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[Colloquium] Information extraction from few, corrupted data

April 7, 2011

Watch Colloquium: 

M4V file (653 MB)

  • Date: Thursday, April 7, 2011 
  • Time: 11:00 am — 11:50 am 
  • Place: Mechanical Engineering 218

Rick Chartrand
Los Alamos National Laboratory

In this talk, we’ll examine some of the surprising capabilities of simple optimization problems to extract meaningful information from seemingly inadequate data. The starting point for this work is the applied mathematics field known as compressive sensing, which has shown impressive abilities to recover images and other signals from very few measurements. We’ll look at some recent generalizations of this work, with applications to MRI reconstruction and the extraction of features from video.

Bio: Rick Chartrand received a Ph.D. in pure mathematics at UC Berkeley, and now works in applied mathematics at Los Alamos National Laboratory. His research interests include compressive sensing, nonconvex optimization, feature extraction from high-dimensional data, and image regularization.