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 the past 3 years in consulting (see below for more),
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.
Honors
Dr. Kolda has received national and international honors. 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). Other recognitions
include two best paper prizes from the IEEE International Conference
on Data Mining (ICDM), a best paper prize from the SIAM International
Conference on Data Mining (SDM), an R&D100 Award from R&D Magazine,
and a Presidential Early Career Award for Scientists and Engineers
(PECASE).
Research
Her research is in the broad domain of mathematical data science.
She is known for work on tensor decompositions, tensor eigenvalues,
graph algorithms, randomized algorithms, machine learning, network
science, numerical optimization, and distributed and parallel
computing.
She has authored 75+ journal articles, conference proceedings papers,
and book chapters.
She has led the development of numerous software packages, including
the Tensor Toolbox.
She is also the author of two books:
Tensor Decompositions for Data Science
and
Unlocking LaTeX Graphics: A Concise Guide to TikZ/PGF and PGFPLOTS.
Service
In terms of professional service,
Dr. Kolda chaired the National Academies’ Committee on
Illustrating the Impact of the Mathematical Science,
which produced a series of posters
showing how math is improving our everyday lives.
She was the founding
Editor-in-Chief (EIC) for the SIAM Journal on Mathematics of Data
Science (SIMODS) from 2018-2023, and she currently serves as
Section Editor. She is also currently a member of the editorial board
of Information and Inference: A Journal of the IMA. She is
on the board of advisors for the Institute for Mathematical and
Statistical Innovation (IMSI).
She has been a member of the Schmidt
Postdoctoral Fellowship Selection Committee for the past four years.
She is the current Chair of the SIAM Activity Group on Equity,
Diversity, and Inclusion (SIAG-EDI).
Links
Interests
- Tensor Decompositions
- Numerical Optimization
- Linear Algebra
- Randomized Algorithms
- Network Science
- Data Science and Machine Learning
- High-Performance Computing
Education and Training
-
Alton S. Householder Postdoc in Scientific Computing, 1997-99
Oak Ridge National Laboratory
-
PhD in Applied Mathematics, 1997
University of Maryland, College Park (UMCP)
-
BS in Applied Mathematics, 1992
University of Maryland, Baltimore County (UMBC)
