Paper 'Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation' accepted to Uncertainty in Artificial Intelligence 2025 conference; Passed both Theory and Data Analysis qualification exams of the Statistical Science PhD program.
Research Experience
No specific work experience or research projects mentioned.
Education
PhD candidate in Statistical Science at Indiana University; Completed the Computer Science PhD minor requirement.
Background
Research interests include Machine Learning and Sports Analytics. Recent focuses are on Generative Artificial Intelligence Modeling (such as Diffusion and Flow-based Models) and Reinforcement Learning, specifically Continuous-time and Multi-task RL.
Miscellany
Contact: IU email and LinkedIn; Website powered by Jekyll with al-folio theme. Hosted by GitHub Pages.