I am a Ph.D. student in the Department of Electrical and Computer Engineering at Virginia Tech, where I am advised by Dr. Ming Jin. My research interests lie at the intersection of efficient and robust machine learning, reinforcement learning, and optimization. I am passionate about use-inspired research and solving real-world problems. More specifically, I am interested in how can we achieve trustworthy reinforcement learning algorithms that are safe, robust, explainable, and can continually adapt to non-stationarity in the real world.
Before coming to Virginia Tech in 2019, I received my Bachelor’s degree in Electrical and Electronics Engineering from Delhi Technological University, India. I received my Master’s in Electrical Engineering in May 2021, where I was advised by Dr. Azim Eskandarian.
- January 2023: Our paper on Meta-safe reinforcement learning is accepted in ICLR 2023 conference as a spotlight.
- November 2022: Our two papers winning the CityLearn challenge 2021, and learning theoretic properties of solution functions of optimization are accepted at AAAI 2023.
- October 2022: Gave a talk by invitation on my research at the undergraduate engineering research seminar on trustworthy reinforcement learning.
- April 2022: Presented our winning solution in the CityLearn challenge 2021 at the PEC Conference at Virginia Tech.
- February 2022: Journal paper accepted at the SAE International Journal of Connected and Autonomous Vehicles.
- November 2021: Presented our theoretical results for the winning solution in the CityLearn challenge 2021 at Southeast Control Coference.