IAM-PIMS Distinguished Colloquium: Andrea Montanari
Topic
Iterative Methods in Signal Processing
Speakers
Details
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.
Additional Information
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.
URL for Speaker: http://www.stanford.edu/~montanar/
Andrea Montanari, Stanford University
URL for Speaker: http://www.stanford.edu/~montanar/
Andrea Montanari, Stanford University