ML@FI Seminar Series

 
ML@FI is a seminar series focused on machine learning and its applications to science. It is aimed at Flatiron Institute research scientists and our collaborators. Seminars usually take place on every other Tuesday at 3:00 p.m. Each seminar is followed by a reception to encourage intercenter interactions.

For more information, to join the seminar mailing list or to propose speakers for future seminars, please contact the organizers: Shirley Ho, Alberto Bietti, and Francois Lanusse.

2025 Schedule

DateSpeakerTitle
January 28, 2025Ziming LiuTowards Unification of Artificial Intelligence and Science
February 18, 2025Mahdi Soltanolkotabi Towards More Reliable Generative AI: Probing Failure Modes, Harnessing Test-Time Inference, and Interpreting Diffusion Models
February 25, 2025Johannes Brandstetter Closing the Gap Between Scientific Foundation Models and Real-World Applications
March 13, 2025Maximilian NickelEpistemic Limits of Model Validation in Complex Social Systems
April 1, 2025Clément HonglerArrows of Time for Large Language Models
April 8, 2025Danqui ChenOptimizing Data Use for Pre-training Language Models
April 15, 2025Pavlos ProtopapasNew Frontiers in Cosmology with Physics-Informed Neural Networks
April 29, 2025Akari AsaiBeyond Scaling: Frontiers of Retrieval-Augmented Language Models
June 3, 2025Andrew StuartLearning Memory and Material Dependent Constitutive Laws
June 17, 2025Akshay Krishnamurthy Understanding Inference Time Compute: Self-Improvement and Scaling
September 16, 2025Anastasis GermanidisVideo Diffusion Models for Storytelling and World Modeling
September 30, 2025Brandon AmosOn meta prompt optimization and coding agents
October 14, 2025Dimitris Tsementzis(Univalent) Foundations of Mathematics, AI, and Time Series (in Finance)
October 28, 2025Romain LopezModeling Complex System Dynamics with Flow Matching Across Time and Conditions

Past Series

Advancing Research in Basic Science and MathematicsSubscribe to Flatiron Institute announcements and other foundation updates

privacy consent banner

Privacy preference

We use cookies to provide you with the best online experience. By clicking "Accept All," you help us understand how our site is used and enhance its performance. You can change your choice at any time here. To learn more, please visit our Privacy Policy.