My research dream is to enable autonomy for physical systems — the autonomous agent must provide verifiable guarantees of performance in the presence of uncertainties and limitations on data availability. These agents must also interact with other agents and humans present in the environment effectively. Addressing these challenges with tractable approaches is crucial for safe deployment of autonomy. My research work uses optimization, control theory, and learning to achieve this goal.
Before joining MERL, I was a Postdoctoral Research Fellow, working with Dr. Ufuk Topcu at the University of Texas at Austin, where I investigated data-driven control for autonomy. I received my PhD in Engineering (Systems and Control) at the University of New Mexico, with Dr. Meeko Oishi as my PhD advisor. My PhD work focused on theory and scalable algorithms for probabilistic safety and stochastic optimal control design under constraints. Along with J. Gleason, I have implemented a significant part of my PhD work in SReachTools, which is an open-source, repeatability-tested, MATLAB toolbox. I obtained my B.Tech. and M.Tech degrees in Electrical Engineering from the Indian Institute of Technology, Madras.
My research has received various recognitions:
PhD in Engineering, 2018
The University of New Mexico, Albuquerque, NM, USA
Dual-degree (B. Tech & M.Tech) in Electrical Engineering, 2014
Indian Institute of Technology Madras, Chennai, TN, India
See my Google scholar profile for the complete list.
The following incomplete list shows a sample of my publications.
Repeatability instructions for codes distributed in this blog
A gentle tutorial to the difference of convex optimization — a large class of non-convex optimization problems
Providing guarantees of safety and synthesis of admissible controllers for stochastic dynamical systems using optimization, Fourier transforms, and computational geometry