Speaker: Dr. Deanna Needell

Abstract:
In this talk we will discuss results for robust signal reconstruction from random observations via synthesis and analysis methods.  Synthesis methods attempt to identify the low-dimensional representation of the signal directly, whereas analysis type methods reconstruct in signal space. We also discuss special cases including provable near-optimal reconstruction guarantees for total-variation minimization and new techniques in super-resolution.

Bio:
Deanna Needell is an assistant professor in the Mathematical Sciences Department at Claremont McKenna College.  Prior to this, she spent 2009-2011 as a postdoctoral fellow in the Mathematics and Statistics Departments at Stanford University.  She recieved her B.S. in mathematics and computer science from the University of Nevada and her M.A. and Ph.D. in mathematics from the University of California, Davis.  Her research interests include compressed sensing, randomized algorithms, functional analysis, computational mathematics, probability, and statistics.  Prof. Needell has been the recipient of awards including the Simons Foundation Collaboration Grant, 2012 IEEE Signal Processing Society Young Author Best Paper Award, and ScienceWatch Fast-Breaking Paper Award.