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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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I am excitied to announce that the National Academies of Sciences, Engineering, and Medicine has convened an ad hoc committee of experts to prepare narratives and graphics to identify and illustrate the impact of the mathematical sciences, and I am chairing the committee.

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At SIAM CSE19, I presented a talk on a DOE Lab Perspective on CSE Education which included specific recommendations for graduate education. Several persons asked for the slides, which are available here.

As a preview, the recommendations are…

  • Make side projects integral to the program
  • Explicitly develop communication skills
  • Require major programming project
  • Encourage leadership and teaming
  • Develop “soft skills” curriculum

(CSE = Computational Science & Engineering)

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Travel & Talks

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Recent Publications

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Generalized Canonical Polyadic Tensor Decomposition

SIAM Review, 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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Estimating Higher-Order Moments Using Symmetric Tensor Decomposition

arXiv, 2019

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Faster Johnson-Lindenstrauss Transforms via Kronecker Products

arXiv, 2019

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Software for Sparse Tensor Decomposition on Emerging Computing Architectures

SIAM Journal on Scientific Computing, 2019

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Stochastic Gradients for Large-Scale Tensor Decomposition

arXiv, 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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XPCA: Extending PCA for a Combination of Discrete and Continuous Variables

arXiv, 2018

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A Practical Randomized CP Tensor Decomposition

SIAM Journal on Matrix Analysis and Applications, 2018

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Unsupervised Discovery of Demixed, Low-dimensional Neural Dynamics across Multiple Timescales through Tensor Components Analysis

Neuron, 2018

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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

Contact