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: 11th July, 2025

Speaker: Romit Maulik (Penn State University)

Topic: SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning

Venue: 2004 AL and Zoom

Jump To

Past Seminars

Upcoming Lectures

Events

Events Search and Views Navigation

Event Views Navigation

Today

Latest Past Events

Previous Lectures

DateSpeaker (Click on link for bio and abstract)
July 11, 2025Frontiers in Scientific Machine Learning Seminar – Romit Maulik (Penn State University): SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning
Additional Details
June 20, 2025Frontiers in Scientific Machine Learning Seminar – Pan Du (University of Notre Dame): Conditional neural field latent diffusion model for generating spatiotemporal turbulence
Additional Details
June 06, 2025Frontiers in Scientific Machine Learning Seminar – Ashwin Renganathan (Penn State): Sample-efficient and Principled Decision-making with Expensive Stochastic Oracles
Additional Details
May 23, 2025FSML Lecture Series – Smita Krishnaswamy (Yale University): Dynamics Models of Cellular and Neuronal Interactions
Additional Details
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
Additional Details
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
Additional Details
October 18, 2024FSML Lecture Series: Domain decomposition and coupling data-driven models of fluid flows by Christopher Wentland, Sandia National Labs
Additional Details
October 04, 2024FSML Lecture Series: Tokenization for Chemistry by Alex Wadell, University of Michigan