CISE Seminar: Anand D. Sarwate, The University of New Jersey, Rutgers

Date: March 28th, 2025
Time: 3:00pm – 4:00pm
Location: 665 Commonwealth Ave., CDS 1101

Flexible Tensor Decompositions for Learning and Optimization

Many measurements or signals are multidimensional, or tensor-valued. To fit this data in existing machine learning pipelines, this data is vectorized, causing a blowup in the dimensionality. An alternative approach is to use tensor decompositions to create more structured models that respect the multidimensional structure. In this work we propose a family such structured decompositions, which we call low separation rank (LSR) tensor models. In the talk I will relate these to classical decompositions and show how the LSR model can balance model complexity and performance in supervised and unsupervised learning. Time permitting, we will describe applications of these ideas in other machine learning problems.

Joint work with Batoul Taki, Zahra Shakeri, Mohsen Ghassemi, Xin Li, and Waheed U. Bajwa

Anand D. Sarwate is an Associate Professor in the Electrical and Computer Engineering Department at Rutgers, The State University of New Jersey. He received B.S. degrees in math and electrical engineering from MIT and a Ph.D. in electrical engineering from UC Berkeley. Prior to joining Rutgers he was a Research Assistant Professor at TTI-Chicago and a postdoc at the ITA Center at UC San Diego. His research interests include information theory, machine learning, signal processing, optimization, and privacy and security.

Faculty Host: Bobak Nazer
Student Host: Qian Wu