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David Donoho auf dem Internationalen Mathematikerkongress 2018. David Leigh Donoho (* 5.März 1957 in Los Angeles) ist ein US-amerikanischer Statistiker.. Donoho studierte Statistik an der Princeton University (Bachelor-Abschluss summa cum laude bei John W. Tukey) und promovierte 1983 bei Peter Huber an der Harvard University (A tool for research in data analysis), während er gleichzeitig ...
David Leigh Donoho (born March 5, 1957) is an American statistician. He 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. [1] His work includes the development of effective methods for the construction of low-dimensional representations for high ...
2535. 2009. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. DL Donoho, C Grimes. Proceedings of the National Academy of Sciences 100 (10), 5591-5596. , 2003. 2266. 2003. New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities.
David Donoho is a renowned expert in signal processing, sparse signals, and compressed sensing. He has received the IEEE Signal Processing Medal and coined the term compressed sensing.
David L. Donoho is a renowned statistician and signal processing expert who has received many awards and honors, including the Shaw Prize and the Gauss Prize. He is the Anne T. and Robert M. Bass Professor in the Humanities and Sciences at Stanford University and has held visiting professorships at several institutions.
home - dave donoho. My theoretical research interests have focused on the mathematics of statistical inference and on theoretical questions arising in applying harmonic analysis to various applied problems. My applied research interests have ranged from data visualization to various problems in scientific signal processing, image processing ...
Professor David Donoho is the Anne T. and Robert M. Bass Professor of Humanities and Sciences and Professor of Statistics at Stanford University. He has made fundamental contributions to theoretical and computational statistics, and his algorithms have contributed significantly to our understanding of how to work with sparse data.
