Matus Telgarsky obtained his PhD in Computer Science from UCSD in 2013 under Sanjoy Dasgupta; while there, his research focused primarily upon optimization and statistical aspects of unconstrained and unregularized algorithms (e.g., boosting), and to a lesser extent, clustering. He then served as a postdoctoral researcher at Rutgers University and the University of Michigan, as well as a consulting researcher at Microsoft Research in New York City. Since fall 2016, he has been an assistant professor at the University of Illinois, Urbana-Champaign; his most recent interests are representation and nonconvex optimization.
- Foundations of Machine Learning, Spring 2017. Research Fellow.