Stochastic Computation

The Stochastic Computation workshop highlights recent advances at the intersection of stochastic processes and numerical analysis. Core topics include:
– the design and analysis of algorithms for stochastic differential equations (SDEs and SPDEs);
– theoretical insights and practical advances in accelerated Markov chain Monte Carlo (MCMC) methods; and,
– emerging directions such as rough path theory, uncertainty quantification for random PDEs, backward SDEs (BSDEs), and stochastic optimization techniques used in machine learning (e.g., stochastic gradient descent).

We anticipate strong synergies with the Foundations of Data Science and Machine Learning and Geometric Integration and Computational Mechanics workshops. In particular, the rapidly evolving field of diffusion-based generative modeling presents an exciting new frontier, blending ideas from stochastic numerics, high-dimensional sampling, and machine learning. These connections promise rich opportunities for cross-pollination, especially in the development of new structure-preserving algorithms and scalable inference techniques with rigorous mathematical foundations. The extent of interaction will naturally depend on the participants and confirmed speakers.