Low-dimensionality in mathematical signal processing

Series: 
Job Candidate Talk
Thursday, January 16, 2014 - 11:05
1 hour (actually 50 minutes)
Location: 
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.