Stephen Becker will be presenting a TCP Seminar on February 5th. The seminar will give a brief introduction to compressed sensing, which is a theory developed in 2004 that shows how, in an appropriate setting, to be able to take many fewer measurements of a signal than the classical limit. It does this by exploiting compressibility in a particular way. An extension of compressed sensing is matrix completion, which fills in missing entries in a matrix by exploiting another type of compressibility. Applications of both theories were initially mostly for signal processing, but the applications are increasingly turning toward data processing and general machine learning.
- Date: Wednesday, February 5th
- Time: 4:30-5:20PM
- Place: ECCR 105 in the CU Engineering Center
About the Speaker
Stephen Becker is an assistant professor in the Applied Mathematics department at the University of Colorado Boulder. Previously, he was a Goldstine Postdoctoral fellow at IBM Research and a Fondation Scientifique et Mathematique de Paris postdoctoral fellow. He received his PhD in Applied Mathematics from Caltech under the supervision of Emmanuel Candes in 2011, and bachelors in math and physics from Wesleyan University. His research interests are in optimization, machine learning, signal processing, imaging, inverse problems in quantum information, PDE-constrained optimization, and randomized numerical linear algebra. His papers are available here.