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
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
Renato Monteiro
ISyE
Continuous Optimization
Statistical Computing
John Stasko
Associate Director
IC, Associate Chair
Information Vis.
Collaboration with NVAC and DHS/CoE
Liaison with Vis. community
FODAVA-Lead Senior Personnel
James Foley
Interim Dean CoC
Graphics and Visualization, HCI
Visual Analytics Digital Library
Alexander Shapiro
ISyE
Stochastic Programming
Optimization
Multivariate Stat. Analysis
Richard Fujimoto
Associate Director
CSE, Chair
Modeling and Simulation
Education and Outreach
Santosh Vempala
CS
Theory of Computig
Director of ARC
Guy Lebanon
Arkadi Nemirovski
CSE
ISyE
Machine Learning
Optimization
Computational Statistics Non-parametric Stat.
Hongyuan Zha
CSE
Numerical Computing
Data Analysis
Director of Graduate Studies
Hao-Min Zhou
Mathematics
Wavelet and PDE
Image 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
Univ. Michigan
Stanford
UC-Davis
UC-Santa Cruz
Cornell
Michigan State
∂
Penn State
Northwestern ∂ ∂ ∂
UI-Chicago ∂ CMU ∂ ∂ ∂ Princeton
∂ Univ. Maryland
UIUC Purdue
∂
Virginia Tech ∂
Georgetown
∂
Duke
Georgia Tech
(FODAVA lead)
∂
FODAVA ‘08 Partners: Welcome Back!
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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 HighDimensional Geometric Spaces
Leland Wilkinson , Robert Grossman (University of Illinois Chicago)
Adilson Motter (Northwestern University)
Welcome New FODAVA Partners!
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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)
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, …
Body of Knowledge: Workshop
December 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 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
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)
FODAVA
Fundamental
Research
Applications
Test 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
Reasoning
I see, therefore,
I reason better
Data Representation
and Transformation
Foundations
Visual Representation and
Interaction
Production, Presentation,
Dissemination
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)
Data Representation
and Transformation
Analytical
Reasoning
Foundations
Visual Representation
and Interaction
Production, Presentation,
Dissemination
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, HCI
Visual Analytics Digital Library
Alexander Shapiro
ISyE
Stochastic Programming
Optimization
Multivariate Stat. Analysis
Richard Fujimoto
Associate Director
CSE, Chair
Modeling and Simulation
Education and Outreach
Santosh Vempala
CS
Theory of Computig
Director of ARC
Guy Lebanon
Arkadi Nemirovski
CSE
ISyE
Machine Learning
Optimization
Computational Statistics Non-parametric Stat.
Hongyuan Zha
CSE
Numerical Computing
Data Analysis
Director of Graduate Studies
Hao-Min Zhou
Mathematics
Wavelet and PDE
Image Processing