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‘Magical’ mathematics unlocks engineering honor for Sandia scientist https://share-ng.sandia.gov/news/resources/news_releases/magical_mathematics/ LIVERMORE, Calif. - Tamara Kolda of Sandia National Laboratories has spent a career finding mathematical patterns in data sets ranging from mouse neurons to crime statistics, so when she talks about how ‘magical’ the results seem, even experts in other fields take notice. Kolda was one of 87 members elected to the National Academy of Engineering this year - one of a handful of mathematicians ever granted membership.

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We’re all in a lot of virtual meetings these days thanks to COVID-19. As we continue to shelter at home, I’ve been trying to optimize my online meeting experience. In this post, I share what I consider to be best practices.

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Humbled to announce that I’m one of the 87 new members of the National Academy of Engineering.

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Just in time for the 2019-2020 interview season, I’m resurfacing posts with interview advice including asking good questions during interviews and preparing interview talks.

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Happy to report that I’ve been named a 2019 ACM Fellow for innovations in algorithms for tensor decompositions, contributions to data science, and community leadership.

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Talks & Travel

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Recent Publications

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Stochastic Gradients for Large-Scale Tensor Decomposition

SIAM Journal on Mathematics of Data Science, 2020

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Faster Johnson-Lindenstrauss Transforms via Kronecker Products

Information and Inference: A Journal of the IMA, 2020

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Mathematics: The Tao of Data Science

Harvard Data Science Review, 2020

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Estimating Higher-Order Moments Using Symmetric Tensor Decomposition

SIAM Journal on Matrix Analysis and Applications, 2020

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Practical Leverage-Based Sampling for Low-Rank Tensor Decomposition

arXiv, 2020

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Estimating Higher-Order Moments Using Symmetric Tensor Decomposition

SIAM Journal on Matrix Analysis and Applications, 2020

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TuckerMPI: A Parallel C++/MPI Software Package for Large-scale Data Compression via the Tucker Tensor Decomposition

ACM Transactions on Mathematical Software, 2020

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Generalized Canonical Polyadic Tensor Decomposition

SIAM Review, 2020

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Software for Sparse Tensor Decomposition on Emerging Computing Architectures

SIAM Journal on Scientific Computing, 2019

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Introduction to SIAM Journal on Mathematics of Data Science (SIMODS)

SIAM Journal on Mathematics of Data Science, 2019

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Selected Older Publications

Tensor Decompositions and Applications

SIAM Review, 2009

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Algorithm 862: MATLAB Tensor Classes for Fast Algorithm Prototyping

ACM Transactions on Mathematical Software, 2006

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An Overview of the Trilinos Project

ACM Transactions on Mathematical Software, 2005

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Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods

SIAM Review, 2003

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Orthogonal Tensor Decompositions

SIAM Journal on Matrix Analysis and Applications, 2001

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Graph Partitioning Models for Parallel Computing

Parallel Computing, 2000

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A Semidiscrete Matrix Decomposition for Latent Semantic Indexing Information Retrieval

ACM Transactions on Information Systems, 1998

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Software

  • FEASTPACK - MATLAB and C codes for graph analysis, including BTER code for graph generation and our wedge sampling technique for triangle counting
  • Poblano Toolbox for MATLAB - Large-scale algorithms for unconstrained nonlinear optimization
  • HOPSPACK - A C++ hybrid optimization parallel search package for derivative-free optimization
  • MET - Memory-efficient Tucker computation (requires Tensor Toolbox)
  • Tensor Toolbox for MATLAB - Higher-order operations of multidimensional arrays
  • NOX - A C++ Nonlinear Solver Package
  • Trilinos - A suite of high-performance numerical codes (including NOX)
  • APPSPACK - A C++ derivative-free optimization package
  • SDDPACK - C and MATLAB code for the semi-discrete matrix decomposition

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