Applied Mathematics seminars
October 2026
Mon, Oct 26 · 07:00 UTC · Online
Scaling Optimal Transport to High-Dimensional Gaussian Distributions with Application to Domain Adaptation
Charles Bouveyron· Inria Centre at Université Côte d’Azur; Université Côte d’Azur
Mon, Oct 26 · 10:30 UTC · Online
Influence Enhancement in Opinion Dynamics Using Edge Modification: A Kron Reduction-Based Approach
Twinkle Tripathy· Indian Institute of Technology Kanpur
Mon, Oct 26 · 15:30 UTC · Online
IX YOUNG Session: Optimization Approaches for Explainable Models in Distributed Settings; Hard Feature Selection in Nonlinear SVMs; Constraint Learning with Neural Networks
J. Carlos Castro, Ilaria Ciocci, Jiaxiao (Frances) Fang· Instituto de Matemáticas de la Universidad de Sevilla
Wed, Oct 28 · 16:00 UTC · Princeton, United States
The polar express: Optimal matrix sign methods and their application to the muon algorithm
David Persson· New York University
Thu, Oct 29 · 23:00 UTC · Online
How does a cell know how big it is?
Jané Kondev· Brandeis University
November 2026
Wed, Nov 4 · 16:00 UTC · Online
Learning to Oscillate: Hebbian Plasticity Imprints Limit Cycles in Neural Networks
Pierfrancesco Urbani· IPhT, Saclay
Thu, Nov 12 · 15:00 UTC · Online
The Influenza Paradox: When Biomarkers Mislead and Models Reveal the Biology
Amber Smith· University of Tennessee Health Science Center College of Medicine; Institute for the Study of Host-Pathogen Systems
Tue, Nov 17 · 17:00 UTC · Montréal, Canada · Hybrid
Flexibility and Resilience in Cortical Processing: Neural mechanisms and mathematical analysis
Udo Ernst· University of Bremen
Mon, Nov 23 · 15:30 UTC · Online
Beyond Modeling: How ML Is Reshaping Operations Research for Dynamic Decision Making
Shadi Sharif Azadeh· Faculty of Civil Engineering and Geosciences, TU Delft
Mon, Nov 30 · 15:30 UTC · Online
Decision-Focused Scenario Learning for Contextual Stochastic Programming
Tito Homem-de-Mello· School of Business, Universidad Adolfo Ibañez
December 2026
Mon, Dec 7 · 15:30 UTC · Online
The Expressive Power of Large Language Models
Gabriel Peyré· CNRS and École Normale Supérieure, Paris
End of results.