Here are tools for promoting SIAM’s latest journal - the SIAM Journal on Mathematics of Data Science (SIMODS).
Dr. Kolda is a Distinguished Member of the Technical Staff at Sandia National Laboratories. She has led numerous projects in computational science and data analysis on topics in multilinear algebra and tensor decompositions, graph models and algorithms, data mining, optimization, nonlinear solvers, parallel computing and the design of scientific software. Her work has been cited more than 13,000 times.
Her work has received several honors, including a 2003 Presidential Early Career Award for Scientists and Engineers (PECASE), an R&D100 award, and three best paper prizes at international conferences. She was named a Distinguished Scientist of the Association for Computing Machinery (ACM) in 2011 and a Fellow of the Society for Industrial and Applied Mathematics (SIAM) in 2015.
She has given keynotes talks at a variety of meetings including the International Symposium on Mathematical Programming (ISMP), the SIAM Conference on Computational Science & Engineering (CS&E), the SIAM Annual Meeting, and the IEEE International Conference on Data Mining (ICDM). She is currently a member of the SIAM Board of Trustees and serves as associate editor for both the SIAM J. Scientific Computing and the SIAM J. Matrix Analysis and Applications. She is also the editor-in-chief of the newly announced SIAM J. Mathematics of Data Science.
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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)
Here are tools for promoting SIAM’s latest journal - the SIAM Journal on Mathematics of Data Science (SIMODS).
In December 2017, SIAM gave its final approval for a brand new journal! The SIAM Journal on Mathematics of Data Science (SIMODS), which will publish work that advances mathematical, statistical, and computational methods in the context of data and information sciences.
Along with Danny Dunlavy, I will be teaching a tutorial on “The Canonical Polyadic Tensor Decomposition and Variants for Mining Multi-Dimensional Data” at the SIAM International Conference on Data Mining (SDM18) in May 2018.
Sometimes the term sparse is used to refer to a matrix that has a large fraction of missing entries, but the more typical usage of that term is to refer to a matrix that has a large fraction of zero entries. We instead recommend the term scarce for a large amount of missing data and discuss various scenarios.
London, UK, Aug 19, 2018 - Aug 23, 2018 (Senior Program Committee)
Portland, OR, Jul 9, 2018 - Jul 13, 2018 (Minisymposium Organizer, Minisymposium Speaker)
San Diego, CA, May 3, 2018 - May 5, 2018 (Tutorial Presenter)
Bethesda, MD, Jan 30, 2018 - Feb 1, 2018 (Invited Participant)
San Diego, CA, Jan 11, 2018 (SIAM Invited Address, Minisymposium Coorganizer)