Compressed Sensing: Examples, Prehistory, and Predictions
Event details
| Date | 05.10.2012 |
| Hour | 12:15 › 13:15 |
| Speaker | Prof. David Donoho, Stanford University |
| Location | |
| Category | Conferences - Seminars |
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 solve systems of equations with {\it fewer} equations than unknowns. One success story for CS comes in pediatric magnetic resonance imaging, where blind trials published in a flagship medical journal by Vansanawala, Lustig et al. gave 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. Part of the spectacular recent interest, is that several fields, from information theory to high-dimensional geometry, are convinced that they saw the key ideas first, and that they know the best way to think about it. This talk will review success stories, precursors, and four modern ways of understanding the problem, from four different disciplines.
Biography
David Leigh Donoho, born on March 5, 1957 in Los Angeles, is a professor of statistics at Stanford University, where he is also the Anne T. and Robert M. Bass Professor in the Humanities and Sciences. His work includes the development of effective methods for the construction of low-dimensional representations for high-dimensional data problems (multiscale geometric analysis), developments of wavelets for denoising and compressed sensing.
Donoho did his undergraduate studies at Princeton University, graduating in 1978. His undergraduate thesis advisor was John W. Tukey. Donoho obtained his Ph.D. from Harvard University in 1983, under the supervision of Peter Jost Huber. He was on the faculty of the University of California, Berkeley from 1984 to 1990 before moving to Stanford.
In 1991, Donoho was named a MacArthur Fellow. He was elected a Fellow of the American Academy of Arts and Sciences in 1992. He was the winner of the COPSS Presidents' Award in 1994. In 2001, he won the John von Neumann Prize of the Society for Industrial and Applied Mathematics. In 2002, he was appointed to the Bass professorship. He was elected a SIAM Fellow and a foreign associate of the French Académie des sciences in 2009, and in the same year received an honorary doctorate from the University of Chicago. In 2010 he won the Norbert Wiener Prize in Applied Mathematics, given jointly by SIAM and the American Mathematical Society. He is also a member of the United States National Academy of Science.
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 solve systems of equations with {\it fewer} equations than unknowns. One success story for CS comes in pediatric magnetic resonance imaging, where blind trials published in a flagship medical journal by Vansanawala, Lustig et al. gave 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. Part of the spectacular recent interest, is that several fields, from information theory to high-dimensional geometry, are convinced that they saw the key ideas first, and that they know the best way to think about it. This talk will review success stories, precursors, and four modern ways of understanding the problem, from four different disciplines.
Biography
David Leigh Donoho, born on March 5, 1957 in Los Angeles, is a professor of statistics at Stanford University, where he is also the Anne T. and Robert M. Bass Professor in the Humanities and Sciences. His work includes the development of effective methods for the construction of low-dimensional representations for high-dimensional data problems (multiscale geometric analysis), developments of wavelets for denoising and compressed sensing.
Donoho did his undergraduate studies at Princeton University, graduating in 1978. His undergraduate thesis advisor was John W. Tukey. Donoho obtained his Ph.D. from Harvard University in 1983, under the supervision of Peter Jost Huber. He was on the faculty of the University of California, Berkeley from 1984 to 1990 before moving to Stanford.
In 1991, Donoho was named a MacArthur Fellow. He was elected a Fellow of the American Academy of Arts and Sciences in 1992. He was the winner of the COPSS Presidents' Award in 1994. In 2001, he won the John von Neumann Prize of the Society for Industrial and Applied Mathematics. In 2002, he was appointed to the Bass professorship. He was elected a SIAM Fellow and a foreign associate of the French Académie des sciences in 2009, and in the same year received an honorary doctorate from the University of Chicago. In 2010 he won the Norbert Wiener Prize in Applied Mathematics, given jointly by SIAM and the American Mathematical Society. He is also a member of the United States National Academy of Science.
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Practical information
- Informed public
- Free
- This event is internal
Organizer
- Martin Vetterli