., Feb 2004.

PROFESSIONAL ACTIVITIES ——————————————————————————–IPAM workshop Collective Variables in Classical Mechanics, Fall 2016, organizer.ICERM workshop Stochastic numerical algorithms, multiscale modeling and high-dimensional data analytics, July 2016, or-ganizer.Duke workshop on Sensing and Analysis of High-Dimensional Data on July 27-29th, 2015, organizer.Hausdorff Research Institute program in Mathematics of Signal Processing, 2016, organizer.Mathematical Biology Institute Semester-long program for Spring 2014, organizer.Duke workshop on Sensing and Analysis of High-Dimensional Data on July 23-25th, 2013, organizer.Workshop on Large Graphs: Modeling, Algorithms and Applications, in the program on Mathematics of Information, Institutefor Mathematics and its Applications, Oct. 2011, organizer.Duke workshop on Sensing and Analysis of High-Dimensional Data on July 26-28th, 2011, organizer.ICIAM Minisymposium on Harmonic Analysis on Graphs and Networks on July 22, 2011, organizer.Workshop on Optimization, Search and Graph-Theoretical Algorithms for Chemical Compound Space, Inst. Pure and Appl.Math., Apr. 2011, organizer.The Mathematics of Information and Knowledge, AMS national meeting, Jan. 2010, organizer.Geometric and Spectral Techniques for Analysis of Graphs, SAMSI working group, Fall 2010.Duke workshop on Large Data Sets: Computation and Structure, CTMS, Nov. 2010, organizer.Symposium on manifold learning, AAAI, Nov. 2009, organizer.Internet Multiresolution program, Inst. Pure and Appl. Math., U.C.L.A., Sep-Dec. 2008, organizer.Document Space workshop, Inst. Pure and Appl. Math., U.C.L.A., Jan. 2006, organizer.Constructive Approximation, editor or; Applied Computational Harmonic Analysis, associate editor for Information & Inference,

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associate editor for Applied and Numerical Harmonic Analysis (ANHA).NSF, ONR, AFOSR, DOE, Australian Research Council, Israel Science Foundation: reviewer/panelist.Reviewer for Appl. Comp. Harm. Anal., Jour. Math. Anal. and Appl., Jour. Comp. Phys., N.I.P.S. 2005, I.E.E.E. Trans. Neur.Net., I.E.E.E. Trans. Image Proc., Jour. Funct. Anal., Jorn. A.M.S.3 U.S. patents pending, one for hyper-spectral imaging, two for data analysis, organization and visualization.High-D Systems, U.K., Director.

TEACHING ———————————————————————–Fall 2018: High-Dimensional Approximation, Probability, and Statistical Learning; Spring 2018: Introduction to HarmonicAnalysis and its Applications; Fall 2017: High-Dimensional Approximation, Probability, and Statistical Learning; Spring 2017:Introduction to Statistical Learning, Data Analysis and Signal Processing; Spring 2016: Advanced Calculus; Fall 2015: Sci-entific Computing I; Spring 2015: Topics in Statistical Learning Theory; Topics in Probability; Fall 2014 :Scientific ComputingI; Spring 2014: Advanced Calculus; Spring 2013: Topics in Statistical Learning, High Dimensional Geometry and RandomMatrices; Fall 2012: Introduction to High Dimensional Data Analysis; Scientific Computing I; Fall 2011: Topics in Probability;Scientific Computing I; Fall 2010: Real Analysis; ...

TRAINING OF STUDENTS ———————————————————————–Master students: Y. Huang, Z. Huang, ’18-; Ph.D students: Jin Zhou, M. P. Martin, Z. Lubberts (co-advisor), J. Miller ’17-;Miles Crosskey, Ph.D. student, 09-14; Prakash Balachandran, Ph.D. on Dimensionality Reduction and Learning on Networks,7/11; Anna V. Little, Ph.D. on Estimating the Intrinsic Dimension of High-Dimensional Data Sets: A Multiscale, GeometricApproach, 5/11; Jason D. Lee, undergraduate thesis advisor, 4/10; Arthur D. Szlam, co-advisor, Ph.D. on Non-StationaryAnalysis on Datasets and Applications, Yale, 5/06.

TRAINING OF POSTDOC’s ———————————————————————–Xiaofeng Ye, ’18-; Ming Zhong, Postdoc, ’16-; Paul Escande, Postdoc, ’16-’18; Sui Tang, Visiting Asst. Prof., ’16-; JamesMurphy, Visiting Asst. Prof., ’15-’18, currently Asst. Prof. at Tufts U.; Stefano Vigogna, Visiting Asst. Prof., ’15-’18; WenjingLiao, Visiting Asst. Prof., ’13-’17, currently Asst. Prof. at GeorgiaTech; Stas Minsker, Visiting Asst. Prof., ’12-’14, currentlyAsst. Prof. at U.S.C.; Samuel Gerber, Visiting Asst. Prof., ’12-15, currently Res. Asst. at University of Oregon; David Lawlor,Visiting Asst. Prof., ’12-15, currently at HERE(Nokia); Joshua Vogelstein, Visiting Asst. Prof., ’12-14, currently Asst. Prof. AtJohns Hopkins; Nate Strawn, Visiting Asst. Prof., ’11-’13, currently Asst. Prof. at Georgetown University; Mark Iwen, VisitingAsst. Prof., ’10-’13, currently Asst. Prof. at Michigan State Univ.; Guangliang Chen, Visiting Asst. Prof., ’09-’13, currentlyVisiting Asst. Prof. at Claremont McKenna College; Jake Bouvrie, Visiting Asst. Prof., ’09-’12; Yoon-Mo Jung, postdoc, 08-10,currently in Comp. Sci. & Eng. at Yonsei Univ.

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