School of CSE Seminar Series: Guannan Zhang

Speaker: Guannan Zhang, distinguished senior staff scientist at Oak Ridge National Laboratory
Date and Time: November 14, 2:00-3:00 p.m. 
Location: Coda 114
Host: Peng Chen

Title: Generative Artificial Intelligence for Uncertainty Quantification

Abstract: Generative artificial intelligence models, including variational autoencoders, normalizing flows, generative adversarial networks, and diffusion models, have dramatically advanced the realism and quality of generated images, text, and audio. Beyond media synthesis, these models hold great promise as powerful tools for probability density estimation and high-dimensional sampling, which are central to uncertainty quantification (UQ) tasks such as amortized Bayesian inference and data assimilation. However, while research on image and audio synthesis emphasizes producing high-quality individual samples, UQ applications demand accurate approximation of statistical quantities of interest rather than visually realistic samples. As a result, direct application of existing generative models to UQ problems can lead to biased approximations or unstable training. In this talk, I will introduce several new generative approaches tailored to UQ. These include training-free diffusion models for density estimation, a score-based nonlinear filter for data assimilation, and conditional diffusion models for amortized Bayesian inference. I will demonstrate their effectiveness across a range of tasks, including density estimation for unimodal and multimodal distributions, learning stochastic dynamical systems, parameter estimation via amortized inference, and scalable data assimilation for atmospheric models.

Bio: Dr. Guannan Zhang is a Distinguished Staff Scientist in Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL). He earned my Ph.D. in applied mathematics at Florida State University in 2012. He joined ORNL in 2012 as the Householder fellow. He received the DOE Early Career Award in 2022. He has been holding a joint faculty appointment with the Department of Mathematics and Statistics at Auburn University since 2014, and a joint faculty appointment with Department of Mathematics at University of Tennessee since 2022. Guannan's research interests include high-dimensional approximation, uncertainty quantification, machine learning and artificial intelligence, stochastic methods for scientific inverse problems.