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.
Nov. 2025: Invited talk at the University of Cambridge in the
Department of Engineering.
Nov. 2025: Invited talk at Morgan Stanley.
Oct. 2025: Serving as an Area Chair for ICLR 2026.
Oct. 2025: Received top reviewer award for NeurIPS 2025.
Jul. 2025: ICML 2025 Outstanding Paper Award for "Conformal Prediction as Bayesian Quadrature."
Apr. 2025: Invited talk at UC San Diego in the Department of Computer
Science & Engineering.
Apr. 2025: Our Nature Human Behaviour paper is now available.
Mar. 2025: Invited talks at Rutgers University, Texas A&M University,
and University of Arizona.
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.
Conformal Prediction as Bayesian Quadrature
Jake C. Snell, Thomas L. Griffiths
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
