FODAVA-LEAD Updates Haesun Park Computational Science and Engineering Division Georgia Institute of Technology FODAVA Annual Meeting, Dec. 3, 2009.

FODAVA-LEAD Updates

Haesun ParkComputational Science and Engineering Division

Georgia Institute of Technology

FODAVA Annual Meeting, Dec. 3, 2009

FODAVA-Lead PIs at GAtech

Alex GrayAssociate Director

CSEMachine Learning

Fast Algorithms for Massive DAIndustry Relations

Haesun ParkDirector

CSE, Associate ChairNumerical Computing

Data AnalysisResearch, FODAVA Community Building

Vladimir KoltchinskiiMathematics

Machine Learning TheoryComputational Statistics

John StaskoAssociate DirectorIC, Associate Chair

Information Vis.Collaboration with NVAC and DHS/CoE

Liaison with Vis. community

Renato MonteiroISyE

Continuous OptimizationStatistical Computing

FODAVA-Lead Senior Personnel

James Foley Interim Dean CoC

Graphics and Visualization, HCIVisual Analytics Digital Library

Richard FujimotoAssociate Director

CSE, ChairModeling and Simulation Education and Outreach

Guy LebanonCSE

Machine LearningComputational Statistics

Arkadi NemirovskiISyE

OptimizationNon-parametric Stat.

Alexander ShapiroISyE

Stochastic ProgrammingOptimization

Multivariate Stat. Analysis

Santosh VempalaCS

Theory of ComputigDirector of ARC

Hongyuan ZhaCSE

Numerical ComputingData Analysis

Director of Graduate Studies

Hao-Min ZhouMathematics

Wavelet and PDEImage Processing

FODAVA-Lead Missions• Research: Serve as a central facility that will involve all

FODAVA awardees in a common effort to develop the scientific foundations for data and visual analytics

• Education: Facilitate the development of a body of knowledge and associated education programs to establish and build workforce

• Community Building: – Integrate diverse DAVA communities and reach out for

broader participation– Liaison between FODAVA researchers and NVAC, DHS

Center of Excellence

FODAVA Teams

Cornell

DukeGeorgetown

PrincetonCMUUniv. Maryland

Penn State

Virginia Tech

NorthwesternUI-Chicago

UIUC

Univ. Michigan

Michigan State

Purdue

Stanford

UC-Davis

UC-Santa Cruz

Georgia Tech (FODAVA lead)

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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)

Welcome New FODAVA Partners!• 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)

Mathematics, Statistics, Numeric and Geometric Computing, Machine Learning, Optimization, Data Analysis, Discrete Algorithms, Graph Theory, Information Retrieval, Information Visualization, Human Computer Interaction, Database, High Performance Computing, Gaming, Simulation, Cognitive Science, Psychology, …

Toward a Discipline: Data & Visual Analytics

• Body of Knowledge– Foundations, subareas, applications– Curriculum– Education programs

• Community Building– Researchers– Educators– Practitioners

Body of Knowledge: WorkshopDecember 15-16, 2008, Georgia Tech, Atlanta GA (K. Cook, J. Stasko, R. Fujimoto)

Goals• Continue efforts such as VAST

Education workshops• Share experiences to date in visual

analytics curriculum development• Identify major topics in DAVA education

programs

Outcomes• Draft DAVA taxonomy• Refined via subsequent discussion

(J. Thomas, K. Cook, JS, RF, GL, HP,..)

• Next workshop planned, Spring 2010, NVAC consortium meeting

DAVA Curriculum Development( R. Fujimoto, S. Stasko, G. Lebanon, A. Gray, H. Park)

• New course on Data and Visual Analytics (Guy Lebanon) on the interface between data analysis and information visualization. Emphasis is on practical methods and case studies.

• Core graduate courses in DAVA curriculum: New course, existing courses on data analysis and information visualization

• Undergraduate version of Data and Visual Analytics to be incorporated into modeling and simulation thread, possibly creating a new thread eventually.

• CDC short course - Visual Analytics and Architectures in Public Health

Outreach to Underrepresented Groups• GT CRUISE Program (Computing Research

Undergraduate Intern Summer Experience)– Encourage students to consider graduate studies– Diverse student participation

• Multicultural, emphasizing minorities, women• U.S. and international students

– Ten week summer research projects– Interdisciplinary individual and group projects and

CRUISE-wide events• Weekly seminars (technical, grad studies)• Symposium: conference-style presentations• VAST Challenge 2009 Problem resulting in “Best

Analytical Technique” award (J. Choo)

• Year-long collaboration with North Carolina A&T University

• NSF REU Site Proposal Submitted (PI: R. Fujimoto), Joint Educational Effort with NVAC (R. May)

DAVA Community DevelopmentOutreach activities to engage existing research communities

in data and visual analytics• Visualization Community

– Birds-of-Feather Session, VAST Conference, Columbus Ohio, October 2008 (K. Cook, K. Ma, and H. Park)

– Forum on Geometric Aspects of Machine Learning and Visual Analytics: Recent Developments and Future Challenges, VisWeek, Atlantic City, October 11-12, 2009 (M. Maggioni, V. Koltchinskii, A. Varshney, H. Park)

– 2010: A workshop at VisWeek ( D. Keim, G. Lebanon, H. Park ..)

• Data Analysis Community– Statistical Machine Learning for Visual Analytics, NIPS Conference,

Vancouver, B.C., Canada, December 11, 2009 (G. Lebanon …)– Large-Scale Machine Learning: Parallelism and Massive Datasets,

NIPS Conference, Vancouver, B.C., Canada, December 11, 2009 (A. Gray ..)

• NVAC Consortium Meeting, Richland Washington, November 2008, August 2009

Distinguished Lecture Series• Lecture series featuring

leaders in the DAVA community

• Develop in collaboration with FODAVA partners and NVAC

• Live Broadcast via web

• Alexey Chervonenkis, "Model Complexity Optimization,” Jan. 16, 2009.• Vladimir Vapnik, “Learning with Teacher: Learning Using Hidden

Information,” Jan.16, 2009.• Joseph Kielman, “Visual Analytics - Past, Present, and Future,” Feb.

27, 2009.• William S. Cleveland, “The Disappearing Second Derivative of

Quadratics: Perceptual, Mathematical, and Statistical Properties of Judging Dependence on Visual Displays,” March 27, 2009.

• Alan Turner, “Mathematical Foundations as a Key Enabler of Agile Human Performance in Visual Analytics Environments,” April 24, 2009.

FODAVA DLS is being planned for Spring 2010.

FODAVA Website http://fodava.gatech.edu

• DAVA community events and meeting information • Dissemination of FODAVA results to user communities :

FODAVA Tech Report • Repository of data sets for FODAVA community• FODAVA meeting/lecture materials available

Collaborative Research : Test Bed for Visual Analytics of

High Dimensional Massive Data• Open source software with several modules

• Integrates results from mathematics, statistics, computational algorithms : FODAVA teams • Easily accessible to a wide community of researchers

• Makes theory/algorithms relevant and readily available to VA community• Identify effective methods for specific problems (evaluation)

FODAVAFundamentalResearch

ApplicationsApplications

Test BedTest Bed

We, the FODAVA community, is to play a key role in developing and defining the foundations for Data and Visual Analytics.

Communication and Collaboration with other elements of Data and Visual Analytics (e.g., NVAC, DHS/S&T CoE) will be essential.

Breakout Group Discussion: How FODAVA teams can best collaborate and advance FODAVA

Data & Visual Analytics (DAVA)Analytical

ReasoningI see, therefore, I reason betterI see, therefore, I reason better

Data Representation

and Transformation

Visual Representation and Interaction

Production, Presentation,

Dissemination

FoundationsFoundations

FODAVA is to create and advance the mathematical and computational foundations for the DAVA Discipline

Old slides follow.

FODAVA-Lead Challenges

Research and Collaboration• Creation of the Mathematical and Computational

Sciences Foundations required to represent and transform all types of digital data in ways to enable efficient and effective Visualization and Analytic Reasoning

• Intrinsic Challenges: Data sets massive, heterogeneous, multi-dimensional, dirty, incomplete, time-varying; solutions must be produced with time and space constraints, ….

• Understanding Fundamental issues/needs in VA and Communicating results– Isolated theoretical research is not enough– Problem driven foundational research is needed

FODAVA-Lead Challenges (cont’d)

• Education and Research– Defining Foundations of Data and Visual Analytics – Undergraduate and Graduate Curriculum (core

body of knowledge) for Data and Visual Analytics

• Community Building/Integration– A community of researchers who claim DAVA as

their own discipline and FODAVA an essential part

– Conferences, journals, books, professional society engagement,

– Industry, tech transfer, …

Project Materials• Goal: Articulate contributions being made by

the FODAVA community• Benefits

– Potential collaborators– Foster technology transition opportunities– Broader exposure to potential sponsors

• Materials requested– Project brochures and other collateral material– Videos especially welcome

• Tell us what you’re doing!• POC: Richard Fujimoto

Data and Visual Analytics (DAVA)

Analytical

Reasoning

Data Representation

and Transformation

Visual Representation and Interaction Production, Presentation,

Dissemination

FoundationsFoundations

Data and Visual Analytics (DAVA)Analytical Reasoning• Apply human judgment to

reach conclusions• Methods to maximally utilize

human capacity to derive deep understanding and insight into complex situations in a minimum amount of time

Data Representation and Transformation• Representing dynamic, incomplete, conflicting data

to convey important content in a form and level of abstraction appropriate to the analytical task to enable understanding

• Transforming data among possible representations to support analysis and discovery

Visual Representation and Interaction

• Visual presentation of information in ways that instantly convey important content taking advantage of human vision

• Interaction techniques (e.g., search) between the analyst and data to facilitate the analytical reasoning process

Production, Presentation, Dissemination• Seamless integration of data acquisition,

analysis, decision making, and action

FODAVA-Lead Senior Personnel

James Foley Interim Dean CoC

Graphics and Visualization, HCIVisual Analytics Digital Library

Richard FujimotoAssociate Director

CSE, ChairModeling and Simulation Education and Outreach

Guy LebanonCSE

Machine LearningComputational Statistics

Arkadi NemirovskiISyE

OptimizationNon-parametric Stat.

Alexander ShapiroISyE

Stochastic ProgrammingOptimization

Multivariate Stat. Analysis

Santosh VempalaCS

Theory of ComputigDirector of ARC

Hongyuan ZhaCSE

Numerical ComputingData Analysis

Director of Graduate Studies

Hao-Min ZhouMathematics

Wavelet and PDEImage Processing