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Tamara G. Kolda
Alma mater University of Maryland Baltimore County
University of Maryland College Park
Awards
  • ACM Fellow (2019)
  • Presidential Early Career Award for Scientists and Engineers (2003)
Scientific career
Fields Applied mathematics
Computational science
Institutions Oak Ridge National Laboratory
Sandia National Laboratories
Thesis Limited-Memory Matrix Methods With Applications (1997)
Doctoral advisor Dianne P. O'Leary

Tamara G. Kolda is an American applied mathematician and former Distinguished Member of Technical Staff at Sandia National Laboratories. She is noted for her contributions in computational science, multilinear algebra, data mining, graph algorithms, mathematical optimization, parallel computing, and software engineering. She is currently a member of the SIAM Board of Trustees and served as associate editor for both the SIAM Journal on Scientific Computing and the SIAM Journal on Matrix Analysis and Applications.

Education

Kolda received her bachelor's degree in mathematics in 1992 from the University of Maryland Baltimore County and her PhD in applied mathematics from the University of Maryland College Park in 1997.

Career and research

Kolda was a Householder Postdoctoral Fellow at Oak Ridge National Laboratory from 1997 to 1999 before joining Sandia National Laboratories.

Awards and honors

Kolda received a Presidential Early Career Award for Scientists and Engineers in 2003, best paper prizes at the 2008 IEEE International Conference on Data Mining and the 2013 SIAM International Conference on Data Mining, and has been a distinguished member of the Association for Computing Machinery since 2011. She was elected a Fellow of the Society for Industrial and Applied Mathematics in 2015. She was elected a Fellow of the Association for Computing Machinery in 2019 for "innovations in algorithms for tensor decompositions, contributions to data science, and community leadership." She was elected to the National Academy of Engineering in 2020, for "contributions to the design of scientific software, including tensor decompositions and multilinear algebra".

See also

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