I design reinforcement learning (RL) algorithms: AI methods that learn how to make intelligent decisions from trial and error. I am especially interested in self-supervised methods, which enable agents to learn intelligent behaviors without labels or human supervision. Our group has developed some of the foremost algorithms and analysis for such self-supervised RL methods. Hereare a few example papers; here and here are some tutorials to learn more about our research. My work has been recognized by an NSF CAREER Award, a Hertz Fellowship, an NSF GRFP Fellowship, and the Alfred Rheinstein Faculty Award. I run the Princeton Reinforcement Learning Lab.
Before joining Princeton, I did by PhD in machine learning at CMU under Ruslan Salakhutdinov and Sergey Levine and supported by the NSF GFRP and the Hertz Fellowship. I spent a number of years at Google Brain/Research before and during my PhD. My undergraduate studies were in math at MIT.
Join us! I am not hiring PhD students in Fall 2025. I am hiring a postdoc. Please read this page before emailing me about joining the lab.
news
Dec 1, 2025
Princeton RL @ NeurIPS 2025! I’m excited to share progress we’ve made in RL algorithms (and the many problems still unsolved):
Demystifying emergent exploration in goal-conditioned RL. Led by Mahsa Bastankhah and Grace Liu, with Dilip Arumugam and Thomas L. Griffiths. (Aligning Reinforcement Learning Experimentalists and Theorists; Interpreting Cognition in Deep Learning Models)
Combinatorial Representations for Temporal Reasoning. Led by Alicja Ziarko, with Michał Bortkiewicz, Michał Zawalski, Piotr Miłoś. (Differentiable Learning of Combinatorial Algorithms; Unifying Representations in Neural Models; WiML)
Horizon Reduction Makes Offline RL Scalable. Led by Seohong Park, with Kevin Frans, Deepinder Mann, Aviral Kumar, Sergey Levine. (Aligning Reinforcement Learning Experimentalists and Theorists)
I gave an ICML tutorial on generative AI and reinforcement learning intrinsic motivation and self-supervised RL, together with Amy Zhang. Recording and slides are available on the tutorial website.
Jun 11, 2025
I gave a tutorial on intrinsic motivation and self-supervised RL at RLDM! Recording and slides are available on the tutorial website.
Apr 24, 2025
Princeton RL @ ICLR 2025! Some say hi in Singapore!
Awarded a grant from the Princeton AI Lab to study ``Do brains perceive, act, and plan using temporal contrast?’’ together with Nathaniel Daw.
Aug 9, 2024
In attempts to change perceptions about who does RL, we’ve put together a poster of Notable Women in RL!
selected publications
The aim is to highlight a small subset of the work done in the group, and to give a sense for the sorts of problems that we're working on. Please see Google Scholar for a complete and up-to-date list of publications.
2025
1000 layer networks for self-supervised rl: Scaling depth can enable new goal-reaching capabilities
Kevin Wang, Ishaan Javali, MichaĹ Bortkiewicz, Benjamin Eysenbach, and others
Princeton RL @ NeurIPS 2025! I’m excited to share progress we’ve made in RL algorithms (and the many problems still unsolved): 
Finally, I’m organizing a NeurIPS 2025 workshop, Data on the Brain & Mind, together with Eva Yi Xie, Catherine Ji, Vivek Myers, Archer Wang, Mahsa Bastankhah, Jenelle Feather, Erin Grant, and Richard Gao. On December 7th. 
Honored and excited to receive a GPU grant from NVIDIA for research into Interactive Generative Models that Learn via Discovery, not Data. 
Awarded the NSF CAREER award for Unsupervised and Autonomous Reinforcement Learning of Skills 
I gave an ICML tutorial on generative AI and reinforcement learning intrinsic motivation and self-supervised RL, together with Amy Zhang. Recording and slides are available on the tutorial website. 
I gave a tutorial on intrinsic motivation and self-supervised RL at RLDM! Recording and slides are available on the tutorial website. 
Princeton RL @ ICLR 2025! Some say hi in Singapore! 
A Connection between One-Step RL and Critic Regularization in Reinforcement LearningIn International Conference on Machine Learning, 2023
Probabilistic Reinforcement Learning: Using Data to Define Desired Outcomes, and Inferring How to Get TherePhD Thesis, Carnegie Mellon University, 2023
