Probabilistic Inference for Generative Models: Enabling Scientific Discovery with Statistical Insights - Luhuan Wu

Abstract: Generative models extensively trained on domain data hold immense potential for scientific discovery, but unlocking their utility requires principled statistical tools to extract meaningful insights. In this talk, Luhan will present her work developing conditional inference methods for diffusion models — a class of generative models powering breakthroughs in protein design, image generation, and beyond. By leveraging the sequential Monte Carlo framework, her approach enables efficient and accurate sampling of outputs from pretrained diffusion models that satisfy domain-specific constraints. She will demonstrate its success in a protein design task, where the method generates protein structures with desired functional segments. Building on this work, she will outline her vision to establish a scalable and reliable probabilistic machine learning framework that bridges statistics, generative models, and modern scientific challenges. Bio: Luhuan Wu is a PhD candidate in the Department of Statistics at Columbia University, where she is co-advised by Prof. David Blei and Prof. John Cunningham. Her research focuses on developing probabilistic machine learning methods to address challenges in modern scientific applications, including astrophysics, biology, and protein science. Her work spans large-scale spatio-temporal modeling, approximate inference, deep generative models, uncertainty quantification, and Bayesian modeling.