Welcome and Schedule

Haesun ParkComputational Science and Engineering School

Georgia Institute of Technology

FODAVA Annual Meeting, Dec. 9-10, 2010

The FODAVA Mission: To develop and advance the mathematical and computational foundations of data and visual analytics through innovative research, educational programs, and the development of workforce to address the challenges of extracting knowledge from massive, complex data.

The FODAVA Mission: To develop and advance the mathematical and computational foundations of data and visual analytics through innovative research, educational programs, and the development of workforce to address the challenges of extracting knowledge from massive, complex data.

FODAVA ‘08 Partners: Welcome back!• Global Structure Discovery on Sampled Spaces

Leonidas Guibas , Gunnar Carlsson (Stanford University)

• Visualizing Audio for Anomaly Detection

Mark Hasegawa-Johnson, Thomas Huang, Hank Kaczmarski, Camille Goudeseune (University of Illinois Urbana-Champaign)

• Principles for Scalable Dynamic Visual Analytics

H. Jagadish, George Michailidis (University of Michigan)

• Efficient Data Reduction and Summarization

Ping Li (Cornell University)

• Uncertainty-Aware Data Transformations for Collaborative Reasoning

Kwan-Liu Ma (UC Davis)

• Mathematical Foundations of Multiscale Graph Representations and Interactive Learning

Mauro Maggioni, Rachael Brady, Eric Monson (Duke University)

• Visually-Motivated Characterizations of Point Sets Embedded in High-Dimensional Geometric Spaces

Leland Wilkinson , Robert Grossman (University of Illinois Chicago)

Adilson Motter (Northwestern University)

FODAVA ‘09 Partners: Welcome back!• Formal Models, Algorithms, and Visualizations for Storytelling

Naren Ramakrishnan, Christopher L North, Francis Quek (Virginia Tech)• New Geometric Methods of Mixture Models for Interactive Visualization

Jia Li, Bruce Lindsay, Xiaolong (Luke) Zhang (Penn State University)• Differential Geometry Approach for Virus Surface Formation, Evolution and

Visualization

Guowei Wei, Yiying Tong, Yang Wang (Michigan State University)• Scalable Visualization and Model Building

William S Cleveland (Purdue University) ,Pat Hanrahan (Stanford)• Foundations of Comparative Analytics for Uncertainty in Graphs

Lise Getoor (University of Maryland), Lisa Singh (Georgetown University), Alex Pang (Univ. of California – Santa Cruz)

• Interactive Discovery and Semantic Labeling of Patterns in Spatial Data

Thomas A Funkhouser, David Blei, Christiane D Fellbaum, Adam Finkelstein (Princeton University)

• Visualization of Analytic Processes

Ole Mengshoel, Marija D Ilic, Edwin Selker (Carnegie Mellon University)• Bayesian Analysis in Visual Analytics (BAVA)

Scotland C Leman, Leanna L House, Christopher L North (Virginia Tech)

FODAVA ‘10 New Partners: Welcome!

• Manifold Alignment of High-Dimensional Data Sets

Sridhar Mahadevan and Rui Wang (U of Massachusetts, Amherst)

• Multi-Source Visual Analytics

Jieping Ye, Anshuman Razdan, Peter Wonka (Arizona State University)

• Modeling the Uncertainty due to Data/Visual Transformations using Sensitivity Analysis

Kwan-Liu Ma and Carlos Correa (U of California – Davis)

Total 8 (‘08) + 8 (‘09) + 3 (‘10) = 19 projects

Large-scale Graph and Network Data

Large-scale Graph and Network Data

Managing Scale: Massive Data Volume, High Dimensionality, Integration of

Heterogeneous Data

Managing Scale: Massive Data Volume, High Dimensionality, Integration of

Heterogeneous Data

Large-scale Image, Audio, Spatial, and Temporal DataLarge-scale Image, Audio,

Spatial, and Temporal DataInteraction and Visual Reasoning ApproachesInteraction and Visual Reasoning Approaches

Clique tree growth as function of moral edges

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Large-scale Graph and Network DataLarge-scale Graph and Network Data

- Increasing complexity in data relationships require multi-level and complex dynamical analysis - Uncertainty and imprecision pose further challenges in analysis and reasoning - Application examples: communication, social, financial and biological network analysis

- Increasing complexity in data relationships require multi-level and complex dynamical analysis - Uncertainty and imprecision pose further challenges in analysis and reasoning - Application examples: communication, social, financial and biological network analysis

Foundations of Comparative Analytics for Uncertainty in Graphs

Lise Getoor (University of Maryland), Lisa Sing (Georgetown University), Alex Pang (Univ. of California –

Santa Cruz)

• Uncertainty• Large scale graph• Network analysis

Mathematical Foundations of Multiscale Graph Representations and Interactive Learning

Mauro Maggioni, Rachael Brady, Eric Monson (Duke University)

• Multi-scale data representation• High-dimensional and large scale graph problems• Interaction modeling

Principles for Scalable Dynamic Visual Analytics

H. Jagadish, George Michailidis (University of Michigan)

• Dynamic data• Large scale graph/network

Managing Scale: Massive Data Volume, High Dimensionality,Integration of Heterogeneous Data

Managing Scale: Massive Data Volume, High Dimensionality,Integration of Heterogeneous Data

- These are critical aspects of modern datasets which are continuing to increase dramatically- These scale obstacles often prevent more than simplistic analysis and interpretation- Application examples: astronomy, drug screening, defense, text analysis, image analysis, …

- These are critical aspects of modern datasets which are continuing to increase dramatically- These scale obstacles often prevent more than simplistic analysis and interpretation- Application examples: astronomy, drug screening, defense, text analysis, image analysis, …

Manifold Alignment of High Dimensional Data Sets

Sridhar Mahadevan and Rui Wang (UMass, Amherst)

• Transfer learning• Aligning multiple

heterogeneous data sets• Extraction of shared latent

semantic structure

Visually-Motivated Characterizations of Point Sets Embedded in High-Dimensional

Geometric Spaces

Leland Wilkinson, Robert Grossman(University of Illinois Chicago), Adilson Motter (Northwestern University)

• Mathematical modeling of visualization

• Geometric and graph theory• Visually based transformations

Dimension Reduction and Data Reduction: Foundations for

Visualization

Haesun Park, John Stasko, Renato Monteiro, Vladimir Koltchinskii, Alexander

Gray (Georgia Tech)

• Dimension reduction• Data reduction• Information fusion• Manifold learning• Application to text and image data

analysis

New Geometric Methods of Mixture Models for Interactive

Visualization

Jia Li, Bruce Lindsay, Xiaolong (Luke) Zhang(Penn State University)

• Geometry of mixture models• Clustering• Dimension reduction• Data summarization

Multi-Source Visual Analytics

Jieping Ye, Anshuman Razdan, Peter Wonka (Arizona State University)

• Dimension reduction• Clustering• Multiple kernel learning• Fusion of heterogeneous data

Efficient Data Reduction and Summarization

Ping Li (Cornell University)

• Data reduction• Summarization

Large-scale Image, Audio, Spatial, and Temporal DataLarge-scale Image, Audio, Spatial, and Temporal Data

- Image and audio data are ever-important and increasingly voluminous- Spatial and temporal data require consideration of their unique structure- Application examples: surveillance, geospatial, biomolecular

- Image and audio data are ever-important and increasingly voluminous- Spatial and temporal data require consideration of their unique structure- Application examples: surveillance, geospatial, biomolecular

Global Structure Discovery on Sampled Spaces

Leonidas Guibas , Gunnar Carlsson (Stanford University)

• Topology and geometry for structure discovery

• Image study

Differential Geometry Approach for Virus Surface Formation, Evolution and

Visualization

Guowei Wei, Yiying Tong, Yang Wang (Michigan State University)

• Viral epidemics and pandemics• Multiscale framework for massive scale

problems• Biology Applications

Visualizing Audio for Anomaly Detection

Mark Hasegawa-Johnson, Thomas Huang, Hank Kaczmarski, Camille Goudeseune (University of Illinois Urbana-Champaign)

• Statistical modeling• Audio visualization• Anomaly detection

Interactive Discovery and Semantic Labeling of Patterns in Spatial Data

Thomas A Funkhouser, David Blei, Christiane D Fellbaum, Adam Finkelstein (Princeton University)

• Labeling of semantic patterns in large spatial data

• Interaction methods

Interaction and Visual Reasoning ApproachesInteraction and Visual Reasoning Approaches- New approaches to interaction with data are needed- Ways to integrate/extract knowledge (such as priors and uncertainty) visually - Application examples: intelligence, public health, network security

- New approaches to interaction with data are needed- Ways to integrate/extract knowledge (such as priors and uncertainty) visually - Application examples: intelligence, public health, network security

Visualization of Analytic Processes

Ole Mengshoel, Marija D Ilic, Edwin Selker (Carnegie Mellon University)

• Interaction methods• Graph model• Bayesian networks

Formal Models, Algorithms, and Visualizations for Storytelling

Naren Ramakrishnan, Christopher L North, Francis Quek (Virginia Tech)

• Modeling of interaction for story telling

Scalable Visualization and Model Building

William S Cleveland (Purdue University), Pat Hanrahan (Stanford)

• Interaction modeling• Scalability in data visualization• Application to public health• Internet network security

Bayesian Analysis in Visual Analytics (BAVA)

Scotland C Leman, Leanna L House, Christopher L North (Virginia Tech)

• Data transformation based on probabilistic Bayesian methods• Visualization modeling• Application to intelligence analysis

Uncertainty-Aware Data Transformations for Collaborative

Reasoning

Kwan-Liu Ma (U of California – Davis)

• Uncertainty representation in network data• Visual reasoning

Modeling the Uncertainty due to Data/Visual Transformations using

Sensitivity Analysis

Kwan-Liu Ma and Carlos Correa (U of California – Davis)

• Uncertainty and sensitivity analysis in visual analytics process

• Scalable visual representations of sensitivity

Thursday – December 9• 08:30 – 09:00 Registration and Breakfast

• 09:00 – 09:10 Welcome (Larry Rosenblum, NSF)

• 09:10 – 09:30 Welcoming Remarks and Updates (Haesun Park),

• 09:30 – 10:00 Visual Analytics Activities at DHS (Joe Kielman, DHS)

• 10:00 – 11:10 Research Vignettes (Year 1 Awardees; 10 minute overview per project)

• 11:10 – 11:30 Break

• 11:30 – 12:00 VAST Visualization Contest Summary (Stasko, Georgia Tech)

• 12:00 – 13:00 LUNCH at Klaus Atrium

• 13:00 – 14:00 Research Vignettes/Educational Activities/Community Building Activities (FODAVA Lead – Georgia Tech)

• 14:00 – 14:45 Talk – Pat Hanrahan (Title tbd)

• 14:45 – 15:00 Break

• 15:00 – 17:00 Posters (Year 1 and FODAVA Lead) and Discussion at Klaus Atrium

• 18:00 Cash Bar, STEEL restaurant

• 18:30 Dinner, STEEL restaurant

Thursday December 9, 201010:00-11:10 Research Vignettes (Year 1 Awardees)

•10:00-10:10 Global Structure Discovery on Sampled Spaces (Stanford)

•10:10-10:20 Visualizing Audio for Anomaly Detection (Illinois Urbana-Champaign)

•10:20-10:30 Principles for Scalable Dynamic Visual Analytics (Michigan)

•10:30-10:40 Efficient Data Reduction and Summarization (Cornell)

•10:40-10:50 Uncertainty-Aware Data Transformations for Collaborative Reasoning (UC Davis)

•10:50-11:00 Mathematical Foundations of Multiscale Graph Representations and Interactive Learning (Duke)

•11:00-11:10 Visually-Motivated Characterizations of Point Sets Embedded in High-Dimensional Geometric Spaces (UIC/Northwestern)

STEEL RestaurantDirections from the Georgia Tech Hotel to STEEL Restaurant

Walking directions• Go north on Spring

Street • Turn right onto 5th

Street when you reach the Barnes and Nobles.

• Turn left and go north on W. Peachtree St.

• It is located north of the 8th St intersection and south of the Peachtree Place intersection.

Friday, December 10• 08:00-08:30 Breakfast, Klaus Atrium• 08:30– 9:45 New Projects (Year 3 Awardees)

– 08:30 - 08:55 Manifold Alignment of High-Dimensional Data Sets (UMass-Amherst)

– 08:55 - 09:20 Multi-Source Visual Analytics (Arizona State) – 09:20 - 09:45 Modeling the Uncertainty Due to Data/Visual

Transformations Using Sensitivity Analysis (UC Davis) • 09:45 – 10:00 Jim Thomas -- In Memorium (Cook et al.) • 10:00 – 10:15 Break • 10:15 – 11:35 Research Vignettes (Year 2 Awardees)• 11:35 – 11:45 Upcoming FODAVA solicitation (Larry) • 11:45 – 12:45 LUNCH

• 12:45 – 2:15 Posters (Year 2) and Discussion • 2:15 – 2:30 Final Remarks (Larry, Joe, Haesun) • 2:30 ADJOURN • 2:30 – 3:00 Management Team Meeting

Friday, December 10• 10:15 – 11:35 Research Vignettes (Year 2 Awardees)

– 10:15-10:25 Formal Models, Algorithms, and Visualizations for Storytelling (Virginia Tech)

– 10:25-10:35 New Geometric Methods of Mixture Models for Interactive Visualization (Penn State)

– 10:35-10:45 Differential geometry approach for virus surface formation, evolution and visualization (Michigan State)

– 10:45 - 10:55 Scalable Visualization and Model Building (Purdue/Stanford)

– 10:55 - 11:05 Foundations of Comparative Analytics for Uncertainty in Graphs (Maryland/Georgetown/UC Santa Cruz)

– 11;05-11:15 Interactive Discovery and Semantic Labeling of Patterns in Spatial Data (Princeton)

– 11:15 - 11:25 Visualization of Analytic Processes (Carnegie Mellon) – 11:25 - 11:35 Bayesian Analysis in Visual Analytics (Virginia Tech)