What is he best known for?
The fields of study he is best known for:
- 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.
His most cited work include:
- De-noising by soft-thresholding (7654 citations)
- Ideal spatial adaptation by wavelet shrinkage (6645 citations)
- Atomic Decomposition by Basis Pursuit (5658 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Algorithm (25.46%)
- Wavelet (24.35%)
- Artificial intelligence (19.56%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (19.56%)
- Matrix (10.33%)
- Algorithm (25.46%)
In recent papers he was focusing on the following fields of study:
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.
Between 2013 and 2021, his most popular works were:
- 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)
In his most recent research, the most cited papers focused on:
- 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.
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