SAMSI-FODAVA Workshop on Interactive Visualization and Analysis of Massive Data - December 10-12, 2012

Workshop Information

December 10, 2012 - 9:00am - December 12, 2012 - 1:30pm
Foundations on Data Analysis and Visual Analytics, National Science Foundation, Department of Homeland Security

Objectives of the Workshop:

With the advance in technology, enormous amounts of data are generated on a daily basis virtually in every area including bioinformatics, astrophysics, chemometrics, social network analysis, web mining, text mining, financial analysis, and security. We are faced with significant analytical challenges due to many special characteristics of these data sets, which are of large volume, often unstructured, high dimensional, noisy, incomplete, time-varying, spatial, and originate from different sources. These challenges can be turned into new opportunities and discoveries when the massive data can be transformed into useful knowledge. Recent developments in data and visual analytics show that incorporating interactive capability through visual interfaces with automated data analysis methods can substantially increase our ability to understand the data and find more meaningful solutions.

The primary goal of the workshop was to bring together researchers in Mathematics, Statistics, Computational Science and Engineering, Computer Science, and Visualization to work on massive scale data and visual analytics. Issues that were investigated include the mathematical, statistical, and algorithmic issues in efficient representation and transformation of data, scalable and dynamic algorithms for real time interaction, visual representation in limited screen space, performing evaluations, and applications.


Monday, December 10
Room 150

9:00-9:30 a.m. Registration and Continental Breakfast
9:30-9:40 Welcome Remarks
9:40-10:10 The Role of Visualization and Analytics in Solving Problems Based on Massive Data
Daniel Keim, University of Konstanz
10:10-10:40 Robust Subspace Modeling
Gilad Lerman, University of Minnesota
10:40-11:10 Break
11:10-11:40 On the Complexity of Statistical Algorithms
Santosh Vempala, Georgia Tech
11:40-1:40 Lunch
1:40-2:10 Interactive Graphics for Data Exploration
Heike Hofmann, Iowa State University
2:10-2:40 The Role of Perception in Visualization and Visual Analytics
Christopher Healey, North Carolina State University
2:40-3:10 Break
3:10-3:40 Visual Analytics for Evidence-Based Medicine
David Gotz, IBM
3:40-4:10 Student Poster Fast Forward
4:10-5:00 Break
5:00-7:00 Poster Session and Reception

SAMSI will provide poster presentation boards and tape. The board dimensions are 4 ft. wide by 3 ft. high. They are tri-fold with each side being 1 ft. wide and the center 2 ft. wide. Please make sure your poster fits the board. The boards can accommodate up to 16 pages of paper measuring 8.5 inches by 11 inches.

Tuesday, December 11
Room 150

9:00-9:30 a.m. Registration and Continental Breakfast
9:30-10:00 Large-Scale Visual Data Analysis
Chris Johnson, University of Utah
10:00-10:30 Computational Signal Processing in Smart Patient monitoring: Algorithms, Applications and Future Challenges
Sabine Van Huffel, K.U. Leuven, ESAT/SISTA
10:30-11:00 Break
11:00-11:30 Discovery of Mechanisms and Prognosis of Cancers from Matrix and Tensor Modeling of Large-Scale Molecular Biological Data
Orly Alter, University of Utah
11:30-Noon The Combinatorial Laplacian and Dimension Reduction
Sayan Mukherjee, Duke University
Noon-2:00 Lunch
2:00-2:30 TB-Vis: Visualizing TB Patient-Pathogen Relationships
Kristen Bennett, RPI
2:30-3:00 VisIRR: Visual Information Retrieval and Recommendation System for Document Discovery
Alexander Gray, Georgia Tech
3:00-3:30 Break
3:00-4:30 Panel
Chair: Jimeng Sun, IBM
Panelists: Polo Chau, Georgia Tech; Daniel Keim, University of Konstanz; Larry Rosenbaum, National Science Foundation; Leland Wilkinson, SYSTAT/Northwestern University
6:00 Dinner on your own in small groups at Southpoint mall
Radisson hotel van service will take us to Southpoint mall

Wednesday, December 12
Room 150

9:00-9:30 a.m. Registration and Continental Breakfast
9:30-10:00 New Approaches for Nonlinear Dimensionality Reduction
Fei Sha, University of Southern California
10:00-10:30 New Approaches to Storytelling from Massive Textual Datasets
Naren Ramakrishnan, Virginia Tech
10:30-11:00 Break
11:00-11:30 Scalable Bayesian Learning for Matrix and Tensors
Alan Qi, Purdue University
11:30-Noon BigData: Probabilistic Methods for Efficient Search and Statistical Learning in Extremely High-Dimensional Data
Ping Li, Cornell University
Noon-12:10 Concluding Remarks
12:10-1:30 Lunch