Introduction

Machine learning and AI are rapidly emerging as transformative tools across various fields of research, including mathematics. Recent notable advancements in this area include data-driven approaches in knot theory and representation theory, the discovery of the murmuration phenomenon in arithmetic, the construction of new singular solutions to the Euler equations, and counterexamples and lower bounds in graph theory.
This week-long workshop will showcase recent developments in applying machine learning to mathematics while providing participants with hands-on tutorials and problem-solving sessions. The program will emphasize practical examples and techniques, tailored specifically for mathematics researchers eager to explore or deepen their use of AI. Talks will focus on accessible problems where machine learning methods can be effectively applied, offering participants actionable insights and skills to advance their research with AI. To attend the workshop, please register. 

Speakers

Jeremy Avigad (Carnegie Mellon University)

Gergely Berczi (Aarhus University)

Tristan Buckmaster (Courant Institute, New York University)

François Charton (Meta)

Fabian Glöckle (Meta / Ecole des Ponts)

Sergei Gukov (Caltech)

Baran Hashemi (Technical University of Munich)

Koji Hashimoto (Kyoto University)

Youngmi Hur (Yonsei University)

Ashvni Narayanan (SMRI, University of Sydney)

Thomas Oliver (University of Westminster)

Alexey Pozdnyakov (Princeton University)

Radmila Sazdanovic (North Carolina State University)

Rak-Kyeong Seong (UNIST)

Tomás Silva (University of Campinas)

Akinori Tanaka (RIKEN Center for Advanced Intelligence Project)

Yutong Wang (National University of Singapore)

Zaiwen Wen (BICMR, Peking University)

Geordie Williamson (University of Sydney)

Organizing Committee

Bin Dong (BICMR, Peking University)

Michael Douglas (CMSA, Harvard University)

Kyu-Hwan Lee (University of Connecticut / KIAS)

Program Committee

Amaury Hayat (CERMICS - Ecole des Ponts)

Yang-Hui He (London Institute for Mathematical Sciences)