Mathematics and Machine Learning Program
Dates: September 3–November 1, 2024
Location: Harvard CMSA, 20 Garden Street, Cambridge, MA 02138
Machine learning and AI are increasingly important tools in all fields of research. Recent milestones in machine learning for mathematics include data-driven discovery of theorems in knot theory and representation theory, the discovery and proof of new singular solutions of the Euler equations, new counterexamples and lower bounds in graph theory, and more. Rigorous numerical methods and interactive theorem proving are playing an important part in obtaining these results. Conversely, much of the spectacular progress in AI has a surprising simplicity at its core. Surely there are remarkable mathematical structures behind this, yet to be elucidated.
The program will begin and end with two week-long workshops, and will feature weekly seminars.
Opening Workshop: September 3–7, 2024
Topics include big data, basics of machine learning, existing mathematical databases, data science techniques, other relevant computational approaches such as verified numerical methods and interactive theorem proving.
Closing Workshop: October 28–Nov 1, 2024
The closing workshop will provide a forum for discussing the most current research in these areas, including work in progress and recent results from program participants. We will devote one day to frontier topics in interactive theorem proving, such as mathematical library development and AI for mathematical search and theorem proving.
Schedule, speakers and topics TBA.
- Francois Charton (Meta AI)
- Michael R. Douglas (Harvard CMSA)
- Michael Freedman (Harvard CMSA)
- Fabian Ruehle (Northeastern)
- Geordie Williamson (Univ. of Sydney)
Further details TBA.