Event ended Lecture
Compressive Sensing

About this event

CISE Distinguished Lecture

Thursday, March 6th, 2008 at 2:00pm, Rm. 375


Compressive Sensing


Dr. Emmanuel Candes

California Institute of Technology





Current data acquisition protocols are often extremely wasteful. Indeed, consider that many protocols acquire massive amounts of data which are then - in large part - discarded by a subsequent compression stage, which is usually necessary for storage and transmission purposes. Why then spend so much time and/or money to acquire all these data when we know that most of it can be thrown away anyway?

This talk surveys a novel sampling or sensing theory now known as "Compressed Sensing" or "Compressive Sampling" which allows the faithful recovery of signals and images from far fewer measurements or data bits than traditional methods use - hence offering an alternative to wasteful protocols by suggesting procedures for sensing and compressing data simultaneously and much faster.  We present the key ideas underlying this new paradigm and emphasize the practicality and the broad applicability of this technique. It is believed that compressive sensing will help address enormous challenges in science and engineering, and we will discuss how this is shaping a new generation of sensing devices. We will review recent progress in biomedical imaging, analog-to-digital conversion and other areas as well.





Emmanuel Candes received his B. Sc. degree from the Ecole Polytechnique (France) in 1993, and the Ph.D. degree in statistics from Stanford University in 1998. He is the Ronald and Maxine Linde Professor of Applied and Computational Mathematics at the California Institute of Technology. Prior to joining Caltech, he was an Assistant Professor of Statistics at Stanford University, 1998--2000.  His research interests are in computational harmonic analysis, multiscale analysis, approximation theory, statistical estimation and detection with applications to the imaging sciences, signal processing, scientific computing, inverse problems. Other topics of interest include theoretical computer science, mathematical optimization, and information theory.

Dr. Candes received the Third Popov Prize in Approximation Theory in 2001, and the DOE Young Investigator Award in 2002. He was selected as an Alfred P. Sloan Research Fellow in 2001. He co-authored a paper that won the Best Paper Award of the European Association for Signal, Speech and Image Processing (EURASIP) in 2003.  He was selected as the main lecturer at the NSF-sponsored 29th Annual Spring Lecture Series in the Mathematical Sciences in 2004 and as the Aziz Lecturer in 2007.

He has also given plenary and keynote addresses at major international conferences including ICIAM 2007 and ICIP 2007.  In 2005, he was awarded the James H. Wilkinson Prize in Numerical Analysis and Scientific Computing by SIAM. Finally, he is the recipient of the 2006 Alan T. Waterman Medal awarded by the US National Science Foundation.

* If you would like to arrange a meeting with Dr. Candes, please contact Dawn Patterson (ext. 7097).