Projects

Active Learning in RL with Human Feedback
  • Develop frameworks to incorporate human feedback into reinforcement learning to refine reward struc- tures, ensuring agent behaviors align with domain-specific quality and safety standards.
  • Advance research on the critical role of human feedback in establishing action constraints for safety and fairness, aiming to improve RL algorithm adaptability in real-world applications.
Statistical Inference for Contextual Bandit with Delayed Action Effects
  • Investigate the delayed effects of actions in critical settings, such as healthcare, where the impact of medications emerges over time and is linked to past decisions.
  • Conduct statistical inference on delayed feedback in contextual bandit frameworks, aiming to rigorously evaluate drug effects and optimize decision-making in healthcare applications.