Compressive Sensing

Dr. Emmanuel Candes

Wednesday, March 12, 2007

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. This talk introduces a novel sampling or sensing theory which goes against this conventional wisdom. This theory now known as Compressed Sensing or Compressive Sampling’’ allows the faithful recovery of signals and images from what appear to be highly incomplete sets of data, i.e. from far fewer measurements or data bits than traditional methods use. We will present the key ideas underlying this new sampling or sensing theory, and will survey some of the most important results. We will emphasize the practicality and the broad applicability of this technique, and discuss what we believe are far reaching implications; e.g. procedures for sensing and compressing data simultaneously and much faster. Finally, there are already many ongoing efforts to build a new generation of sensing devices based on compressed sensing and we will discuss remarkable recent progress in this area 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.