ML@Flatiron 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. in the CCN classroom on the fourth floor of 160 Fifth Ave. 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.
| Date | Speaker | Title |
| February 22, 2022 | Alex Williams | Statistical methods to characterize variability and individuality in neural recordings |
| March 15, 2022 | SueYeon Chung | Structure, Function, and Learning in Distributed Neural Networks |
| March 22, 2022 | Domenico Di Sante | Deep Learning the Functional Renormalization Group Flow for Correlated Fermions |
| March 29, 2022 | Miles Cranmer | Interpretable Machine Learning for Science |
| April 5, 2022 | Steven L. Brunton | Machine Learning for Scientific Discovery, with Examples in Fluid Mechanics |
| April 26, 2022 | Jeremy Cohen | Semi-supervised Low-rank Approximation, from variational methods to deep learning |
| May 31, 2022 | Michael Eickenberg | Wavelet methods for cosmological parameter estimation |
| June 8, 2022 | Yann LeCun | Self-Supervised Learning, Energy-based Methods, and World Models |
| August 2, 2022 | Zahra Kadkhodaie | Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser |
| October 4, 2022 | Mariel Patee | Interdisciplinary Machine Learning for Fundamental Physics (and Art) |
| October 18, 2022 | Johann Brehmer | Learning causal representations through weak supervision |
| October 25, 2022 | Wenda Zhou | An introduction to higher-order graph neural networks |
| November 1, 2022 | Alan Heavens | Hierarchical Bayesian Models and Simulation-based Inference in Cosmology |
| November 11, 2022 | Joan Bruna | Separations in symmetric and antisymmetric neural ansatze |
| December 6, 2022 | Ben Wandelt | Neural Computation in Bayesian Inference and Applications to Cosmological Data Science |
| December 13, 2022 | Lawrence Saul | A geometrical connection between sparse and low-rank matrices and its uses for machine learning |