About me

I am a Ph.D. candidate in Electrical and Computer Engineering at Vanderbilt University. My research focuses on sequential decision-making under uncertainty over long time horizons. I apply this framework to the critical intersection of transportation and energy systems. The core scientific problem is making an optimal decision now, such as when an Electric Vehicle (EV) should charge or discharge back to a building, while facing deep uncertainty about future energy prices, grid demand, and driver behavior.

To solve this, I integrate methods from reinforcement learning and optimization theory. Crucially, my work incorporates a behavioral dimension by designing negotiation mechanisms that are embedded within the sequential decision-making framework, allowing the system to reconcile the individual goals of drivers with the collective needs of the energy system. The ultimate goal is to create robust coordination policies for vehicle-to-building (V2B) systems that can navigate these long-future uncertainties and operate efficiently over extended time horizons.

I have authored multiple research papers, including state-of-the-art optimization models for V2B and EV integration, with recognition at conferences such as the International Conference on Cyber-Physical Systems (ICCPS) and the International Conference on Autonomous Agents and Multiagent Systems (AAMAS). My recent work includes online decision-making algorithms and discrete-event simulations for real-world electric mobility challenges.

In 2025, I was selected as a member of the OpenMinds NextGen Leaders cohort, joining a network of researchers and professionals working to solve the Dual Challenge: enabling more energy with fewer emissions.