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Title: Compressed Sensing: Past, Present, Future
Originating Office: OUDPA
Speaker: Donoho, David L
Issue Date: 24-Sep-2013
Event Date: 24-Sep-2013
Group/Series/Folder: Record Group 3.8 - Office of University Development and Public Affairs
Series 6 - Audio-visual Materials
Location: 3.8:6 EF
Notes: Shaw Prize Lecture in Mathematical Sciences.
Title from opening screen.
Abstract: From 2004 to today, the research topic 'Compressed Sensing' (CS) became popular in applied mathematics, signal processing, and information theory, and was applied to fields as distant as computational biology and astronomical image processing. Some early papers have gained thousands of citations. Part of the attraction is paradox: CS claims to correctly and efficiently solve systems of equations with *fewer* equations than unknowns -- when the vector of unknowns is sparse. Another attraction is intellectual: some beautiful mathematics explains how CS can be possible, combining combinatorics of sparse vectors and geometry of random point clouds. Still another attraction is practical: the mathematics has already been used in concrete ways for human benefit. For example, in pediatric magnetic resonance imaging, blind trials at Stanford and UCSF, published in a flagship medical journal by Vasanawala, Lustig et al. proved a 6X MRI speedup while maintaining diagnostic quality images. Concretely, children needed to sit still in an MRI machine for about 1 minute rather than 8 minutes. The prehistory of CS goes back on a metaphoric level to coin-balance weighing puzzles known for millennia and more specifically to convex geometry known for a hundred years, and continues throughout the last century in several very different fields of research. Experimental evidence for the possibility of CS accumulated over the last thirty years in oil exploration, medical imaging, physical chemistry, and astronomy. Part of the spectacular recent interest, is that several theoretical approaches, from information theory to high-dimensional geometry, came across powerful, interesting and very different ways to rigorously derive and analyze efficient CS algorithms. This talk will review success stories, precursors, and four modern ways of understanding the problem, from four different disciplines, and also will point to developing trends -- From HKUST Web page.
David L Donoho was born in 1957 in Los Angeles, USA and is currently Anne T and Robert M Bass Professor of the Humanities and Sciences, and Professor of Statistics at Stanford University, USA. He graduated from Princeton University in 1978 and received his PhD from Harvard University in 1983. From 1984 to 1990, he was on the faculty of the University of California, Berkeley before moving to Stanford. He is a fellow of the American Academy of Arts and Sciences, a SIAM Fellow, a foreign associate of the French Academy of Sciences, and a member of the US National Academy of Sciences.
Duration: 108 min.
Appears in Series:8.15:3 - Audio-visual Materials
Videos for Public -- Distinguished Lectures