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 Joint Mathematics Meeting (JMM), 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 Journal on Scientific Computing (SISC) and SIAM Journal on Matrix Analysis and Applications (SIMAX). She is also the founding Editor-in-Chief for the new SIAM Journal on the Mathematics of Data Science (SIMODS).
Recent Headshot: 4MB
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)
I recreated the page from a standard engineering computation pad for use with electronic note-taking systems.
SIAM has launched a new journal on the Mathematics of Data Science, and it is now taking submissions.
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.
Bern, Switzerland, Jul 9, 2019 - Jul 13, 2019 (Invited Speaker)
Center Paul-Langevin, France, Jun 17, 2019 - Jun 28, 2019 (Lecturer)
Bellevue, WA, May 29, 2019 - Jun 1, 2019 (Keynote Speaker)
Purdue University, West Layfayette, IN, Apr 11, 2019 - Apr 13, 2019 (Keynote Speaker)
Spokane, Washington, Feb 25, 2019 - Mar 1, 2019 (Organizing Committee, Panel Organizer, Minisymposium Speaker)
North Carolina State University, Rayleigh, NC, Oct 29, 2018 (Colloquium Talk)
Arlington, VA, Oct 15, 2018 - Oct 17, 2018 (Attendee)