Iterative Methods in Signal Processing

IAM-PIMS Distinguished Colloquium
March 18, 2013 10:00 pm

Speaker:  Assoc. Prof. Andrea Montanari, Department of Electrical Engineering and Department of Statistics, Stanford University, California

URL for Speaker:

Location:  LSK 460

Intended Audience:  Public

The classical statistical estimation problem requires to reconstruct an unknown vector of parameters from a set of observations, when the two are connected through a stochastic relationship. Over the last ten years, a whole new generation of statistical estimation problems has emerged, posing fascinating new challenges to the existing theory. Prominent examples include the image reconstruction problems arising in magnetic resonance imaging (MRI), exploration seismology, radar imaging or hyperspectral imaging. While these problems are characterized by a sharp increase in the amount of available data and computational resources, both are outpaced by the increase in dimensionality of the unknown vector of parameters. I will describe a class of reconstruction algorithms that emphasize statistical efficiency under bounded computational resources. These algorithms are known as approximate message passing (AMP) algorithms and are inspired by ideas in graphical models. [Based on joint work with D. Donoho, I. Johnstone, A. Maleki]

Andrea Montanari received a Laurea degree in Physics in 1997 and a Ph. D. in Theoretical Physics in 2001 (both from Scuola Normale Superiore in Pisa, Italy). He has been post-doctoral fellow at Laboratoire de Physique Thorique de l’Ecole Normale Suprieure (LPTENS), Paris, France, and the Mathematical Sciences Research Institute, Berkeley, USA. Since 2002 he is Charg de Recherche (with Centre National de la Recherche Scientifique, CNRS) at LPTENS. In September 2006 he joined Stanford University as a faculty, and since 2010 he is Associate Professor in the Departments of Electrical Engineering and Statistics. He was co-awarded the ACM SIGMETRICS best paper award in 2008. He received the CNRS bronze medal for theoretical physics in 2006 and the National Science Foundation CAREER award in 2008.