fodava-lead updates haesun park computational science and engineering division georgia institute of...
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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