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.
Anna Gilbert (University of Michigan)
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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