Center for Optimization and Learning

Education

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.

MINI-COURSE

NEW

Led by Distinguished Visiting Professor, Tamara Kolda

Introduction to Tensor Decompositions

The class is targeted to graduate and advanced undergraduate students. Space permitting, faculty and postdocs are also welcome.

Description:

Tensors are multidimensional arrays, and many datasets are most naturally represented in this framework. This mini-course will introduce attendees to tensors decompositions, whose applications range from analysis of fluorescence data in chemistry, to brain imaging in neuroscience, to data compression of combustion simulations, to Internet traffic analysis in cybersecurity, and much more.  The goals of this course are as follows:

  • Introduction to tensors – relationship to matrices, basic manipulations and operations
  • Tucker decomposition – utility for compression, key kernels, computational algorithms, approximation theory, applications
  • CP decomposition – utility for data analysis, key kernels, computation algorithms, choosing the rank, applications
  • Special topics – Depending on time and the interest of the students, special topics could include working with large scale and/or sparse tensors, alternative loss functions, decompositions of symmetric tensors, randomized methods, applications to moment tensors and Gaussian Mixture Models, etc.

Students will gain experience analyzing real data sets (in MATLAB).

Students should ideally have had a class in linear algebra or numerical analysis.

EVERY TUESDAY and THURSDAY, MAY 3 - MAY 17, 2022

10am - 11am

TECH C211

This is a combined CS and IEMS zero-credit course, and is in CAESAR as follows:

COMP_SCI 496: 40651

IEMS 490: 40652

REGISTER HERE

DEADLINE: Monday, APRIL 25

Contact Dr. Kolda at tamara.kolda@northwestern.edu for questions, topic requests, and space availability

Dr. 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, MathSci.ai. You can read more about Dr. Kolda and her career at https://www.MathSci.ai.

Optimization

Statistics

Machine Learning

Other