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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

James Demmel publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where James Demmel sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 82 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 343 publications — 90th percentile

90% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

James Demmel D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where James Demmel sits on this spectrum.

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 137 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 89 D-Index — 98th percentile

98% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

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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