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- David L. Donoho

Discipline name
H-index
Citations
Publications
World Ranking
National Ranking

Mathematics
H-index
89
Citations
116,673
147
World Ranking
34
National Ranking
22

Computer Science
H-index
91
Citations
119,157
175
World Ranking
237
National Ranking
143

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

- Statistics
- Artificial intelligence
- Mathematical analysis

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.

- De-noising by soft-thresholding (7654 citations)
- Ideal spatial adaptation by wavelet shrinkage (6645 citations)
- Atomic Decomposition by Basis Pursuit (5658 citations)

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.

- Algorithm (25.46%)
- Wavelet (24.35%)
- Artificial intelligence (19.56%)

- Artificial intelligence (19.56%)
- Matrix (10.33%)
- Algorithm (25.46%)

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.

- The Optimal Hard Threshold for Singular Values is $4/\sqrt {3}$ (291 citations)
- 50 Years of Data Science (226 citations)
- High dimensional robust M-estimation: asymptotic variance via approximate message passing (115 citations)

- Statistics
- Artificial intelligence
- Mathematical analysis

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)**

13339 Citations

Atomic Decomposition by Basis Pursuit

Scott Shaobing Chen;David L. Donoho;Michael A. Saunders.

Siam Review **(2001)**

12558 Citations

Ideal spatial adaptation by wavelet shrinkage

David L Donoho;Iain M Johnstone.

Biometrika **(1994)**

10870 Citations

Sparse MRI: The application of compressed sensing for rapid MR imaging.

Michael Lustig;David Donoho;John M. Pauly.

Magnetic Resonance in Medicine **(2007)**

5933 Citations

Adapting to Unknown Smoothness via Wavelet Shrinkage

David L. Donoho;Iain M. Johnstone.

Journal of the American Statistical Association **(1995)**

5843 Citations

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)**

3499 Citations

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)**

3244 Citations

Fast Discrete Curvelet Transforms

Emmanuel J. Candès;Laurent Demanet;David L. Donoho;Lexing Ying.

Multiscale Modeling & Simulation **(2006)**

3153 Citations

Translation-Invariant De-Noising

R. R. Coifman;D. L. Donoho.

**(1995)**

3066 Citations

The curvelet transform for image denoising

Jean-Luc Starck;E.J. Candes;D.L. Donoho.

IEEE Transactions on Image Processing **(2002)**

3054 Citations

Profile was last updated on December 6th, 2021.

Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).

The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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