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.

For the latest updates on my research, please visit my research profile at Mitsubishi Electric Research Laboratories (MERL).

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:


  • Constrained control of dynamical systems
  • Reinforcement learning
  • Optimization
  • Verification and controller synthesis


  • 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.

Convexified contextual optimization for on-the-fly control of smooth systems. Proceedings of the American Control Conference (ACC), 2020.

SReachTools: a MATLAB stochastic reachability toolbox. Proceedings of the ACM International Conference on Hybrid Systems: Computation and Control (HSCC), 2019.

Probabilistic Occupancy Function and Sets Using Forward Stochastic Reachability for Rigid-Body Dynamic Obstacles. arXiv preprint arXiv:1803.07180, 2018.

Scalable Underapproximative Verification of Stochastic LTI Systems using Convexity and Compactness. Proceedings of the ACM International Conference on Hybrid Systems: Computation and Control (HSCC), 2018.

Stochastic reachability of a target tube: Theory and computation. arXiv preprint arXiv:1810.05217, 2018.

Forward stochastic reachability analysis for uncontrolled linear systems using Fourier Transforms. Proceedings of the ACM International Conference on Hybrid Systems: Computation and Control (HSCC), 2017.


Safety under uncertainty and constraints

Providing guarantees of safety and synthesis of admissible controllers for stochastic dynamical systems using optimization, Fourier transforms, and computational geometry

Probabilistic occupancy via forward stochastic reachability

A grid-free, recursion-free, and sampling-free estimation of events with convexity guarantees