2022 Machine Learning at the Flatiron Institute Seminar Series

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.

February 22, 2022Alex WilliamsStatistical methods to characterize variability and individuality in neural recordings
March 15, 2022SueYeon ChungStructure, Function, and Learning in Distributed Neural Networks
March 22, 2022Domenico Di SanteDeep Learning the Functional Renormalization Group Flow for Correlated Fermions
March 29, 2022Miles Cranmer Interpretable Machine Learning for Science
April 5, 2022Steven L. BruntonMachine Learning for Scientific Discovery, with Examples in Fluid Mechanics
April 26, 2022Jeremy CohenSemi-supervised Low-rank Approximation, from variational methods to deep learning
May 31, 2022Michael EickenbergWavelet methods for cosmological parameter estimation
June 8, 2022Yann LeCunSelf-Supervised Learning, Energy-based Methods, and World Models
August 2, 2022Zahra KadkhodaieStochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser
October 4, 2022Mariel Patee Interdisciplinary Machine Learning for Fundamental Physics (and Art)
October 18, 2022Johann BrehmerLearning causal representations through weak supervision
October 25, 2022Wenda ZhouAn introduction to higher-order graph neural networks
November 1, 2022Alan HeavensHierarchical Bayesian Models and Simulation-based Inference in Cosmology
November 11, 2022Joan BrunaSeparations in symmetric and antisymmetric neural ansatze
December 6, 2022Ben WandeltNeural Computation in Bayesian Inference and Applications to Cosmological Data Science
December 13, 2022Lawrence SaulA geometrical connection between sparse and low-rank matrices and its uses for machine learning
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