My research focuses on sequential decision-making under uncertainty, particularly in the context of human feedback. I have extensively studied distributional reinforcement learning (DistRL), reinforcement learning from human feedback (RLHF), and regret analysis, aiming to bridge theory and practice.

My long-term goal is to build human-aligned, socially aware agents — systems that reason about people, navigate the dynamics of human interaction, and act on their preferences, values, and decisions. I draw inspiration from how humans make decisions. By seeking to understand the cognitive mechanisms underlying human choice and mathematically modeling the structure that governs interaction, I aim to develop both theoretical insights and practical algorithms for robust decision-making under uncertainty.