Math AI Seminar
Math AI Seminar
The Math AI Seminar is a research seminar on computer-assisted mathematics with topics ranging from mathematical formalization, the integration of machine learning tactics in mathematical formalization, the mathematical theory of machine learning and artificial intelligence, and applying machine learning techniques in pure mathematical research.
Upcoming Events
Past Events
- Geometry and Expressivity of Neuromanifolds: Where Algebraic Geometry Meets Neural Networks (Maksym Zubkov) -
- Machine Learning for Accelerating Mathematical Discovery in Algebraic Combinatorics
(Henry Kvinge, PNNL & UW) - - Proving and Improving: Language Models for Theorem Proving and Proof Shortening in Lean (Alex Gu) -
- Lean Together (Vasily Ilin) -
- CayleyPy - Artificial intelligence methods for group and graph theories (Alexander Chervov, Institut Curie) -
- Seed-Prover: Deep and Broad Reasoning for Automated Theorem Proving (Vasily Ilin) -
- AI Meets Mathematics: A Survey of Recent Breakthroughs and Emerging Directions (Carina Hong (Stanford Math / Stanford Law / Axiom AI)) -
- Formalizing Engineering Mathematics with Lean (Eric Klavins, Proffessor at ECE, UW) -
- Lower bounds on neural networks and slicing the hypercube (Gal Yehuda, Yale University) -
- Math AI Lab Winter 2025 Projects (Math AI Lab) -
- Diffusion Generative Modeling: Making Pictures from Noise with Math (Vasily Ilin) -
- Formalizing mathematics in the eXperimental Lean Lab (Experimental Lean Lab) -
- Generating Functions in Lean (Herman Chau, University of Washington) -