I am an associate research scholar in the Dept. of Computer Science at Princeton University, where I work with Tom Griffiths. I earned my Ph.D. in computer science at U of Toronto, where my advisor was Richard Zemel. Previously, I completed my bachelor's degree in biomedical engineering at Yale.
See my CV and publications.
I work on building trustworthy deep learning algorithms through the perspective of probabilistic modeling. My current research interests include:
1. Robustness. Designing learning algorithms that are robust to new environments and changes over time, with a particular focus on meta-learning and Bayesian filtering.
2. Reliability. Quantifying the reliability of black box models, with an emphasis on distribution-free and nonparametric methods.
3. Transparency. Developing Bayesian inference algorithms to better understand representations and behavior of AI models.
May 2025: One paper accepted at ICML 2025 (oral presentation, top 1% of submissions).
Apr. 2025: Our Nature Human Behaviour paper is now available.
Jan. 2025: One paper accepted to Nature Human Behaviour.
Sep. 2024: One paper accepted at NeurIPS 2024.
Jul. 2024: One paper accepted at TMLR.
Feb. 2024: Invited talk at Stanford University in the Department of Statistics.
Jan. 2024: Two papers accepted at ICLR 2024.
Sep. 2023: Two papers accepted at NeurIPS 2023.
Current Mentees
Gianluca Bencomo,
CS Ph.D. student at Princeton.
Yan (Roger) Weng, CS Undergrad at Princeton.
Liyi Zhang, CS Ph.D.
student at Princeton.
Former Mentees
Bhishma Dedhia, ECE
Ph.D. student at Princeton.
Grace Liu, CS Master's at
Princeton. Now a Ph.D. student at Carnegie Mellon.
Feng (Shelley) Xia, CS Master's at Princeton. Now a machine learning engineer at
TikTok.
Tom Zollo, CS Master's at
Columbia. Now a Ph.D. student, also at Columbia.
A Metalearned Neural Circuit for Nonparametric Bayesian Inference
Jake C. Snell, Gianluca Bencomo, Thomas L. Griffiths
Prototypical Networks for Few-shot Learning
Jake C. Snell, Kevin Swersky, Richard Zemel
