About MathSci.ai
MathSci.ai is the consulting company of Tammy Kolda and is based in Dublin, California.
Dr. Kolda holds a Ph.D. and has over two decades of scientific research experience in mathematical and computational data science,
with deep expertise in tensor decompositions for data analysis.
She has published extensively, given keynote and plenary lectures at various international conferences,
and worked on practical problems in application domains from science, medicine, and business.
Her work has been cited over 20,000 times.
Her consulting has different flavors,
including strategic advice on
machine learning research and development that is tailored to the customer’s specific data domain,
in-depth technical consulting,
development of specialized training courses on tensor decompositions (textbook forthcoming),
evaluation of candidates for academic appointments, etc.
Inquires about consulting can be sent to the email listed below.
Brief Bio for Tamara G. Kolda
Tamara Kolda is an independent mathematical consultant under the auspices of her company MathSci.ai based in California. From 1999-2021, she was a member of the technical staff at Sandia National Laboratories, California. She specializes in mathematical algorithms and computation methods for tensor decompositions, tensor eigenvalues, graph algorithms, randomized algorithms, machine learning, network science, numerical optimization, and distributed and parallel computing. She is the founding editor-in-chief for the SIAM Journal on Mathematics of Data Science. She is a member of the National Academy of Engineering (NAE), Fellow of the Society for Industrial and Applied Mathematics (SIAM), and Fellow of the Association for Computing Machinery (ACM).
Longer Bio
Since 2021, Dr. Kolda consults as her main occupation.
She is also a Distinguished Visiting Professor in the Department of Industrial Engineering & Management Science at Northwestern University in Evanston, Illinois.
From 1999-2021, she was a researcher at Sandia National Laboratories in Livermore, California.
Her current professional leadership includes
Editor-in-Chief for SIAM Journal on the Mathematics of Data Science (SIMODS),
Senior Program Committee for the Conference on Learning Theory (COLT’22), and
Chair of the Illustrating the Impact of the Mathematical Sciences study for the National Academies.
She is also a member of the National Academies’ Board on Mathematical Sciences and Analytics (BMSA),
the board of advisors for the Institute for Mathematical and Statistical Innovation (IMSI),
the SIAM Ethics Committee,
the SIAM Block Lecture Selection Committee,
the ACM-IEEE CS George Michael Memorial HPC Fellowships Committee,
and the Schmidt Postdoctoral Fellowship Selection Committee.
In recognition of her work, she has been elected to the National Academy of Engineering (NAE) and named Fellow of both the Society for Industrial and Applied Mathematics (SIAM) and the Association for Computing Machinery (ACM).
Other recognitions include two best paper prizes from the IEEE Interational Conference on Data Mining (ICDM), a best paper prize from the SIAM Interational Conference on Data Mining (SDM), an R&D100 Award from R&D Magazine, and a Presidential Early Career Award for Scientists and Engineers (PECASE).
Interests
- Tensor Decompositions
- Numerical Optimization
- Linear Algebra
- Randomized Algorithms
- Network Science
- Data Science and Machine Learning
- High-Performance Computing
Education and Training
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Alton S. Householder Postdoc in Scientific Computing, 1997-99
Oak Ridge National Laboratory
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PhD in Applied Mathematics, 1997
University of Maryland, College Park (UMCP)
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BS in Applied Mathematics, 1992
University of Maryland, Baltimore County (UMBC)
