Published: Nov. 2, 2007
Event Description:

Mike Wakin, Assistant Professor, University Michigan

The Geometry of Compressed Sensing

Compressed Sensing (CS) is a rapidly emerging field based on the revelation that signals obeying sparse models can be recovered from small numbers of nonadaptive (even random) linear measurements. In this talk I will survey some of the theoretical foundations of CS, highlighting the important role that geometry has played in the development of the core CS theory and exploring new directions that have been inspired by this perspective. I will demonstrate how visual, geometric arguments can lead to a clear, intuitive understanding of the reasons why sparse signals can be recovered from random projections, and I will emphasize connections between a broad variety of geometric themes, including convex optimization, random projections of polytopes, Uniform Uncertainty Principles, n-widths, the Johnson-Lindenstruass lemme, and Whitney's embedding theorem for manifolds.

Location Information:
Main Campus - Engineering Classroom Wing  (View Map)
1111 Engineering DR 
Boulder, CO 
Room: 265
Contact Information:
Name: Ian Cunningham
Phone: 303-492-4668