Description
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.
Schedule and Supporting Media
Monday, December 10
at SAMSI, Room 150
Tuesday, December 11
at SAMSI, Room 150
| Time | Description | Speaker | Slides | Videos |
|---|---|---|---|---|
| 9:00-9:30 | 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 |
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| 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
at SAMSI, Room 150
| Time | Description | Speaker | Slides | Videos |
|---|---|---|---|---|
| 9:00-9:30 | 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 |