Frontiers in Scientific Machine Learning (FSML)

The Frontiers in Scientific Machine Learning (FSML) Seminar Series at the University of Michigan is organized by Sahil Bhola and Aniket Jivani. This is a platform dedicated to exploring the intersection of machine learning with scientific research and engineering.  This series will feature talks from leading experts and student researchers in the field, covering cutting-edge topics and fostering discussions on the latest advancements and challenges in scientific ML.

Lecture Format: The seminars are held in hybrid format, with the audience joining in-person and online via Zoom. Presenters give talks ranging from 30 to 45 minutes in duration, followed by discussions and Q & A.

Time: Fridays (every two weeks), 12pm – 1pm Eastern Time

 

Recordings of previous talks can be found on our YouTube Channel – FSML Seminars at UMich

 

Details of upcoming talks can be found below and also at the FSML U-M Events Page

Next talk: 25th April, 2025

Speaker: Julie Bessac (National Renewable Energy Laboratory) 

Title: Statistical learning for Summary Statistics of Physics-based Model Outputs and their Correction and Probabilistic Outputs from Neural Networks applied to Super-resolution

Venue: 2636 GGBA and Zoom

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DateSpeaker (Click on link for bio and abstract)
April 25, 2025FSML Lecture Series – Julie Bessac (National Renewable Energy Laboratory): Statistical learning for Summary Statistics of Physics-based Model Outputs and their Correction and Probabilistic Outputs from Neural Networks applied to Super-resolution
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March 28, 2025FSML Lecture Series – Lianghao Cao (Caltech): Derivative-Informed Operator Learning with Applications to Cost-Efficient Bayesian Inversion
March 14, 2025FSML Lecture Series – Alexander Tong (Mila – Quebec AI Institute): Flow matching in cell trajectories and protein design
February 28, 2025FSML Lecture Series – Doruk Aksoy: From Turbulent Flows to Video Games: Managing Large-Scale Data with Tensor Decomposition
February 14, 2025FSML Lecture Series – Ricardo Vinuesa: Identifying coherent structures and controlling turbulent flows through deep learning
January 31, 2025FSML Lecture Series – Panos Stinis: When big neural networks are not enough: physics, multi-fidelity and kernels
December 06, 2024FSML Lecture Series – Anoushka Bhutani: Foundation Model for Molecular Design
November 15, 2024FSML Lecture Series – Hongfan Chen: Global Geomagnetic Perturbation Forecasting with Quantified Uncertainty using Deep Gaussian Process
November 01, 2024FSML Lecture Series – Nicholas Galioto: Discovery of Cellular Reprogramming Methodology Through Single-cell Foundation Models
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October 18, 2024FSML Lecture Series: Domain decomposition and coupling data-driven models of fluid flows by Christopher Wentland, Sandia National Labs
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October 04, 2024FSML Lecture Series: Tokenization for Chemistry by Alex Wadell, University of Michigan