Tamara G. Kolda

Mathematical Consultant

MathSci.ai

About MathSci.ai

In the realm of AI, how can you differentiate the hype from the technologies that can truly benefit your organization? MathSci.ai provides unbiased, objective advice to clients in the following areas:

Identifying opportunities in which AI will advance your mission
Understanding the mathematical and statistical underpinnings of AI technologies
Developing appropriate data resources for AI training and evaluation
Navigating ethical considerations and protecting confidential data
Interacting with AI technology vendors and in-house data scientists
Setting realistic expectations and timelines for AI adoption

With a distinguished career, including over 20 years at Sandia National Laboratories and over 5 years in consulting, Tamara Kolda has worked with prominent nonprofits, government agencies, contractors, and technology companies. Based in the San Francisco Bay Area, she offers flexible, hourly-based assistance online or in person. Inquires about consulting can be sent to tammy.kolda@mathsci.ai.

Bio

Tamara Kolda, Ph.D., is an independent mathematical consultant under the auspices of her company MathSci.ai based in California and founded in 2021. From 1999-2021, she was a Distinguished Member of the Technical Staff at Sandia National Laboratories in Livermore, California. She is a member of the U.S. National Academy of Engineering (NAE), Fellow of the Society for Industrial and Applied Mathematics (SIAM), and Fellow of the Association for Computing Machinery (ACM). Her research is in the broad domain of mathematical data science. She has authored 75+ journal articles, conference proceedings papers, and book chapters. She is the author of two books: Tensor Decompositions for Data Science and Unlocking LaTeX Graphics: A Concise Guide to TikZ/PGF and PGFPLOTS. She is the SIAM Vice President for Publications, co-chair of the 2027 SIAM Conference on Linear Algebra (LA27), and Program Chair of the SIAM Activity Group on Equity, Diversity, and Inclusion (SIAG-EDI). She also sits on the board of the First Proof Foundation, which is dedicated to understanding the capabilities of AI systems on problems that come up in math research.

Links

More Talks

SIAM Conference on Applied Linear Algebra (LA27)
Long Beach, CA, May 24, 2027 - May 28, 2027 (Conference Co-Chair)
SIAM Conference on Computational Science and Engineering (CSE27)
Pittsburgh, Pennsylvania, USA, Feb 22, 2027 - Feb 26, 2027 (Minisymposium Organizer)
SIAM Conference on Mathematics of Data Science (MDS26)
Salt Lake City, UT, Nov 16, 2026 - Nov 20, 2026 (Organizing Committee Member and Minisymposium Speaker)
SIAM Northern and Central California Sectional Conference (NCC26)
UC Davis, Davis, CA, Oct 26, 2026 - Oct 27, 2026 (Attendee)
2026 SIAM Annual Meeting (AN26)
Cleveland, OH, Jul 6, 2026 - Jul 10, 2026 (Attendee)
Scientific Computing and Differential Equations (SciCADE 2026)
Edinburgh, UK, Jun 29, 2026 - Jul 3, 2026 (Plenary Speaker and Minisymposium Organizer)
Computing Sciences Colloquium, Lawrence Berkeley National Laboratory
Berkeley, CA, May 12, 2026 (Colloquium Speaker)
Dean’s Distinguished Lecture Series – University of Waterloo
Waterloo, ON, Canada, Apr 10, 2026 (Colloquium Speaker)
SIAM Northern and Central California Sectional Conference (NCC25)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA, Oct 27, 2025 - Oct 28, 2025 (Technical Program Committee and Panelist)

Recent Publications

More Publications

A Contract of Trust: Artificial Intelligence Usage for SIAM Journal Submissions
T. G. Kolda
SIAM News, 2026

Details URL

Revisiting Approximate Leverage Score Sketching for Matrix Least Squares
B. W. Larsen, T. G. Kolda
arXiv, 2026

Details PDF URL

Fast and Accurate CP-HIFI Tensor Decompositions: Exploiting Kronecker Structure
J. J. Brust, T. G. Kolda
arXiv, 2026

Details PDF URL

The Future of Math Research in the Age of AI
T. Kolda
Silicon Reckoner, 2026

Details URL

First Proof
M. Abouzaid, A. J. Blumberg, M. Hairer, J. Kileel, T. G. Kolda, P. D. Nelson, D. Spielman, N. Srivastava, R. Ward, S. Weinberger, L. Williams
arXiv, 2026

Details PDF URL

Tensor Decomposition with Unaligned Observations
R. Tang, T. Kolda, A. R. Zhang
SIAM Journal on Matrix Analysis and Applications, 2026

Details DOI URL (Full Access) Preprint URL Preprint PDF

Is it Safe to Deploy AI in Safety-Critical Systems?
T. G. Kolda
Philosophical Transactions of the Royal Society A, 2025

Details URL

Taming the Chaos of Computational Experiments
T. G. Kolda
SIAM News, 2025

Details URL

Tensor Decompositions for Data Science
G. Ballard, T. G. Kolda
Cambridge University Press, 2025

Details PDF URL

The Fascinating World of 2 $\times$ 2 $\times$ 2 Tensors: Its Geometry and Optimization Challenges
G. H. Brown, J. Kileel, T. G. Kolda
arXiv, 2025

Details PDF URL

Selected Older Publications

Tensor Decompositions and Applications
T. G. Kolda, B. W. Bader
SIAM Review, 2009

Details PDF DOI

Algorithm 862: MATLAB Tensor Classes for Fast Algorithm Prototyping
B. W. Bader, T. G. Kolda
ACM Transactions on Mathematical Software, 2006

Details PDF DOI

An Overview of the Trilinos Project
M. A. Heroux, R. A. Bartlett, V. E. Howle, R. J. Hoekstra, J. J. Hu, T. G. Kolda, …
ACM Transactions on Mathematical Software, 2005

Details PDF DOI

Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods
T. G. Kolda, R. M. Lewis, V. Torczon
SIAM Review, 2003

Details PDF DOI

Orthogonal Tensor Decompositions
T. G. Kolda
SIAM Journal on Matrix Analysis and Applications, 2001

Details PDF DOI

Graph Partitioning Models for Parallel Computing
B. Hendrickson, T. G. Kolda
Parallel Computing, 2000

Details PDF DOI

A Semidiscrete Matrix Decomposition for Latent Semantic Indexing Information Retrieval
T. G. Kolda, D. P. O’Leary
ACM Transactions on Information Systems, 1998

Details PDF DOI

Software

FEASTPACK - MATLAB and C codes for graph analysis, including BTER code for graph generation and our wedge sampling technique for triangle counting
Poblano Toolbox for MATLAB - Large-scale algorithms for unconstrained nonlinear optimization
HOPSPACK - A C++ hybrid optimization parallel search package for derivative-free optimization
MET - Memory-efficient Tucker computation (requires Tensor Toolbox)
Tensor Toolbox for MATLAB - Higher-order operations of multidimensional arrays
NOX - A C++ Nonlinear Solver Package
Trilinos - A suite of high-performance numerical codes (including NOX)
APPSPACK - A C++ derivative-free optimization package
SDDPACK - C and MATLAB code for the semi-discrete matrix decomposition

Contact

tammy.kolda@mathsci.ai (preferred) or tammy.kolda@gmail.com
MathSci.ai, Dublin, California, USA

Tamara G. Kolda

Mathematical Consultant

MathSci.ai

About MathSci.ai

Bio

Links

Recent Posts

Quoted in Trouw on the Leiden Declaration

I Signed the Leiden Declaration on AI and Mathematics

Milestone: 2⁶ YouTube Videos on Unlocking LaTeX Graphics

Essay: The Future of Math Research in the Age of AI

First Proof NYT Interview

Talks & Travel

Recent Publications

Selected Older Publications

Software

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