Welcome and Schedule
Haesun Park
Computational 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.
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 HighDimensional 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
a
.
b
.
c
.
d
.
Large-scale Graph and
Network Data
Managing Scale: Massive Data Volume,
High Dimensionality, Integration of
Heterogeneous Data
Large-scale Image, Audio,
Spatial, and Temporal Data
Interaction and Visual
Reasoning Approaches
Clique tree growth as function of moral edges
Clique tree size, root nodes
1.E+09
1.E+08
1.E+07
1.E+06
y = 74.062e0.0474x
1.E+05
Sample means
Gompertz
Logistic
Complementary
Expon. (Sample means)
1.E+04
1.E+03
1.E+02
1.E+01
0
50
100
150
200
250
Expected number of moral edges
300
350
Large-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
Foundations of Comparative
Analytics for Uncertainty in Graphs
Principles for Scalable Dynamic Visual
Analytics
Lise Getoor (University of Maryland), Lisa Sing
(Georgetown University), Alex Pang (Univ. of California –
Santa Cruz)
H. Jagadish, George Michailidis (University of
Michigan)
•
•
•
Uncertainty
Large scale graph
Network analysis
•
•
Dynamic data
Large scale graph/network
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
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, …
Visually-Motivated
Characterizations of Point Sets
Embedded in High-Dimensional
Geometric Spaces
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
Leland Wilkinson, Robert
Grossman(University of Illinois Chicago),
Adilson Motter (Northwestern University)
•
•
•
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
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)
•
•
•
•
•
Efficient Data Reduction and
Summarization
Multi-Source Visual Analytics
Ping Li (Cornell University)
Jieping Ye, Anshuman Razdan, Peter
Wonka (Arizona State University)
•
•
•
•
Dimension reduction
Clustering
Multiple kernel learning
Fusion of heterogeneous data
Dimension reduction
Data reduction
Information fusion
Manifold learning
Application to text and image data
analysis
•
•
Data reduction
Summarization
Large-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
Differential Geometry Approach for Virus
Surface Formation, Evolution and
Visualization
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
Guowei Wei, Yiying Tong, Yang Wang (Michigan State
University)
•
•
•
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
Viral epidemics and pandemics
Multiscale framework for massive scale
problems
Biology Applications
Global Structure Discovery on Sampled
Spaces
Leonidas Guibas , Gunnar Carlsson (Stanford University)
•
•
Topology and geometry for structure
discovery
Image study
Interaction 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
Visualization of Analytic Processes
Formal Models, Algorithms, and
Visualizations for Storytelling
Ole Mengshoel, Marija D Ilic, Edwin Selker (Carnegie Mellon
University)
•
•
•
Interaction methods
Graph model
Bayesian networks
Naren Ramakrishnan, Christopher L North, Francis
Quek (Virginia Tech)
•
Modeling of interaction for story telling
Uncertainty-Aware Data
Transformations for Collaborative
Reasoning
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
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
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
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, 2010
10:00-11:10 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 UrbanaChampaign)
•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 Restaurant
Directions 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)