Sparsity: algorithms, approximations, and analysis
Anna Gilbert
2011 Invited Talk
Abstract
The last 15 years we have seen an explosion in the role of sparsity in mathematical signal and image processing, signal and image acquisition and reconstruction algorithms, and myriad applications. It is also central to machine learning. I will present an overview of the mathematical theory and several fundamental algorithmic results, including a fun application to solving Sudoku puzzles.
Speaker
Anna Gilbert
Anna Gilbert received an S.B. degree from the University of Chicago and a
Ph.D. from Princeton University, both in mathematics. In 1997, she was a
postdoctoral fellow at Yale University and AT&T Labs-Research. From 1998 to
2004, she was a member of technical staff at AT&T Labs-Research in Florham
Park, NJ. Since then she has been with the Department of Mathematics at the
University of Michigan, where she is now a Professor. She has received
several awards, including a Sloan Research Fellowship (2006), an NSF
CAREER award (2006), the National Academy of Sciences Award for
Initiatives in Research (2008), the Association of Computing Machinery
(ACM) Douglas Engelbart Best Paper award (2008), and the EURASIP
Signal Processing Best Paper award (2010).
Her research interests include analysis, probability, networking, and
algorithms. She is especially interested in randomized algorithms with
applications to harmonic analysis, signal and image processing,
networking, and massive datasets.
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