What is he best known for?
The fields of study he is best known for:
- 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.
His most cited work include:
- 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)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Combinatorics (51.80%)
- Upper and lower bounds (30.46%)
- Discrete mathematics (28.30%)
What were the highlights of his more recent work (between 2017-2021)?
- Combinatorics (51.80%)
- Upper and lower bounds (30.46%)
- Matrix (26.86%)
In recent papers he was focusing on the following fields of study:
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.
Between 2017 and 2021, his most popular works were:
- 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)
In his most recent research, the most cited papers focused on:
- 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.
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