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.
For more information, to join the seminar mailing list or to propose speakers for future seminars, please contact the organizers: Shirley Ho, Alberto Bietti, Carolina Cuesta-Lazaro.
2026 Schedule
| Date | Speaker | Title |
| January 6, 2026 | Diana Cai | From intuition to innovation: accelerating scientific discovery through black-box probabilistic inference |
| January 20, 2026 | Petros Koumoutsakos | AI and Scientific Computing: Algorithmic Alloys for Forecasting and Control of Complex Systems |
| February 10, 2026 | Joan Bruno | Beyond Generative Modeling with Measure Dynamics |
| February 17, 2026 | Karen Ullrich | Ensuring Reliability and Understanding Theoretical Limits of Foundation Models |
| March 3, 2026 | Michael Albergo | Frontiers of dynamical control of generative models |
| March 24, 2026 | David Chalmers | What We Talk to When We Talk to Language Models |
| March 31, 2026 | Andrew Gordon Wilson | From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence |
| April 7, 2026 | Steve Brunton | Machine Learning for Scientific Discovery, with Examples in Fluid Mechanics |
| April 14, 2026 | Jeremias Sulam | Beyond scores: proximal diffusion models |
| April 28, 2026 | Siddharth Mishra-Sharma | Riding the exponential: Some reflections on using AI for research |
Past Series
2025 Machine Learning at the Flatiron Institute Seminar Series
2024 Machine Learning at the Flatiron Institute Seminar Series
2023 Machine Learning at the Flatiron Institute Seminar Series
2022 Machine Learning at the Flatiron Institute Seminar Series
2021 Machine Learning at the Flatiron Institute Seminar Series
2020 Machine Learning at the Flatiron Institute Seminar Series
2019 Machine Learning at the Flatiron Institute Seminar Series