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- James Demmel

Mathematics

USA

2023

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

Mathematics
D-index
82
Citations
32,355
319
World Ranking
83
National Ranking
47

Computer Science
D-index
85
Citations
35,878
354
World Ranking
457
National Ranking
267

2023 - Research.com Mathematics in United States Leader Award

2018 - Fellow of the American Academy of Arts and Sciences

2015 - Fellow of the American Association for the Advancement of Science (AAAS)

2014 - ACM Paris Kanellakis Theory and Practice Award For contributions to algorithms and software for numerical linear algebra used in scientific computing and large-scale data analysis.

2013 - Fellow of the American Mathematical Society

2011 - Member of the National Academy of Sciences

2009 - SIAM Fellow For contributions to numerical linear algebra, including the LAPACK project.

2002 - IEEE Fellow For contributions to the field of computational mathematics and the development of mathematical software.

1999 - Member of the National Academy of Engineering For contributions to numerical linear algebra and scientific computing.

1999 - ACM Fellow For outstanding contributions to scientific computing, parallel processing and software engineering.

- Algorithm
- Operating system
- Programming language

His scientific interests lie mostly in Parallel computing, Sparse matrix, Matrix multiplication, Linear algebra and Algorithm. His Parallel computing study combines topics from a wide range of disciplines, such as Scalability, Computation, ScaLAPACK and Data structure. His Matrix multiplication research integrates issues from Supercomputer, LU decomposition, Parallel algorithm, Multiplication and QR decomposition.

His Linear algebra research includes elements of Discrete mathematics, Basic Linear Algebra Subprograms and Numerical linear algebra. His Numerical linear algebra study incorporates themes from Eigenvalues and eigenvectors, System of linear equations and Relaxation. His work in Algorithm addresses issues such as Linear system, which are connected to fields such as Gaussian elimination and Solver.

- Applied Numerical Linear Algebra (2277 citations)
- Templates for the Solution of Algebraic Eigenvalue Problems: A Practical Guide (999 citations)
- Health monitoring of civil infrastructures using wireless sensor networks (857 citations)

James Demmel mainly investigates Parallel computing, Algorithm, Matrix, Matrix multiplication and Linear algebra. His Parallel computing research integrates issues from Computation and ScaLAPACK. While the research belongs to areas of Algorithm, James Demmel spends his time largely on the problem of Sparse matrix, intersecting his research to questions surrounding Kernel and Sparse approximation.

His Matrix research incorporates themes from Eigenvalues and eigenvectors and Combinatorics. His study focuses on the intersection of Matrix multiplication and fields such as Discrete mathematics with connections in the field of Upper and lower bounds. James Demmel has included themes like Memory hierarchy, Theoretical computer science, Basic Linear Algebra Subprograms and Numerical linear algebra in his Linear algebra study.

- Parallel computing (25.17%)
- Algorithm (22.86%)
- Matrix (16.40%)

- Parallel computing (25.17%)
- Algorithm (22.86%)
- Artificial intelligence (4.85%)

James Demmel mostly deals with Parallel computing, Algorithm, Artificial intelligence, Speedup and Computation. His work deals with themes such as Sparse matrix, Krylov subspace, Matrix multiplication and Scaling, which intersect with Parallel computing. His work in Algorithm tackles topics such as Block which are related to areas like Iterative method.

His research on Speedup also deals with topics like

- Asynchronous communication which is related to area like sync and Solver,
- Scalability, which have a strong connection to Kernel ridge regression. His research integrates issues of Singular value, Tridiagonal matrix, Bidiagonal matrix, Cluster analysis and Dimensionality reduction in his study of Computation. His Parallel algorithm study combines topics from a wide range of disciplines, such as Eigenvalues and eigenvectors, Band matrix, QR decomposition, Symmetric matrix and Numerical linear algebra.

- ImageNet Training in Minutes (202 citations)
- Large Batch Optimization for Deep Learning: Training BERT in 76 minutes (133 citations)
- Solving Sparse Linear Systems with Sparse Backward Error (117 citations)

- Operating system
- Algorithm
- Programming language

His primary scientific interests are in Artificial intelligence, Parallel computing, Speedup, Algorithm and Machine learning. His Parallel computing study frequently links to adjacent areas such as Multigrid method. His Speedup study combines topics in areas such as Artificial neural network, Simulation, Reduction and Asynchronous communication.

His Algorithm research is multidisciplinary, incorporating elements of Theoretical computer science, Microarchitecture, Cache, Matrix and Mathematical optimization. His Matrix study often links to related topics such as Computation. His biological study spans a wide range of topics, including Parallel algorithm, Eigenvalues and eigenvectors, Symmetric matrix and Kernel.

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.

Applied Numerical Linear Algebra

James W. Demmel.

**(1997)**

4350 Citations

ScaLAPACK Users' Guide

L. S. Blackford;J. Choi;A. Cleary;E. D'Azevedo.

**(1987)**

2332 Citations

ScaLAPACK user's guide

L. S. Blackford;J. Choi;A. Cleary;E. D'Azeuedo.

**(1997)**

2328 Citations

Templates for the Solution of Algebraic Eigenvalue Problems: A Practical Guide

James Demmel;Jack Dongarra;Axel Ruhe;Henk van der Vorst.

**(1987)**

1932 Citations

Health monitoring of civil infrastructures using wireless sensor networks

Sukun Kim;Shamim Pakzad;David Culler;James Demmel.

information processing in sensor networks **(2007)**

1470 Citations

A Supernodal Approach to Sparse Partial Pivoting

James W. Demmel;Stanley C. Eisenstat;John R. Gilbert;Xiaoye S. Li.

SIAM Journal on Matrix Analysis and Applications **(1999)**

1203 Citations

Benchmarking GPUs to tune dense linear algebra

Vasily Volkov;James W. Demmel.

ieee international conference on high performance computing data and analytics **(2008)**

1116 Citations

Optimization of sparse matrix-vector multiplication on emerging multicore platforms

Samuel Williams;Leonid Oliker;Richard Vuduc;John Shalf.

parallel computing **(2009)**

987 Citations

A view of the parallel computing landscape

Krste Asanovic;Rastislav Bodik;James Demmel;Tony Keaveny.

parallel computing **(2009)**

879 Citations

An Updated Set of Basic Linear Algebra Subprograms (BLAS)

Susan Blackford;James Demmel;Jack Dongarra;Iain Duff.

ACM Transactions on Mathematical Software **(2002)**

867 Citations

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