2025: 'KL-Regularized Reinforcement Learning is Designed to Mode Collapse', NeurIPS Workshop on Foundations of Reasoning in Language Models (accepted)
2025: 'Language Agents Mirror Human Causal Reasoning Biases. How Can We Help Them Think Like Scientists?', Conference on Language Modelling (COLM)
2025: 'Efficient Exploration and Discriminative World Model Learning with an Object-Centric Abstraction', ICLR
2024: 'Testing Causal Hypotheses through Hierarchical Reinforcement Learning', NeurIPS Workshop on Intrinsically Motivated Open-ended Learning
2024: 'Light-weight probing of unsupervised representations for reinforcement learning', Reinforcement Learning Conference (RLC), co-authored with Yann LeCun et al.
Background
5th-year Ph.D. candidate at NYU's CILVR Lab and Center for Data Science
Research focuses on understanding the reinforcement learning (RL) framework and developing better RL algorithms
Key questions: efficient exploration and autonomous world modeling, scalable RL with minimal tricks, leveraging foundation models to discover unknowns
Master's thesis introduced new value function decomposition methods in RL, linked to hippocampal neuroscience theories
Undergraduate collaborations with researchers in psychiatric genomics, computational neuroscience, and theoretical neuroscience