August 1, 2026
Introduction
This short course is designed for researchers in statistics and data analysis who are interested in exploring recent advances in deep learning and applying these methods to complex statistical problems.
Building on last year’s offering, we will make substantial structural revisions to improve the organization of the course. The course will cover two parts, in the afternoon session, Dr Hongtu Zhu will introduce the basics of deep learning, while in the afternoon session, we will highlight focus on two specific topics (LLMs and diffusion models) which will focus more on up-to date research topics.
Through this one-day short course, participants will gain practical experience in exploring and applying deep learning methodologies to a range of statistical challenges. Basic knowledge of Python programming will be helpful but is not required.
Coding Sessions
📢 This year, we offer the coding sessions in a virtual format. The recordings of coding sessions as well as the updated coding materials will be offered. Stay tuned for the updates!
In the mean time, you can check out the coding materials from our JSM 2025 and ENAR 2026 short courses.
Schedule and Slides
📢 Finalized slides will be released shortly after the short course!
| Topic | Time | Speaker |
|---|---|---|
| Foundations of Deep Learning Methods | Morning session | Hongtu Zhu |
| Large Language Models | Afternoon session I | Runpeng Dai |
| Diffusion Models and Flow Matching | Afternoon session II | Xiao Wang |
Tutorial Organizers
Hongtu Zhu is the Kenan Distinguished Professor of Biostatistics, Statistics, Radiology, Computer Science and Genetics at the University of North Carolina at Chapel Hill. He was a DiDi Fellow and Chief Scientist of Statistics at DiDi Chuxing between 2018 and 2020 and held the Endowed Bao-Shan Jing Professorship in Diagnostic Imaging at MD Anderson Cancer Center between 2016 and 2018. He is an internationally recognized expert in statistical learning, medical image analysis, precision medicine, biostatistics, artificial intelligence, and big data analytics. He received an established investigator award from the Cancer Prevention Research Institute of Texas in 2016, the INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice in 2019, the IMS 2027 Medallion award and Lecture, and the COPSS 2025 Snedecor Award. He has published more than 345 papers in top journals, including Nature, Science, Cell, Nature Genetics, Nature Communication, PNAS, AOS, JASA, Biometrika, and JRSSB, as well as presenting 58+ conference papers at top conferences, including meetings for Neurips, ICLR, ICML, AAAI, and KDD. He is the coordinating editor of JASA and the editor of JASA ACS.
Xiao Wang obtained his Ph.D. in Statistics from University of Michigan. He is Head and J.O. Berger and M.E. Bock Professor of Statistics at Purdue University. His research expertise lies at the intersection of modern statistics and AI. Dr. Wang's work has been featured in leading statistical journals and machine learning conferences, and he is a fellow of the Institute of Mathematical Statistics (IMS) and the American Statistical Association (ASA). He also serves as associate editor of Journal of the American Statistical Association, Technometrics, and Lifetime Data Analysis.
Runpeng Dai is a third-year Ph.D. candidate at the University of North Carolina at Chapel Hill, advised by Dr. Hongtu Zhu. Before that, he obtained his B.S in Statistics from the Shanghai University of Finance and Economics where he was advised by Dr. Fan Zhou. His research sits at the intersection of Reinforcement Learning and LLM Reasoning.