Presenter Bio:
Mohit Singh is a Coca-Cola Foundation Professor at the H. Milton Stewart School of Industrial & Systems Engineering (ISyE), Georgia Institute of Technology. His research interests include discrete optimization, approximation algorithms, and convex optimization. His research is focused on optimization problems arising in cloud computing, logistics, network design, and machine learning. Previously, he has worked at Microsoft Research and McGill University and received his PhD in Algorithms, Combinatorics, and Optimization (ACO) program from Tepper School of Business, Carnegie Mellon University in 2008.

Abstract:
Representing data via vectors and matrices and optimizing spectral objectives such as determinants, and traces of naturally associated matrices is a standard paradigm that is utilized in multiple areas including machine learning, statistics, convex geometry, location problems, allocation problems, and network design problems. In this talk, we will look at many of these applications with a focus on the determinant objective. We will then give algorithms for these problems that build on classical matroid intersection algorithms.