Maxim Raginsky received the B.S. and M.S. degrees in 2000 and the Ph.D. degree in 2002 from Northwestern

University, all in Electrical Engineering. He has held research positions with Northwestern, the University of Illinois at

Urbana-Champaign (where he was a Beckman Foundation Fellow from 2004 to 2007), and Duke University.

In this talk, based on joint work with Belinda Tzen and with Tanya Veeravalli, he will discuss some theoretical

foundations of generative modeling and function approximation using diffusion models. Prof. Raginsky will first provide a

unified viewpoint on both sampling and variational inference in these models through the lens of stochastic

control. Building on these ideas, he will show that one can efficiently sample from a wide class of terminal target

distributions by choosing the drift of the latent diffusion from the class of multilayer feedforward neural nets,

with the accuracy of sampling measured by the Kullback-Leibler divergence to the target distribution. He will also

discuss the relation between the expressive power of diffusion-based function approximators and nonlinear

controllability, i.e., the problem of optimally steering a certain deterministic dynamical system between two

given points in finite time. Maxim will conclude with an outline of some ongoing work and future directions.