August 3, 2025
Introduction
This short course is designed for researchers in statistics and data analysis who are eager to explore the latest trends in deep learning and apply these methods to solve complex statistical problems. The course delves into the intersection of deep learning and statistical analysis, covering topics familiar to statisticians such as time series analysis, survival analysis, and quantile regression. Additionally, it addresses cutting-edge topics in the deep learning community, including transformers, diffusion models, and large language models. In this one-day short course participants will gain hands-on experience in exploring and applying deep learning methodologies to tackle various statistical challenges. Basic knowledge of Python programming will be helpful but not necessary.
Coding Sessions
| Session | Open in Colab |
|---|---|
| Session 1: Introduction |  |
| Session 2: Generative Models | |
Schedule and Slides
📢 Finalized slides will be released shortly after the short course!
| Topic | Duration | Speaker |
|---|---|---|
| Foundations of Deep Learning Methods | TBD | Hongtu Zhu |
| Computational Resources and Examples | TBD | Runpeng Dai |
| Deep Generative Models | TBD | Xiao Wang |
| Attention and Transformer | TBD | Xiao Wang |
| Deep Sequence Modeling and Spatio-temporal Modeling | TBD | Hongtu Zhu |
| Large Language Models | TBD | Runpeng Dai |
| Deep Learning in Advanced Statistical Problems | TBD | Hongtu Zhu |
Tutorial Organizers
Hongtu Zhu is Professor of Biostatistics, Statistics, and Computer Science at the University of North Carolina at Chapel Hill. He earned his PhD in Statistics from the Chinese University of Hong Kong in 2000. He was the chief scientist of statistics in DiDi Chuxing, a tech company. Zhu is particularly noted for his work in neuroimaging data analysis and causal reinforcement learning in two-sided markets and the integration of big data, focusing on secondary data analysis related to e-commerce and neurodegenerative and neuropsychiatric diseases. He is the fellow of IMS and ASA. He also serves as the editor of JASA applications and case studies and the coordinating editor of JASA.
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 obtained his B.S in statistics from Shanghai University of Finance and Economics and is now a PhD candidate in Department of Biostatistics at University of North Carolina at Chapel Hill. His research interest lies in Reinforcement learning, Large language model and spatial-temporal data analysis.