University of Michigan · Fall 2026
AI for Physics Seminar
A seminar series at the University of Michigan on machine learning and artificial intelligence for physics (e.g. astronomy, cosmology, high-energy, applied physics).
About
AI is reshaping how physics is done: from model building, data analysis, parameter inference, and even the processes by which knowledge is disseminated and passed-down. This seminar seeks to bring together researchers across physics (broadly defined) to discuss AI methodologies that are moving science forward, and the opportunities and challenges they bring.
Talks are aimed at a broad physics audience, with a solid Q&A afterward moderated by the organizers. We graciously acknowledge support from the Leinweber Institute for Theoretical Physics (LITP).
Schedule (Fall 2026)
| Date | Speaker | Title |
|---|---|---|
| Sep 2 | Nick Kern, Yueying Ni, Ming Feng Ho University of Michigan | AI for physics key concepts; intro to generative modeling; intro to AI coding workflows |
| Sep 16 | Bin Xia Georgia Institute of Technology | TBD (foundation models in cosmology) |
| Sep 30 | Ricardo Vinuesa University of Michigan | From explainable deep learning to foundation models: discovery and control |
| Oct 14 | TBD | TBD |
| Oct 28 | TBD | TBD |
| Nov 11 | TBD | TBD |
| Nov 18 | TBD | TBD |
| Dec 2 | Francisco Villaescusa-Navarro Center for Computational Astrophysics, Flatiron Institute | TBD |
| Dec 16 | Yuan-Sen Ting Dept. of Astronomy, The Ohio State University | TBD |