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.
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.
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.
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
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.
ScaLAPACK Users' Guide
L. S. Blackford;J. Choi;A. Cleary;E. D'Azevedo.
ScaLAPACK user's guide
L. S. Blackford;J. Choi;A. Cleary;E. D'Azeuedo.
Templates for the Solution of Algebraic Eigenvalue Problems: A Practical Guide
James Demmel;Jack Dongarra;Axel Ruhe;Henk van der Vorst.
Health monitoring of civil infrastructures using wireless sensor networks
Sukun Kim;Shamim Pakzad;David Culler;James Demmel.
information processing in sensor networks (2007)
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)
Benchmarking GPUs to tune dense linear algebra
Vasily Volkov;James W. Demmel.
ieee international conference on high performance computing data and analytics (2008)
Optimization of sparse matrix-vector multiplication on emerging multicore platforms
Samuel Williams;Leonid Oliker;Richard Vuduc;John Shalf.
parallel computing (2009)
A view of the parallel computing landscape
Krste Asanovic;Rastislav Bodik;James Demmel;Tony Keaveny.
parallel computing (2009)
An Updated Set of Basic Linear Algebra Subprograms (BLAS)
Susan Blackford;James Demmel;Jack Dongarra;Iain Duff.
ACM Transactions on Mathematical Software (2002)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: