{"691851":{"#nid":"691851","#data":{"type":"event","title":"School of CSE Seminar Series: Guannan Zhang","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ESpeaker:\u003C\/strong\u003E\u0026nbsp;Guannan Zhang, distinguished senior staff scientist at Oak Ridge National Laboratory\u003Cbr\u003E\u003Cstrong\u003EDate and Time:\u003C\/strong\u003E\u0026nbsp;August 28, 2:00-3:00 p.m.\u0026nbsp;\u003Cbr\u003E\u003Cstrong\u003ELocation:\u003C\/strong\u003E\u0026nbsp;Coda 114\u003Cbr\u003E\u003Cstrong\u003EHost:\u003C\/strong\u003E\u0026nbsp;Peng Chen\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u0026nbsp;\u003Cem\u003EGenerative Artificial Intelligence for Uncertainty Quantification\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E Generative artificial intelligence models\u2014including variational autoencoders, normalizing flows, generative adversarial networks, and diffusion models\u2014have dramatically advanced the realism and quality of generated images, text, and audio. Beyond these tasks, generative 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 synthesis emphasizes producing high-quality individual samples, UQ applications require 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, we 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 training-free conditional diffusion models for amortized Bayesian inference. We 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.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E 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. Guannan\u0027s research interests include high-dimensional approximation, uncertainty quantification, machine learning and artificial intelligence, stochastic methods for scientific inverse problems.\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003ESpeaker:\u003C\/strong\u003E\u0026nbsp;Guannan Zhang, distinguished senior staff scientist at Oak Ridge National Laboratory\u003Cbr\u003E\u003Cstrong\u003EDate and Time:\u003C\/strong\u003E\u0026nbsp;August 28, 2:00-3:00 p.m.\u0026nbsp;\u003Cbr\u003E\u003Cstrong\u003ELocation:\u003C\/strong\u003E\u0026nbsp;Coda 114\u003Cbr\u003E\u003Cstrong\u003EHost:\u003C\/strong\u003E\u0026nbsp;Peng Chen\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u0026nbsp;\u003Cem\u003EGenerative Artificial Intelligence for Uncertainty Quantification\u003C\/em\u003E\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"School of CSE hosts a seminar from Guannan Zhang, distinguished senior staff scientist at Oak Ridge National Laboratory"}],"uid":"36319","created_gmt":"2026-08-21 14:34:23","changed_gmt":"2026-08-21 17:07:34","author":"Bryant Wine","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-08-28T14:00:00-04:00","event_time_end":"2026-08-28T15:00:00-04:00","event_time_end_last":"2026-08-28T15:00:00-04:00","gmt_time_start":"2026-08-28 18:00:00","gmt_time_end":"2026-08-28 19:00:00","gmt_time_end_last":"2026-08-28 19:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Coda, Room 114","extras":[],"hg_media":{"674837":{"id":"674837","type":"image","title":"Guannan Zhang.jpeg","body":null,"created":"1725559999","gmt_created":"2024-09-05 18:13:19","changed":"1725559999","gmt_changed":"2024-09-05 18:13:19","alt":"CSE Seminar Series","file":{"fid":"258401","name":"Guannan Zhang.jpeg","image_path":"\/sites\/default\/files\/2024\/09\/05\/Guannan%20Zhang.jpeg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2024\/09\/05\/Guannan%20Zhang.jpeg","mime":"image\/jpeg","size":11470,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2024\/09\/05\/Guannan%20Zhang.jpeg?itok=ejFnO5Js"}}},"media_ids":["674837"],"groups":[{"id":"47223","name":"College of Computing"},{"id":"50877","name":"School of Computational Science and Engineering"}],"categories":[],"keywords":[{"id":"166983","name":"School of Computational Science and Engineering"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"194945","name":"Alumni"},{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003ERapha\u00ebl Pestourie (rpestourie3@gatech.edu)\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}