World's Best Scientists 2026 revealed!
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Mathematics
USA
2026

D-Index & Metrics

Computer Science

D-Index
95
Citations
49958
World Ranking
453
National Ranking
249

Mathematics

D-Index
89
Citations
38149
World Ranking
78
National Ranking
47

Research.com Recognitions

  • 2026 - Research.com Mathematics in United States Leader Award
  • 2025 - 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.

Overview

James Demmel is affiliated with the University of California, Berkeley in the United States. Their research primarily lies within the field of Computer Science, with a notable focus on subfields such as Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Hardware and Architecture, and Computational Mechanics.

Demmel's scholarly output includes recent papers such as:

  • Algorithms for Efficient Reproducible Floating Point Summation, 2020, ACM Transactions on Mathematical Software
  • Randomized Numerical Linear Algebra: A Perspective on the Field With an Eye to Software, 2023, arXiv (Cornell University)
  • Large scale multi-GPU based parallel traffic simulation for accelerated traffic assignment and propagation, 2024, Transportation Research Part C Emerging Technologies
  • Fast Bilinear Algorithms for Symmetric Tensor Contractions, 2020, Computational Methods in Applied Mathematics
  • Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization, 2024, Journal of Computational and Graphical Statistics

Their frequent co-authors include Hengrui Luo, Aydın Buluç, Yang You, Riley Murray, and Younghyun Cho.

Demmel publishes often in venues such as arXiv (Cornell University), ACM Transactions on Mathematical Software, SIAM Journal on Matrix Analysis and Applications, SIAM Journal on Scientific Computing, and Transportation Research Part C Emerging Technologies.

The main topics addressed in Demmel's work cover:

  • Stochastic Gradient Optimization Techniques
  • Parallel Computing and Optimization Techniques
  • Matrix Theory and Algorithms
  • Advanced Neural Network Applications
  • Sparse and Compressive Sensing Techniques
  • Advanced Multi-Objective Optimization Algorithms
  • Gaussian Processes and Bayesian Inference

Demmel has received several distinguished awards throughout their career, including:

  • Fellow of the American Academy of Arts and Sciences (2018)
  • Fellow of the American Association for the Advancement of Science (AAAS) (2015)
  • ACM Paris Kanellakis Theory and Practice Award (2014) for contributions to algorithms and software for numerical linear algebra used in scientific computing and large-scale data analysis
  • Fellow of the American Mathematical Society (2013)
  • Member of the National Academy of Sciences (2011)
  • SIAM Fellow (2009) for contributions to numerical linear algebra, including the LAPACK project
  • IEEE Fellow (2002) for contributions to computational mathematics and mathematical software development
  • Member of the National Academy of Engineering (1999) for contributions to numerical linear algebra and scientific computing
  • ACM Fellow (1999) for outstanding contributions to scientific computing, parallel processing, and software engineering

Best Publications

  • 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

  • A Supernodal Approach to Sparse Partial Pivoting

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

  • Benchmarking GPUs to tune dense linear algebra

    Vasily Volkov;James W. Demmel

  • An Updated Set of Basic Linear Algebra Subprograms (BLAS)

    Susan Blackford;James Demmel;Jack Dongarra;Iain Duff

  • Optimization of sparse matrix-vector multiplication on emerging multicore platforms

    Samuel Williams;Leonid Oliker;Richard Vuduc;John Shalf

  • IEEE Standard for Floating-Point Arithmetic

    Dan Zuras;Mike Cowlishaw;Alex Aiken;Matthew Applegate

  • Optimization of sparse matrix-vector multiplication on emerging multicore platforms

    Samuel Williams;Leonid Oliker;Richard Vuduc;John Shalf

  • A view of the parallel computing landscape

    Krste Asanovic;Rastislav Bodik;James Demmel;Tony Keaveny

  • SuperLU_DIST: A scalable distributed-memory sparse direct solver for unsymmetric linear systems

    Xiaoye S. Li;James W. Demmel

  • Optimizing matrix multiply using PHiPAC: a portable, high-performance, ANSI C coding methodology

    Jeff Bilmes;Krste Asanovic;Chee-Whye Chin;Jim Demmel

  • OSKI: A Library of Automatically Tuned Sparse Matrix Kernels

    Richard Vuduc;James W Demmel;Katherine A Yelick

  • LAPACK: a portable linear algebra library for high-performance computers

    E. Anderson;Z. Bai;J. Dongarra;A. Greenbaum

  • LAPACK Users' Guide, 3rd ed.

    Ed Anderson;Zhaojun Bai;Christian Bischof;Susan Blackford

  • Numerical linear algebra on emerging architectures: The PLASMA and MAGMA projects

    Emmanuel Agullo;Jim Demmel;Jack Dongarra;Bilel Hadri

  • Jacobi's Method Is More Accurate Than Qr

    James Demmel;Krešimir Veselic

  • Accurate singular values of bidiagonal matrices

    James Demmel;W. Kahan

  • Large Batch Optimization for Deep Learning: Training BERT in 76 minutes

    Yang You;Jing Li;Sashank Reddi;Jonathan Hseu

  • ScaLAPACK: A Portable Linear Algebra Library for Distributed Memory Computers - Design Issues and Performance

    Laura Susan Blackford;J. Choi;A. Cleary;A. Petitet

  • Optimization of Sparse Matrix-Vector Multiplication on EmergingMulticore Platforms

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

Frequent Co-Authors

Jack Dongarra
Jack Dongarra University of Tennessee at Knoxville
Katherine Yelick
Katherine Yelick University of California, Berkeley
Zhaojun Bai
Zhaojun Bai University of California, Davis
Richard Vuduc
Richard Vuduc Georgia Institute of Technology
Xiaoye S. Li
Xiaoye S. Li Lawrence Berkeley National Laboratory
Cho-Jui Hsieh
Cho-Jui Hsieh University of California, Los Angeles
Kurt Keutzer
Kurt Keutzer University of California, Berkeley
Christian Bischof
Christian Bischof Technical University of Darmstadt
Richard S. Muller
Richard S. Muller University of California, Berkeley
Inderjit S. Dhillon
Inderjit S. Dhillon Google (United States)

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