New Directions
Program
New Directions Short Course:
Compressive Sampling and Frontiers in Signal
Processing
June
4 - 15, 2007
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 |
|