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Tamara G. Kolda , Sandia National Laboratories, Tensor Decomposition: A Mathematical Tool for Data Analysis

October 29, 2018 | 6:00 pm - 7:00 pm EDT

Tensors are multiway arrays, and these occur naturally in many data analysis. Consider a series of experiments tracking multiple sensors over time, resulting in a three-way tensor of the form experiment-by-sensor-by-time. Tensor decompositions are powerful tools for data analysis that can be used for data interpretation, dimensionality reduction, outlier detection, and estimation of missing data. In this talk, we consider the mathematical, algorithmic, and computational challenges of tensor methods and highlight their wide ranging utility with examples in neuroscience, chemical detection, social network analysis, and more. We discuss several new developments, including a new “generalized” version of tensor decomposition that allows for alternative statistically-motivated fitting functions.
BIO:

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 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 is currently a member of the SIAM Board of Trustees and is the editor-in-chief of the new SIAM J. Mathematics of Data Science.

Details

Date:
October 29, 2018
Time:
6:00 pm - 7:00 pm EDT
Event Category:

Venue

SAS 2203