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New Directions Program

New Directions Short Course:

Compressive Sampling and Frontiers in Signal Processing

June 4 - 15, 2007

Instructors:
Emmanuel J. Candès Applied and Computational Mathematics, California Institute of Technology
Ronald A. DeVore Mathematics, University of South Carolina

with additional lectures by:

Richard G. Baraniuk Electrical and Computer Engineering, Rice University
Anna C. Gilbert Mathematics, University of Michigan

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Abstracts and Talk Materials Dining Guide


From June 4-15, 2007 the IMA will host an intensive short course designed to efficiently provide researchers in the mathematical sciences and related disciplines the basic knowledge prerequisite to undertake research in signal processing and compressive sampling. The course will be taught by Emmanuel J. Candes, Professor of Applied and Computational Mathematics at California Institute of Technology, Ronald A. DeVore, Professor of Mathematics at University of South Carolina, and Richard G. Baraniuk, Professor of Electrical and Computer Engineering at Rice University. The primary audience for the course is mathematics faculty. No prior background signal processing is expected. Participants will receive full travel and lodging support during the workshop.

Description:

One of the central tenets of signal processing and data acquisition is the Shannon/Nyquist sampling theory: the number of samples needed to capture a signal is dictated by its bandwidth. Very recently, an alternative sampling or sensing theory has emerged which goes against this conventional wisdom. This theory now known as "Compressive Sampling" or "Compressed Sensing" allows the faithful recovery of signals and images from what appear to be highly incomplete sets of data, i.e., from far fewer data bits than traditional methods use. Underlying this metholdology is a concrete protocol for sensing and compressing data simultaneously. Following this protocol would bypass the current wasteful acquisition process in which massive amounts of data are collected only to be—in large part—discarded at the compression stage, which is necessary for storage and transmission purposes. In the compressed sensing paradigm, one could translate analog data into already compressed digital form, obtaining super-resolved signals from just a few sensors.

The last two years have seen an explosion of research activity in the area of compressive sampling and our lectures will present the key mathematical ideas underlying this new sampling or sensing theory, which come from various subdisciplines within the mathematical sciences; namely, probability theory and especially random matrix theory, mathematical optimization, and analysis in high-dimensional Banach spaces. In addition, a beautiful thing about compressed sensing is that is has deep connections with many disciplines; with signal processing of course, but also with information theory, coding theory, theoretical computer science and statistics to name just a few. A good portion of these lectures will make these connections clear. Compressed sensing also offers a new vantage point for a diverse set of applications including accelerated tomographic imaging, analog-to-digital conversion, and digital photography. Interestingly, there are already many ongoing efforts to build a new generation of sensing devices based on compressed sensing and the lecturers will address remarkable recent progress in this area as well. Finally, while we will survey foundational results in compressive sampling, it is good to keep in mind that this is after all a very young field, which has a flurry of open problems. We hope to expose the participants to the most exciting ones.

Application procedure. The IMA New Directions Short Courses will be limited to 25 participants selected by application. All successful applicants will be funded for travel and local expenses. Please see the IMA reimbursement policy for details about airfare.

Schedule

Week 1: Monday | Tuesday | Wednesday | Thursday | Friday | Saturday | Sunday | 
Week 2: Monday | Tuesday | Wednesday | Thursday | Friday | 
  Monday, June 4
8:30a-8:50a Coffee   Lind Hall 400
8:50a-9:00a Welcome and introduction Douglas N. Arnold (University of Minnesota) Lind Hall 409
9:00a-10:30a Sparsity Emmanuel J. Candès (California Institute of Technology) Lind Hall 409
10:30a-11:00a Break   Lind Hall 400
11:00a-12:30p Signal encoding Ronald DeVore (University of South Carolina) Lind Hall 409
12:30p-2:00a Lunch    
2:00p-3:30p Short presentations by participants   Lind Hall 409
3:45p-4:00p Group Photo    
4:00p-5:00p Reception   Lind Hall 400
  Tuesday, June 5
8:45a-9:00a Coffee   Lind Hall 400
9:00a-10:30a Sparsity and the l1 norm Emmanuel J. Candès (California Institute of Technology) Lind Hall 409
10:30a-11:00a Break   Lind Hall 400
11:00a-12:30p Compression Ronald DeVore (University of South Carolina) Lind Hall 409
12:30p-2:00p Lunch    
2:00p-3:00p Introduction to MRI Leon Axel (New York University), Steen Moeller (University of Minnesota) Lind Hall 409
3:00p-4:30p Discussion   Lind Hall 409
  Wednesday, June 6
8:45a-9:00a Coffee   Lind Hall 400
9:00a-10:30a Compressive sampling: sparsity and incoherence Emmanuel J. Candès (California Institute of Technology) Lind Hall 409
10:30a-11:00a Break   Lind Hall 400
11:00a-12:30p Discrete compressed sensing Ronald DeVore (University of South Carolina) Lind Hall 409
12:30p-2:00p Lunch    
2:00p-3:00p Short presentations by participants   Lind Hall 409
3:00p-3:30p Discussion   Lind Hall 409
6:30p-8:30p Group dinner at Kikugawa   Kikugawa, 43 Main Street SE, Minneapolis, MN 55414 
  Thursday, June 7
8:45a-9:00a Coffee   Lind Hall 400
9:00a-10:30a The uniform uncertainty principle Emmanuel J. Candès (California Institute of Technology) Lind Hall 409
10:30a-11:00a Break   Lind Hall 400
11:00a-12:30p The restricted isometry property (RIP) Ronald DeVore (University of South Carolina) Lind Hall 409
12:30p-2:00p Lunch    
2:00p-3:00p Algorithms for Compressed Sensing, I Anna Gilbert (University of Michigan) Lind Hall 409
3:00p-3:30p Discussion   Lind Hall 409
  Friday, June 8
8:45a-9:00a Coffee   Lind Hall 400
9:00a-10:30a The role of probability in compressive sampling Emmanuel J. Candès (California Institute of Technology) Lind Hall 409
10:30a-11:00a Break   Lind Hall 400
11:00a-12:30p Construction of CS matrices with best RIP Ronald DeVore (University of South Carolina) Lind Hall 409
12:30p-2:00p Lunch    
2:00p-3:00p Algorithms for Compressed Sensing, II Anna Gilbert (University of Michigan) Lind Hall 409
3:00p-3:30p Discussion   Lind Hall 409
  Saturday, June 9
No lecture scheduled.
  Sunday, June 10
No lecture scheduled.
  Monday, June 11
8:45a-9:00a Coffee   Lind Hall 400
9:00a-10:30a Robust compressive sampling and connections with statistics Emmanuel J. Candès (California Institute of Technology) Lind Hall 409
10:30a-11:00a Break   Lind Hall 400
11:00a-12:30p Performance of CS matrices revisited Ronald DeVore (University of South Carolina) Lind Hall 409
12:30p-2:00p Lunch    
2:00p-3:00p An introduction to transform coding Richard Baraniuk (Rice University) Lind Hall 409
3:00p-3:30p Discussion/break   Lind Hall 409
3:30p-4:30p Compressive sensing for time signals: Analog to information conversion Richard Baraniuk (Rice University) Lind Hall 409
  Tuesday, June 12
8:45a-9:00a Coffee   Lind Hall 400
9:00a-10:30a Robust compressive sampling and connections with statistics (continued) Emmanuel J. Candès (California Institute of Technology) Lind Hall 409
10:30a-11:00a Break   Lind Hall 400
11:00a-12:30p Performance in probability Ronald DeVore (University of South Carolina) Lind Hall 409
12:30p-2:00p Lunch    
2:00p-3:00p Compressive sensing for detection and classification problems Richard Baraniuk (Rice University) Lind Hall 409
3:00p-3:30p Discussion/break   Lind Hall 409
3:30p-4:30p Multi-signal, distributed compressive sensing Richard Baraniuk (Rice University)  
  Wednesday, June 13
8:45a-9:00a Coffee   Lind Hall 400
9:00a-10:30a Connections with information and coding theory Emmanuel J. Candès (California Institute of Technology) Lind Hall 409
10:30a-11:00a Break   Lind Hall 400
11:00a-12:30p Compressive imaging with a single pixel camera Richard Baraniuk (Rice University) Lind Hall 409
12:30p-2:00p Lunch    
2:00p-3:00p Decoders Ronald DeVore (University of South Carolina) Lind Hall 409
3:00p-3:30p Discussion   Lind Hall 409
  Thursday, June 14
8:45a-9:00a Coffee   Lind Hall 400
9:00a-10:30a Modern convex optimization Emmanuel J. Candès (California Institute of Technology) Lind Hall 409
10:30a-11:00a Break   Lind Hall 400
11:00a-12:30p Performance of iterated least squares Ronald DeVore (University of South Carolina) Lind Hall 409
12:30p-2:00p Lunch    
2:00p-3:00p Short presentations by participants   Lind Hall 409
3:00p-3:30p Discussion   Lind Hall 409
  Friday, June 15
8:45a-9:00a Coffee   Lind Hall 400
9:00a-10:30a Applications, experiments and open problems Emmanuel J. Candès (California Institute of Technology) Lind Hall 409
10:30a-10:45a Break   Lind Hall 400
10:45a-11:45a Deterministic constructions of CS Matrices Ronald DeVore (University of South Carolina) Lind Hall 409

LIST OF CONFIRMED PARTICIPANTS

Name Department Affiliation
Douglas N. Arnold Institute for Mathematics and its Applications University of Minnesota
Leon Axel Medical Center New York University
Richard Baraniuk Department of Electrical and Computer Engineering Rice University
Emmanuel J. Candès Department of Applied and Computational Mathematics California Institute of Technology
Sohae Chung Department of Radiology New York University
Steven Benjamin Damelin Department of Mathematical Sciences Georgia Southern University
Ronald DeVore Industrial Mathematics Institute University of South Carolina
Dean M. Evasius Division of Mathematical Sciences National Science Foundation
Anna Gilbert Department of Mathematics University of Michigan
Hongbin Guo Department of Mathematics and Statistics Arizona State University
Keigo Hirakawa Department of Statistics Harvard University
Olga Holtz Department of Mathematics TU Berlin
Richard K. Jordan Department of Mathematics and Statistics Mount Holyoke College
In-Jae Kim Department of Mathematics and Statistics Minnesota State University
Ilya A Krishtal Department of Mathematics Northern Illinois University
Raoul LePage Department of Statistics and Probability Michigan State University
Hstau Y Liao   University of Minnesota
En-Bing Lin Department of Mathematics University of Toledo
Steen Moeller Department of Radiology University of Minnesota
Edmond Nadler    
Andrea R. Nahmod Department of Mathematics and Statistics University of Massachusetts
Carmeliza Navasca Equipe Traitement des Images et du Signal Laboratoire Centre National de la Recherche Scientifique (CNRS)
Guergana Petrova Department of Mathematics Texas A & M University
Rodrigo B. Platte Department of Mathematics and Statistics Arizona State University
Leming Qu Department of Mathematics Boise State University
Amos Ron Department of Computer Science and Mathematics University of Wisconsin
Hans Rullgård Department of Mathematics University of Stockholm
Chris Sansing   Department of Defense
Guillermo R. Sapiro Department of Electrical and Computer Engineering University of Minnesota
Arnd Scheel Institute for Mathematics and its Applications University of Minnesota
Chehrzad Shakiban Institute of Mathematics and its Application University of Minnesota
Xiaoping Annie Shen Department of Mathematics Ohio University
Rodolfo H. Torres Department of Mathematics University of Kansas
Jingbo Wang Corporate Strategic Research Exxon Research and Engineering Company
Yi Ming Zou Department of Mathematical Sciences University of Wisconsin