World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
58
Citations
29530
World Ranking
611
National Ranking
308

Computer Science

D-Index
60
Citations
30354
World Ranking
3155
National Ranking
1528

Tamara G. Kolda 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 Tamara G. Kolda 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: 83 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: 170 publications — 49th percentile

49% of scientists in this discipline score the same or lower.

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

Tamara G. Kolda 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 Tamara G. Kolda 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: 138 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: 58 D-Index — 83rd percentile

83% 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

  • 2020 - Member of the National Academy of Engineering For contributions to the design of scientific software, including tensor decompositions and multilinear algebra.
  • 2019 - ACM Fellow For innovations in algorithms for tensor decompositions, contributions to data science, and community leadership
  • 2015 - SIAM Fellow For contributions to numerical algorithms and software in multi-linear algebra, optimization, and graph analysis.
  • 2011 - ACM Distinguished Member
  • 2009 - ACM Senior Member

Overview

Tamara G. Kolda is an independent scientist and consultant based in the United States. Their research primarily spans the fields of computer science and mathematics, with significant contributions to computational mathematics and artificial intelligence. The scientist's work is characterized by a focus on tensor decomposition and applications, and it also extends to areas such as sparse and compressive sensing techniques, computational physics with Python applications, advanced neural network applications, matrix theory and algorithms, algorithms and data compression, and machine learning and data classification.

Publications by Tamara G. Kolda include a range of recent papers published in prominent venues. Among them are:

  • Generalized Canonical Polyadic Tensor Decomposition, 2020, SIAM Review
  • Practical Leverage-Based Sampling for Low-Rank Tensor Decomposition, 2022, SIAM Journal on Matrix Analysis and Applications
  • Practical Leverage-Based Sampling for Low-Rank Tensor Decomposition, 2020, arXiv (Cornell University)
  • Tensor Moments of Gaussian Mixture Models: Theory and Applications, 2022, arXiv (Cornell University)
  • Scalable Symmetric Tucker Tensor Decomposition, 2024, SIAM Journal on Matrix Analysis and Applications

The frequent publication venues for this scientist predominantly include:

  • arXiv (Cornell University)
  • SIAM Journal on Matrix Analysis and Applications
  • SIAM Review
  • Harvard Data Science Review

Collaborations have played a notable role in Tamara G. Kolda's research output. The scientist has frequently worked with the following co-authors:

  • Brett W. Larsen
  • Joe Kileel
  • Rachel Ward
  • Ruhui Jin
  • Anru R. Zhang

Kolda has authored a book titled Tensor Decompositions for Data Science, which is forthcoming in 2025 from Cambridge University Press. This work adds to their extensive research portfolio in the area of tensor methods and their applications within data science.

Recognition of the scientist's contributions includes multiple awards and honors:

  • Member of the National Academy of Engineering (2020) for contributions to the design of scientific software, including tensor decompositions and multilinear algebra
  • ACM Fellow (2019) for innovations in algorithms for tensor decompositions, contributions to data science, and community leadership
  • SIAM Fellow (2015) for contributions to numerical algorithms and software in multi-linear algebra, optimization, and graph analysis
  • ACM Distinguished Member (2011)
  • ACM Senior Member (2009)

Best Publications

  • Tensor Decompositions and Applications

    Tamara G. Kolda;Brett W. Bader

  • Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods ∗

    Tamara G. Kolda;Robert Michael Lewis;Virginia Torczon

  • An overview of the Trilinos project

    Michael A. Heroux;Roscoe A. Bartlett;Vicki E. Howle;Robert J. Hoekstra

  • Scalable tensor factorizations for incomplete data

    Evrim Acar;Daniel M. Dunlavy;Tamara G. Kolda;Morten Mørup

  • Temporal Link Prediction Using Matrix and Tensor Factorizations

    Daniel M. Dunlavy;Tamara G. Kolda;Evrim Acar

  • Graph partitioning models for parallel computing

    Bruce Hendrickson;Tamara G. Kolda

  • Algorithm 862: MATLAB tensor classes for fast algorithm prototyping

    Brett W. Bader;Tamara G. Kolda

  • Efficient MATLAB Computations with Sparse and Factored Tensors

    Brett W. Bader;Tamara G. Kolda

  • Orthogonal Tensor Decompositions

    Tamara G. Kolda

  • An overview of Trilinos.

    Kevin R. Long;Raymond Stephen Tuminaro;Roscoe Ainsworth Bartlett;Robert John Hoekstra

  • Multilinear operators for higher-order decompositions

    Tamara Gibson Kolda

  • Scalable Tensor Decompositions for Multi-aspect Data Mining

    T.G. Kolda;Jimeng Sun

  • Shifted Power Method for Computing Tensor Eigenpairs

    Tamara G. Kolda;Jackson R. Mayo

  • A scalable optimization approach for fitting canonical tensor decompositions

    Evrim Acar;Daniel M. Dunlavy;Tamara G. Kolda

  • Higher-order Web link analysis using multilinear algebra

    T.G. Kolda;B.W. Bader;J.P. Kenny

  • Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis.

    Alex H. Williams;Tony Hyun Kim;Forea Wang;Saurabh Vyas

  • A semidiscrete matrix decomposition for latent semantic indexing information retrieval

    Tamara G. Kolda;Dianne P. O'Leary

  • All-at-once Optimization for Coupled Matrix and Tensor Factorizations

    Evrim Acar;Tamara G. Kolda;Daniel M. Dunlavy

  • On Tensors, Sparsity, and Nonnegative Factorizations

    Eric C. Chi;Tamara G. Kolda

  • A Practical Randomized CP Tensor Decomposition

    Casey Battaglino;Grey Ballard;Tamara G. Kolda

  • Community structure and scale-free collections of Erdős-Rényi graphs.

    C. Seshadhri;Tamara G. Kolda;Ali Pinar

Frequent Co-Authors

Ali Pinar
Ali Pinar Sandia National Laboratories
C. Seshadhri
C. Seshadhri University of California, Santa Cruz
Dianne P. O'Leary
Dianne P. O'Leary University of Maryland, College Park
Bruce Hendrickson
Bruce Hendrickson Lawrence Livermore National Laboratory
Jaideep Srivastava
Jaideep Srivastava University of Minnesota
Christos Faloutsos
Christos Faloutsos Carnegie Mellon University
Jimeng Sun
Jimeng Sun University of Illinois at Urbana-Champaign
Robert A. van de Geijn
Robert A. van de Geijn The University of Texas at Austin
Stephen I. Ryu
Stephen I. Ryu Stanford University
Jaijeet Roychowdhury
Jaijeet Roychowdhury University of California, Berkeley

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