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 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 we can 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.

Contact: vanshajk@vt.edu

News

• September 2024: Gave a tutorial on Safe RL for Smart Grid Operations and Control at SmartGridComm 2024 conference. (Link)
• July 2024: Our paper “Zero-Day Attack Detection in Digital Substations using In-Context Learning” has been accepted to SmartGridComm 2024!
• June 2024: Started my summer research internship at National Renewable Energy Lab. Working on a graph-reinforcement learning based solution for critical load restoration under uncertain topologies.
• May 2024: Our paper “Algorithm of thoughts: Enhancing exploration of ideas in large language models” has been accepted to ICML 2024! (Paper link)
• March 2024: Co-organizing the Trustworthy Interactive Decision-Making with Foundation Models Workshop at the IJCAI 2024 conference. TIDMwFM workshop
• March 2024: Our workshop paper on the novel prompting strategy “CausalPrompt” for enhancing LLMs in non-language tasks has been accepted at the ICLR 2024 Workshop: Tackling Climate Change with Machine Learning. Paper link. Thanks to my collaborators Wayne Lin and Steven Huang.
• January 2024: One paper on offline reinforcement learning using optimization solution functions accepted in American Control Conference 2024 Paper link.
• December 2023: Attending NeurIPS 2023 in New Orleans!
• August 2023: Our short note on HVAC control for smart buildings using evolutionary RL in collaboration with Mostafa Meimand to appear in BuildSys 2023.
• 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.