A weekly online seminar on all aspects of Monte Carlo methods.
Join us weekly to explore recent developments and connect with a diverse community of passionate researchers in this exciting field!
Videos of past talks available on YouTube.
For announcements and Zoom invitations please subscribe to our mailing list.
Upcoming Seminar Presentations
All seminars are on Tuesdays 8:30 am PT = 11:30 am ET = 4:30 pm London = 5:30 pm Paris = 11:30 am Beijing (+1d).
Subscribe to our mailing list and calendar for up-to-date schedule!
Tuesday, September 23, 2025
Speaker: Alexandre Bouchard-Côté (University of British Columbia) [Zoom Link]
Title: Computational Lebesgue integration
Abstract: In many modern applications in science and engineering, we seek to reconstruct a complicated object x from noisy data y, for example, one may seek to reconstruct an evolutionary tree from sequencing data. In principle, Bayesian statistics provides a broad framework to approach such problems, by modelling knowns and unknowns as random variables X and Y. Since the notion of a posterior distribution, X|Y, is defined under very general conditions, Bayesian inference is in a sense universal for the purpose of data analysis. In contrast, other inferential setups often require, among other things, for x to be real-valued in order to use approximations such as those based on the central limit theorem.
However, this generality hinges on being able to approximate expectations with respect to an arbitrary measure. Can we develop generic sampling methods in such an unstructured context? Surprisingly, practical methodologies are indeed possible. I will describe some of our work in the area with a focus on recent developments based on regenerative MCMC, particle methods, and non-reversibility. My group is also working on making these complex Monte Carlo methods easy to use: check out https://pigeons.run/dev/ , a package that allows users to leverage clusters of 1000s of nodes to speed up difficult Monte Carlo problems without requiring knowledge of distributed algorithms.
Links: Webpage
Tuesday, September 30, 2025
Speaker: Yian Ma (University of California San Diego)
Tuesday, October 7, 2025
Speaker: Michael Albergo (Harvard)
Format and Rules
The seminars are held on Zoom and last 60 minutes.
A moderator collects audience questions in Q&A section.
Moderators may ask you to unmute yourself to participate in the discussion. Please note that you may be recorded if you activate your audio or video during the seminar.
Organizers
Valentin De Bortoli (Google DeepMind)
Yang Chen (University of Michigan)
Nianqiao Ju (Dartmouth College)
Sifan Liu (Duke University)
Sam Power (University of Bristol)
Qian Qin (University of Minnesota)
Guanyang Wang (Rutgers University)
Feedback and Suggestions
If you have feedback or suggestions or want to propose a speaker, email us at internationalmontecarloseminar@gmail.com, or fill out this form.
Acknowledgements
Sincerely thank Ying Jin for the patient guidance
Gratefully inspired by the Online Causal Inference Seminar