Welcome to the website for the Princeton Machine Learning Theory Summer School. The school will run in person August 12 - August 21, 2025 at Princeton and is aimed at PhD students interested in machine learning theory. The primary goal is to showcase, through four main courses, a range of exciting recent developments in the subject. The primary focus this year is on theoretical advances in deep learning. An important secondary goal is to connect young researchers and foster community within theoretical machine learning.

Principle courses, each consisting of four to five hours of lecture, are given by (Titles TBA):

1. Florent Krzakala (EPFL)
2. Yury Polyanskiy (MIT)
3. Theodor Misiakiewicz (Yale)
4. Yue Lu (Harvard)
5. Jianfeng Lu (Duke) 

PhD students in any technical discipline with a strong interest in theory are encouraged to apply. Accepted participants will be given free accommodation (double occupancy) in Princeton. Applications can be submitted here are due March 31, 2025 and require a CV, a letter of recommendation and a statement of purpose.

This summer school is organized by Boris Hanin (Princeton ORFE). Support was provided by the Department of Operations Research and Financial Engineering (ORFE) at Princeton, the Princeton School of Engineering and Applied Sciences (SEAS), the Program on Applied and Computational Mathematics (PACM) at Princeton, Princeton Language + Intelligence (PLI), Princeton AI Lab (PAIL), Princeton AI for Accelerating Innovation (AI2), Princeton Natural and Artificial Minds (NAM), and Princeton Data-Driven Social Science (DDSS).