The Geometry of Machine Learning 2026
September 8, 2026 @ 9:00 am - September 11, 2026 @ 5:00 pm
The Geometry of Machine Learning 2026
Dates: September 8–11, 2026
Location: Harvard CMSA, Room G10, 20 Garden Street, Cambridge MA 02138
Large language models are presently, and will increasingly, be complemented by other dimensions of intelligence: formal verification and energy-based optimizers, becoming parts of larger ecosystems. Can AIs reason geometrically and can we use geometry to reveal how data is currently processed in NNs? Can AIs reveal the geometry of mathematics, as well as studying geometry as a subject within math. This conference is intended to continue the discussion of these topics.
Confirmed Speakers:
- Nada Amin, Harvard
- Randall Balestriero, Brown
- Michael Brenner, Harvard and Google
- Bennet Chow, UCSD
- Surya Ganguli, Stanford
- Boris Hanin, Princeton
- Roi Holtzman, Oxford
- Dimitry Krotov, IAS
- Slava Krushkal, Virginia
- Mike Mulligan, UCR, Logical Intelligence
- Gabriele Poesia, Stanford
- Mathew Vanherreweghe, Logical Intelligence
- Sean Welleck, CMU (via Zoom)
- Mattiew Wyart, JHU
Organizers: Michael R. Douglas (CMSA) and Mike Freedman (CMSA)
Support provided by Logical Intelligence.
