Center for Optimization and Learning


Combining coursework from a wide range of disciplines, the Center for Optimization and Statistical Learning provides an interdisciplinary approach to focus on opportunities at the intersection of optimization and machine and statistical learning.



Led by Distinguished Visiting Professor, Tamara Kolda

Randomized Alogorithms in Linear Algebra and Scientific Computing

The class is targeted to graduate and advanced undergraduate students, as well as faculty and postdocs.

  • Overview: Success Stories for Randomized Methods; Review of Statistics and Probability, including union bounds, non-asymptotic statistics
  • Review of Matrix Factorization and related concepts; Stochastic Rounding and Applications
  • Randomized Range Finder and Applications
  • More Randomization in Matrix Factorization
  • Johnson-Lindenstrauss Transforms (JLT) and Structured Variants; Randomized Least Squares
  • Randomization Applications in Optimization

EVERY Monday and Wednesday, MAY 1 - MAY 17, 2023

9:30am - 10:50am


This is a zero-credit course

Register via CAESAR

COMP_SCI 39957

IEMS 490: 39979

Contact Dr. Kolda at for questions and topic requests

Tamara KoldaDr. Kolda is an expert on tensor methods, data science, and optimization, and was elected in 2020 to the National Academy of Engineering. Her 2020 NAE citation reads, "For contributions to the design of scientic software, including tensor decompositions and multilinear algebra."  As Distinguished Visiting Professor in the IEMS Department, Dr. Kolda is currently collaborating with Prof. Matt Plumlee on tensor methods in statistics, and with Prof. Jorge Nocedal on randomized optimization methods.  After more than two decades at Sandia National Laboratories, Dr. Kolda has recently transitioned to independent consultant under her business, You can read more about Dr. Kolda and her career at




Machine Learning


  • Modeling with Data (ESAM 495)
  • Introduction to Tensor Decompositions mini-course (May 2022, led by Distinguished Visiting Professor Tamara Kolda