Prof. Emmanuel J. Candes, California Institute of Technology
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 or an image 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 used by traditional methods. We present the key ideas underlying this new sampling or sensing theory, and survey some of the most important results. We 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 image data simultaneously and much faster. Finally, we will discuss several ongoing efforts which are well under way to build a new generation of compressed sensing-based devices.
Emmanuel Candes received his BSc from Ecole Polytechnique, Paris, in 1993, and his PhD 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 from 1998 to 2000. His research interests are in computational harmonic analysis, multiscale analysis, statistical estimation and detection, with applications to the imaging science, signal processing, scientific computing, and inverse problems. His other research interests 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 addresses at major international conferences. In 2005, he was awarded the James H. Wilkinson Prize in Numerical Analysis and Scientific Computing by the Society for Industrial and Applied Mathematics (SIAM). He is the recipient of the 2006 Alan T. Waterman Medal awarded by the US National Science Foundation.