Tuesday, October 27, 2026
3:30 PM – 5:00 PM CDT
A Riemannian Geometry Perspective on Foundation Models
About this event
A UT Austin Oden Institute seminar with Yale’s Rex Ying on geometric deep learning, examining how non-Euclidean structures can inform scalable foundation models, transformers, and multimodal systems.
Foundation models beyond Euclidean geometry
Yale computer scientist Rex Ying will discuss why the geometry used in machine learning matters as models scale. The seminar considers real-world data with relationships, hierarchies, and other non-Euclidean structure, then connects those ideas to foundation models, transformers, multimodal systems, and practical applications such as recommender systems.
The Oden Institute lists this as an in-person seminar in POB 6.304 with a Zoom option. It is intended for researchers, students, and technical practitioners interested in machine learning theory and representation learning. The official Oden event page gives the definitive date and time after a separate calendar listing showed an inconsistent date. Use the event link to join remotely or confirm room details before attending.
Organized by
A University of Texas at Austin research institute advancing computational science, engineering, and mathematics through research and public academic events.