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

Presentation slides

• Alexandr Andoni, MIT
  Approximating Edit Distance in Near-Linear Time

• Choongsoon Bae, Google Inc., U.C.Berkeley
  Versions of Random Forests: Properties and Performances

• Robert Calderbank, Princeton University
  Fast Reconstruction Algorithms for Deterministic Sensing Matrices and Applications

• Amit Chakrabarti, Dartmouth College
  Lower Bounds for Gap-Hamming-Distance and Consequences for Data Stream Algorithms

• Kenneth Church, Microsoft
  Using Sketches to Estimate Associations

• Graham Cormode, AT & T
  Finding Frequent Items in Data Streams

• Wei Dai, University of Illinois at Urbana-Champaign
  Weighted Superimposed Codes And Constrained Compressed Sensing

• Piotr Indyk, MIT
  Sparse Recovery Using Sparse (Random) Matrices

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

• Kunal Talwar, MSR SVC
  The Price of Privacy and the Limits of LP decoding

• Ping Li, Cornell University
  Compressed Counting

• George Michailidis, The University of Michigan
  Identifiability in Network Tomography under Dependence

• Andrea Montanari, Stanford University
  Matrix Completion from Fewer Entries

• Jelani Nelson, MIT
  Sketching and Streaming Entropy via Approximation Theory

  Sketching and Streaming Entropy via Approximation Theory-Part II

• Ben Recht, Caltech
  Low-rank Matrix Completion via Convex Optimization

• Martin J. Strauss, University of Michigan
  Euclidean Sparse Recovery with Optimal Measurement (in progress)

• Joel A. Tropp, California Institute of Technology
  CoSaMP Iterative signal recovery from incomplete and inaccurate samples

• Martin Wainwright, UC Berkeley
  High-dimensional graphical model selection: Practical and information-theoretic limits

Workshop Index

DIMACS Homepage