Diffusion models are now the de facto approach to generative modeling across a wide range of data modalities including images, audio, videos, and visuomotor policies. In recent years, there has been a surge of interest in building the mathematical foundations undergirding this family of methods. This research has driven a rich transfusion of ideas between the practice of generative modeling on the one hand, and research in physics, TCS, statistics, and the foundations of machine learning on the other. This workshop will serve as a "preview" to the upcoming Simons semester on Diffusion Generative Modeling to be held in Fall '27, in which we will take stock of recent progress on both theoretical and empirical fronts. Participants will have the opportunity to provide their input on directions that they would like to see represented in the programming next year.
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