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FODAVA-LEAD Updates

FODAVA-LEAD Updates. Haesun Park Computational Science and Engineering Division Georgia Institute of Technology FODAVA Annual Meeting, Dec. 3, 2009. FODAVA-Lead PIs at GAtech. Vladimir Koltchinskii Mathematics Machine Learning Theory Computational Statistics. Alex Gray

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FODAVA-LEAD Updates

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

  2. FODAVA-Lead PIs at GAtech Vladimir Koltchinskii Mathematics Machine Learning Theory Computational Statistics Alex Gray Associate Director CSE Machine Learning Fast Algorithms for Massive DA Industry Relations Haesun Park Director CSE, Associate Chair Numerical Computing Data Analysis Research, FODAVA Community Building John Stasko Associate Director IC, Associate Chair Information Vis. Collaboration with NVAC and DHS/CoE Liaison with Vis. community Renato Monteiro ISyE Continuous Optimization Statistical Computing

  3. FODAVA-Lead Senior Personnel James Foley Interim Dean CoC Graphics and Visualization, HCI Visual Analytics Digital Library Richard Fujimoto Associate Director CSE, Chair Modeling and Simulation Education and Outreach Guy Lebanon CSE Machine Learning Computational Statistics Arkadi Nemirovski ISyE Optimization Non-parametric Stat. Santosh Vempala CS Theory of Computig Director of ARC Hongyuan Zha CSE Numerical Computing Data Analysis Director of Graduate Studies Alexander Shapiro ISyE Stochastic Programming Optimization Multivariate Stat. Analysis Hao-Min Zhou Mathematics Wavelet and PDE Image Processing

  4. 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

  5. FODAVA Teams Univ. Michigan Cornell Stanford Michigan State Penn State ∂ Northwestern ∂ ∂ UI-Chicago Princeton ∂ CMU UC-Davis ∂ ∂ ∂ ∂ UIUC Univ. Maryland ∂ Purdue ∂ UC-Santa Cruz Virginia Tech Georgetown ∂ Duke ∂ Georgia Tech (FODAVA lead) ∂

  6. 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)

  7. 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)

  8. Toward a Discipline: Data & Visual Analytics • Body of Knowledge • Foundations, subareas, applications • Curriculum • Education programs • Community Building • Researchers • Educators • Practitioners 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, …

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

  10. 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

  11. 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)

  12. DAVA Community Development Outreach 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

  13. 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.

  14. 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

  15. 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) Test Bed Applications FODAVA Fundamental Research

  16. 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

  17. I see, therefore, I reason better Data & Visual Analytics (DAVA) Data Representation and Transformation Analytical Reasoning Foundations Visual Representation and Interaction Production, Presentation, Dissemination FODAVA is to create and advance the mathematical and computational foundations for the DAVA Discipline

  18. Old slides follow.

  19. 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

  20. 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, …

  21. 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

  22. Data and Visual Analytics (DAVA) Data Representation and Transformation Analytical Reasoning Foundations Visual Representation and Interaction Production, Presentation, Dissemination

  23. Data and Visual Analytics (DAVA) • 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 • 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 • 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

  24. FODAVA-Lead Senior Personnel James Foley Interim Dean CoC Graphics and Visualization, HCI Visual Analytics Digital Library Richard Fujimoto Associate Director CSE, Chair Modeling and Simulation Education and Outreach Guy Lebanon CSE Machine Learning Computational Statistics Arkadi Nemirovski ISyE Optimization Non-parametric Stat. Santosh Vempala CS Theory of Computig Director of ARC Hongyuan Zha CSE Numerical Computing Data Analysis Director of Graduate Studies Alexander Shapiro ISyE Stochastic Programming Optimization Multivariate Stat. Analysis Hao-Min Zhou Mathematics Wavelet and PDE Image Processing

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