Andrew Gordon Wilson


We are a research group at New York University, wishing to understand the foundations of generalization, learning, and decision making, towards building highly practical new methods in machine learning. Our work covers deep learning models, including LLMs, uncertainty representation, AI alignment, distribution shifts, physics inspired ML, equivariance modelling, numerical methods, and scientific discovery. We believe in open and reproducible research. If you'd like to try out these methods check out our code page. See also our info about joining, which describes research interests and some example papers in greater detail.

Group Leader
Andrew Gordon Wilson

Postdoctoral Fellows
Micah Goldblum

PhD Students
Marc Finzi
Nate Gruver
Sanyam Kapoor
Polina Kirichenko
Yucen (Lily) Li
Sanae Lotfi
Andres Potapczynski
Shikai Qiu

Ben Athiwaratkun (Scientist at Together AI)
Ian Delbridge (Senior Research Scientist at Klaviyo)
Marc Finzi (Postdoc at CMU)
Jacob Gardner (Assistant Professor at the University of Pennsylvania)
Pavel Izmailov (Scientist at OpenAI, and incoming NYU Assistant Professor)
Wesley Maddox (Quantitative Researcher at Jump Trading)
Geoff Pleiss (Assistant Professor at the University of British Columbia)
Samuel Stanton (Research Scientist at GenenTech)
Ke Alexander Wang (PhD Student at Stanford)
Ruqi Zhang (Assistant Professor at the University of Purdue)