Location: Duke University, Durham, NC
Dates: August 17-20, 2026
This graduate summer school will feature four principal 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. More information, including a schedule, will be posted here as it is available.
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”
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”
Nicholas Boffi (Carnegie Mellon University): “Flow-based generative models, Flow maps, Inference-time scaling and fine-tuning”
Registration:
Please register for the summer school HERE. There is no registration fee. For US Citizens and Permanent Residents, there is some funding from the US National Science Foundation to support participation, which you can request through registration. This funding will be committed on a rolling basis. Early application is encouraged; the first round of funding offers will be made at the end of April.
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.
