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David Donoho is a mathematician who has made fundamental contributions to theoretical and computational statistics, as well as signal processing and harmonic analysis. His algorithms have significantly enhanced the understanding of the maximum entropy principle, the structure of robust procedures, and the description of sparse data. His theoretical research interests center around the mathematical foundations of statistical inference, specifically addressing theoretical questions that arise when applying harmonic analysis to practical problems. In terms of applied research, Donoho has dealt with issues related to data visualization, scientific signal processing, image processing, and inverse problems.
Stanford University • Stanford, California
Member of the academic faculty in the Department of Statistics.
The Computer Science department emphasizes research potential. GRE General is currently optional but recommended for some tracks.