Location: Duke University, Durham, NC. Gross Hall, Room 103.
Dates: August 17-20, 2026
This graduate summer school will feature five speakers, each giving multiple tutorial lectures to introduce current topics in the area of stochastic dynamics and connections with machine learning. The target audience is graduate students and postdocs in mathematics, working in this broad research area. There will also be a poster session and problem sessions related to the lectures.
Registration:
Please register for the summer school HERE. There is no registration fee, but your registration will help us plan.
Speakers:
Alex Blumenthal (Georgia Institute of Technology): “Stability and instability of almost-surely invariant structures in random systems — Lyapunov’s first method meets his second”
Jianfeng Lu (Duke University): “Analysis of Diffusion Models”
Kavita Ramanan (Brown University): “Limit theorems for interacting particle systems on networks with diverse topologies”
Maximilian Engel (University of Amsterdam): “Random Dynamical Systems in Artificial Neural Networks”
Ricardo Baptista (University of Toronto): “Machine Learning for Inverse Problems and Data Assimilation”
Tentative Schedule:
All talks will take place in Gross Hall 103.
Monday, August 17
9:00–10:15 Alex Blumenthal
10:15–10:30 BREAK
10:30–11:45 Ricardo Baptista
11:45 – 1:15 LUNCH
1:15–2:30 Kavita Ramanan
2:30-2:45 BREAK
2:45 – 4:00 Problem/discussion session
Tuesday, August 18
9:00–10:15 Kavita Ramanan
10:15–10:30 BREAK
10:30–11:45 Maximilian Engel (via Zoom)
11:45 – 1:15 LUNCH
1:15–2:30 Ricardo Baptista
2:30-2:45 BREAK
2:45 – 4:00 Problem/discussion session
Wednesday, August 19
9:00–10:15 Kavita Ramanan
10:15–10:30 BREAK
10:30–11:45 Jianfeng Lu
11:45 – 1:15 LUNCH
1:15–2:30 Alex Blumental
2:30-2:45 BREAK
2:45 – 4:00 Problem/discussion session
4:00-5:15 POSTER Session
Thursday, August 20
9:00–10:15 Jianfeng Lu
10:15–10:30 BREAK
10:30–11:45 Maximilian Engel (via Zoom)
11:45 – 1:15 LUNCH
1:15–2:30 Alex Blumenthal
2:30-2:45 BREAK
2:45 – 4:00 Ricardo Baptista
Titles of Posters at Poster Session (on Wednesday):
Tianmin Yu (Northwestern University): “Mixing times of Langevin dynamics for spiked matrix models”
Thejani Gamage (University of Massachusetts): “Task-specific fine-tuning of generative models”
Sanchay Agarwal (Pennsylvania State University): “Optimizing mapping operators for Course-Grained Molecular Dynamics”
Jack Sullivan (University of North Carolina at Chapel Hill): “Active and Passive Mixtures Interacting Through a Chemical Gradient”
Nali Badis Khelifa (University of Cambridge): “Quantifying Error Propagation and Model Collapse in Diffusion Models”
Jiachen Liu (University of California at Davis): “Optimal Transport Learning through Synchronized Forward-Backward Interpolants”
Arif Ali (Louisiana State University): “Distributional Reinforcement Learning”
Thomas Aloysius O’Hare (Northwestern University) : “Finite Periodic Data Rigidity for Area-Preserving Anosov Diffeomorphisms”
Jiayi Wang (Duke University): “Posterior Sampling with Multiscale Diffusion Prior”
Tianyi Liu (Imperial College London): TBD
Enrique Carro Garrido (Universiteit van Amsterdam): “A cascade of Hopf bifurcations in infinite dimensions with(out) noise”
Organizers:
Nick Cook, Alex Dunlap, Jonathan Mattingly, Jim Nolen, and the Research Training Group in Analysis, Probability, PDE and Applications.
Questions can be sent to the organizers at: probability-summer26@duke.edu
This summer school is funded by the US National Science Foundation, grant DMS-2038056.
