Low-dimensionality in mathematical signal processing

Job Candidate Talk
Thursday, January 16, 2014 - 11:05
1 hour (actually 50 minutes)
Skiles 006
University of Michigan
Natural images tend to be compressible, i.e., the amount of information needed to encode an image is small. This conciseness of information -- in other words, low dimensionality of the signal -- is found throughout a plethora of applications ranging from MRI to quantum state tomography. It is natural to ask: can the number of measurements needed to determine a signal be comparable with the information content? We explore this question under modern models of low-dimensionality and measurement acquisition.