DIMACS/DyDAn Workshop on Streaming, Coding, and Compressive Sensing: Unifying Theory and Common Applications to Sparse Signal/Data Analysis and Processing

March 27, 2009
DIMACS/DyDAn Center, CoRE Building, Rutgers University

Organizers:
Aiyou Chen, Bell Labs, aychen at research.bell-labs.com
Graham Cormode, AT&T Labs, grahamr at research.att.com
Andrew McGregor, UCSD, amcgregor.web at gmail.com
Olgica Milenkovic, UIUC, milenkov at uiuc.edu.
S. Muthukrishnan, Rutgers University, muthu at cs.rutgers.edu
Fred Roberts, Rutgers University, froberts at dimacs.rutgers.edu
Emina Soljanin, Bell Labs, emina at bell-labs.com
Presented under the auspices of the Special Focus on Hardness of Approximation and the Center for Dynamic Data Analysis (DyDAn).

Workshop Program:

Friday, March 27, 2009

 8:00 -  8:50  Breakfast and Registration 

 8:50  - 9:00  Welcome and Opening Remarks
               Fred Roberts, DIMACS Director

 9:00 -  9:25  Sketching and Streaming Entropy via Approximation Theory
               Jelani Nelson, MIT

 9:25 -  9:50  Lower Bounds for Gap-Hamming-Distance and Consequences for Data Stream Algorithms
               Amit Chakrabarti, Dartmouth

 9:50 - 10:15  Approximating Edit Distance in Near-Linear Time
               Alexandr Andoni, MIT

10:15 - 10:45  Break

10:45 - 11:10  Exact Low-rank Matrix Completion via Convex Optimization
               Ben Recht, Caltech

11:10 - 11:35  The Price of Privacy and the limits of LP decoding
               Kunal Talwar, Microsoft Research

11:35 - 12:00  A Fast and Compact Method for Unveiling Significant Patterns in High Speed Networks
               Jin Cao, Bell Labs

12:00 -  1:30  Lunch

 1:30 -  1:55  Sparsity Recovery: Limits, Algorithms, and Wireless Applications
               Alyson Fletcher, UC Berkeley

 1:55 -  2:20  Statistical recovery in high dimensions: Practical and information- theoretic limitations
               Martin Wainwright, UC Berkeley

 2:20 -  2:45  QUAPO : Quantitative Analysis of Pooling in High-Throughput Drug Screening
               Raghu Kainkaryam, University of Michigan 

 2:45 -  3:15  Break

 3:15 -  3:40  Sketches and Conditional Random Sampling (CRS) for Large Sparse Data Sets
               Ken Church, Microsoft Research

 3:40 -  4:05  Weighted Superimposed Codes and Constrained Integer Compressed Sensing
               Wei Dai, University of Illinois, Urbana Champaign 

 4:05 -  4:30  Coresets and Sketches for High Dimensional Subspace Approximation Problems
               Christian Sohler, Institut fur Informatik der Universitat Bonn 

 4:30          Final Remarks
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Document last modified on March 9, 2009.