World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
60
Citations
30354
World Ranking
3155
National Ranking
1527

Mathematics

D-Index
58
Citations
29530
World Ranking
611
National Ranking
307

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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