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- David P. Woodruff

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
disciplines.
Citations
Publications
World Ranking
National Ranking

Computer Science
D-index
52
Citations
9,710
312
World Ranking
3387
National Ranking
1740

- Statistics
- Algorithm
- Combinatorics

David P. Woodruff mostly deals with Combinatorics, Discrete mathematics, Matrix, Upper and lower bounds and Algorithm. David P. Woodruff is interested in Binary logarithm, which is a branch of Combinatorics. The study incorporates disciplines such as Function, Subspace topology, Arbitrarily large and Bounded function in addition to Discrete mathematics.

David P. Woodruff does research in Matrix, focusing on Low-rank approximation specifically. His Low-rank approximation study integrates concerns from other disciplines, such as Embedding and Numerical linear algebra. His research integrates issues of Singular value, Norm and Communication complexity in his study of Upper and lower bounds.

- Sketching as a Tool for Numerical Linear Algebra (511 citations)
- Low rank approximation and regression in input sparsity time (350 citations)
- Fast approximation of matrix coherence and statistical leverage (302 citations)

David P. Woodruff mainly investigates Combinatorics, Upper and lower bounds, Discrete mathematics, Matrix and Algorithm. In general Combinatorics, his work in Binary logarithm is often linked to Omega linking many areas of study. His work deals with themes such as Dimension, Norm, Streaming algorithm, Bounded function and Communication complexity, which intersect with Upper and lower bounds.

David P. Woodruff interconnects Sampling, Polynomial and Matching in the investigation of issues within Discrete mathematics. His research in Matrix intersects with topics in Function, Subspace topology and Singular value decomposition. His work is dedicated to discovering how Algorithm, Data stream mining are connected with Data stream and other disciplines.

- Combinatorics (51.80%)
- Upper and lower bounds (30.46%)
- Discrete mathematics (28.30%)

- Combinatorics (51.80%)
- Upper and lower bounds (30.46%)
- Matrix (26.86%)

His scientific interests lie mostly in Combinatorics, Upper and lower bounds, Matrix, Low-rank approximation and Algorithm. His work on Binary logarithm is typically connected to Omega as part of general Combinatorics study, connecting several disciplines of science. David P. Woodruff has included themes like Discrete mathematics, Communication complexity, Dimension, Matching and Bounded function in his Upper and lower bounds study.

The various areas that he examines in his Matrix study include Distribution, Singular value decomposition and Rank. The concepts of his Low-rank approximation study are interwoven with issues in Time complexity, Approximation algorithm and Matrix norm. His study in Algorithm is interdisciplinary in nature, drawing from both Sampling, Kernel, Streaming algorithm and Numerical linear algebra.

- Relative error tensor low rank approximation (35 citations)
- Oblivious sketching of high-degree polynomial kernels (22 citations)
- Faster Algorithms for High-Dimensional Robust Covariance Estimation (21 citations)

- Algorithm
- Statistics
- Combinatorics

David P. Woodruff mainly focuses on Combinatorics, Algorithm, Matrix, Upper and lower bounds and Low-rank approximation. In general Combinatorics study, his work on Binary logarithm often relates to the realm of Omega, thereby connecting several areas of interest. The Algorithm study combines topics in areas such as Kernel, Streaming algorithm, Numerical linear algebra and Matrix norm.

His studies in Matrix integrate themes in fields like Time complexity, Distribution and Trace. His research in Upper and lower bounds focuses on subjects like Sampling, which are connected to Data stream and Bounded function. David P. Woodruff has researched Low-rank approximation in several fields, including Distance matrices in phylogeny, Approximation algorithm, Kronecker product, Metric space and Distance matrix.

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.

Sketching as a Tool for Numerical Linear Algebra

David P. Woodruff.

**(2014)**

814 Citations

Low-Rank Approximation and Regression in Input Sparsity Time

Kenneth L. Clarkson;David P. Woodruff.

Journal of the ACM **(2017)**

619 Citations

Fast approximation of matrix coherence and statistical leverage

Petros Drineas;Malik Magdon-Ismail;Michael W. Mahoney;David P. Woodruff.

Journal of Machine Learning Research **(2012)**

451 Citations

Numerical linear algebra in the streaming model

Kenneth L. Clarkson;David P. Woodruff.

symposium on the theory of computing **(2009)**

362 Citations

An optimal algorithm for the distinct elements problem

Daniel M. Kane;Jelani Nelson;David P. Woodruff.

symposium on principles of database systems **(2010)**

327 Citations

Optimal approximations of the frequency moments of data streams

Piotr Indyk;David Woodruff.

symposium on the theory of computing **(2005)**

281 Citations

Optimal space lower bounds for all frequency moments

David Woodruff.

symposium on discrete algorithms **(2004)**

191 Citations

Lower bounds for sparse recovery

Khanh Do Ba;Piotr Indyk;Eric Price;David P. Woodruff.

symposium on discrete algorithms **(2010)**

175 Citations

Optimal bounds for Johnson-Lindenstrauss transforms and streaming problems with sub-constant error

T. S. Jayram;David P. Woodruff.

symposium on discrete algorithms **(2011)**

174 Citations

On the exact space complexity of sketching and streaming small norms

Daniel M. Kane;Jelani Nelson;David P. Woodruff.

symposium on discrete algorithms **(2010)**

159 Citations

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