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, Siavash Golkar, Anna Dawid or Michael Eickenberg.
2023 Schedule
Date | Speaker | Title |
February 7, 2023 | Chirag Modi | Reconstructing the initial conditions of the Universe |
March 7, 2023 | Jean Ponce | Physical models and machine learning for Photography and Astronomy |
March 14, 2023 | Ching-Yao Lai | Physics-informed neural networks for fluid and ice dynamics |
April 11, 2023 | Laure Zanna | Machine learning for climate modeling |
April 18, 2023 | Tammy Kolda | Generalized tensor decomposition: Utility for data analysis and mathematical challenges |
April 25, 2023 | Dmitri Kochkov | Numerical methods + ML for simulation of turbulent systems |
May 2, 2023 | David Hogg | Is good machine learning bad for science? |
September 19, 2023 | Eero Simoncelli | Photographic Image Priors in the Era of Machine Learning |
October 3, 2023 | Alberto Bietti | Transformers and Associative Memories |
October 17, 2023 | David Pfau | Natural Quantum Monte Carlo Computation of Excited States |
October 31, 2023 | Sebastian J Wetzel | Finding Symmetry Invariants and Conserved Quantities with Artificial Neural Networks |
November 14, 2023 | Eliška Greplová | TBA |