Research InterestsMachine and Reinforcement Learning, Robust and Distributed Optimal Control, Convex Optimization, CyberPhysical Systems, Software Defined Networking Research OverviewMy work aims to develop foundational theory and computational tools, rooted in machine learning, optimization, and control for the design of largescale datadriven cyberphysical systems (DDCPS) such as the smartgrid, softwaredefined networks and smart transportation systems. For such a theory to be successful, it must provide the nonasymptotic guarantees of contemporary highdimensional statistics, the stability, safety and performance guarantees of robust control theory, and the ability to learn and adapt of datadriven systems, all while being applicable to safetycritical systems that are highly dynamic and interconnected, difficult to model, ever changing and of huge scale. The goal of my research is to develop such a theoretical framework and the corresponding computational tools needed to make these insights actionable. Please see my publications page for current research projects, and the talk below for an accessible introduction (aimed at a general audience) to some of the ideas behind my work. Recent Travel and Updates
