Topic: high-dimensional geometry

Researchers and practitioners will discuss mathematical foundations of data science, including high-dimensional geometry, dimensionality reduction, scalable algorithms, uncertainty and machine learning. The conference takes place in person at the Salt Palace Convention Center on 16–20 November 2026. Early rates run through 19 October; abstract and travel-support deadlines have passed. Registration also covers the co-located SIAM Imaging Science and Data Mining conferences.

Stochastic localization has become a tool for high-dimensional geometry, sampling and diffusion-model analysis. Motivated by mirror descent and non-Euclidean sampling, this talk generalizes the process by replacing Gaussian regularization with multiples of log-Laplace transforms. It introduces the construction, its analysis, applications and remaining questions.