Diffusion models have emerged as a powerful approach to generative modelling of images. In this tutorial, we will discuss the basic mathematical models and techniques that underlie diffusions. Topics covered will include an overview of stochastic differential equations, the Fokker-Planck equation, forward and reverse processes, learning score functions through Tweedie’s formula, and ODE flow models. If time permits, we will survey state-of-art approaches to posterior sampling with diffusion models for image editing, stylization, and personalization.
Sanjay Shakkottai received his Ph.D. from the ECE Department at the University of Illinois at Urbana-Champaign in 2002. He is with The University of Texas at Austin, where he is a Professor in the Chandra Family Department of Electrical and Computer Engineering, and holds the Cockrell Family Chair in Engineering #15. He is also the Director of the Center for Generative AI, a campus-wide computing cluster at UT Austin. He received the NSF CAREER award in 2004 and was elected as an IEEE Fellow in 2014. He was a co-recipient of the IEEE Communications Society William R. Bennett Prize in 2021. He has served as the Editor in Chief of IEEE/ACM Transactions on Networking. His research interests lie at the intersection of statistical learning and algorithms for resource allocation, with applications to generative models and wireless communication networks.