2016 - Samuel S. Wilks Memorial Award, American Statistical Association (ASA)
2013 - Fellow of the American Mathematical Society
2010 - Norbert Wiener Prize in Applied Mathematics
2009 - Academie des sciences, France
2009 - SIAM Fellow For contributions to theoretical and computational statistics, signal processing and harmonic analysis.
2001 - John von Neumann Lecturer
1998 - Member of the National Academy of Sciences
1997 - Wald Memorial Lecturer
1994 - COPSS Presidents' Award
1992 - Fellow of the American Academy of Arts and Sciences
1991 - Fellow of the MacArthur Foundation
His primary areas of investigation include Wavelet, Algorithm, Discrete mathematics, Matching pursuit and Basis pursuit. His Wavelet study combines topics from a wide range of disciplines, such as Orthogonal basis, Radon transform and Thresholding. David L. Donoho has researched Algorithm in several fields, including Fourier transform, Mathematical optimization, Wavelet packet decomposition and Harmonic analysis.
His work carried out in the field of Mathematical optimization brings together such families of science as Wavelet noise and Estimator. His Discrete mathematics study combines topics in areas such as Information theory, Data compression, Gaussian and Calculus. The various areas that David L. Donoho examines in his Basis pursuit study include Convex optimization, Sparse approximation and Time–frequency analysis.
His scientific interests lie mostly in Algorithm, Wavelet, Artificial intelligence, Combinatorics and Mathematical optimization. The concepts of his Algorithm study are interwoven with issues in Theoretical computer science and Signal processing. His Wavelet research focuses on Sparse approximation and how it relates to Basis pursuit.
The study incorporates disciplines such as Computer vision and Pattern recognition in addition to Artificial intelligence. David L. Donoho interconnects Estimator, Density estimation and Applied mathematics in the investigation of issues within Mathematical optimization. His studies in Minimax integrate themes in fields like Discrete mathematics, Gaussian and Thresholding.
Artificial intelligence, Matrix, Algorithm, Singular value and Applied mathematics are his primary areas of study. Wavelet is the focus of his Artificial intelligence research. He has included themes like Covariance and Combinatorics in his Matrix study.
The various areas that David L. Donoho examines in his Algorithm study include Network planning and design, Artificial neural network, Sample size determination and Undersampling. His studies deal with areas such as Matrix norm, Singular value decomposition, Thresholding, Rank and White noise as well as Singular value. His biological study spans a wide range of topics, including Lasso, Mathematical optimization, Linear regression and Delta method.
David L. Donoho mainly investigates Matrix norm, Applied mathematics, Matrix, Singular value and White noise. As a part of the same scientific study, David L. Donoho usually deals with the Matrix norm, concentrating on Operator norm and frequently concerns with Estimation of covariance matrices, Condition number, Eigenvalues and eigenvectors and Covariance matrix. The study incorporates disciplines such as Estimator, Lasso, Mathematical optimization and Linear regression in addition to Applied mathematics.
His Matrix research incorporates elements of Lambda, Noise reduction, Gaussian and Minimax. The Singular value study combines topics in areas such as Bounded function, Combinatorics and Rank. David L. Donoho combines subjects such as Singular value decomposition, Thresholding, Mean squared error, Norm and Sigma with his study of White noise.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
De-noising by soft-thresholding
D.L. Donoho.
IEEE Transactions on Information Theory (1995)
Atomic Decomposition by Basis Pursuit
Scott Shaobing Chen;David L. Donoho;Michael A. Saunders.
Siam Review (2001)
Ideal spatial adaptation by wavelet shrinkage
David L Donoho;Iain M Johnstone.
Biometrika (1994)
Sparse MRI: The application of compressed sensing for rapid MR imaging.
Michael Lustig;David Donoho;John M. Pauly.
Magnetic Resonance in Medicine (2007)
Adapting to Unknown Smoothness via Wavelet Shrinkage
David L. Donoho;Iain M. Johnstone.
Journal of the American Statistical Association (1995)
For most large underdetermined systems of linear equations the minimal 1-norm solution is also the sparsest solution
David L. Donoho.
Communications on Pure and Applied Mathematics (2006)
Optimally sparse representation in general (nonorthogonal) dictionaries via 1 minimization
David L. Donoho;Michael Elad.
Proceedings of the National Academy of Sciences of the United States of America (2003)
Fast Discrete Curvelet Transforms
Emmanuel J. Candès;Laurent Demanet;David L. Donoho;Lexing Ying.
Multiscale Modeling & Simulation (2006)
Translation-Invariant De-Noising
R. R. Coifman;D. L. Donoho.
(1995)
The curvelet transform for image denoising
Jean-Luc Starck;E.J. Candes;D.L. Donoho.
IEEE Transactions on Image Processing (2002)
Profile was last updated on December 6th, 2021.
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