Events

Department Seminars

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  Wednesday, February 11, 2026

  1:30 - 2:30 pm Central Time online

  Statistics in the Age of AI: Theory, Methods, and Data

  We are pleased to welcome Didong Li, PhD, to our Zoom seminar room for "Statistics in the Age of AI: Theory, Methods, and Data." Artificial intelligence (AI) has surged in popularity, creating both opportunities and challenges for statistics. In this talk, Dr. Li will present three recent directions from his lab that reflect his team's efforts to engage with the age of AI. First, he will discuss theoretical results for decoder-based generative models, providing statistical foundations that connect latent dimension, approximation error, and model complexity. Second, he will discuss a method to use embeddings from large language models to enhance high-dimensional hypothesis testing, a widely used statistical tool in scientific domains, motivated by problems in cancer genomics where traditional methods are underpowered. He will also discuss extensions to genetic studies, where his team curated annotations for 8.9 billion genetic variants from the human genome, and obtained embeddings of these 8.9 billion variants for downstream tasks. Finally, he will switch to an infrastructural view, introducing STimage-1K4M, one of the first and largest publicly available spatial transcriptomics datasets curated by his group, consisting of 1,149 slides and more than 4 million pathology image–gene expression pairs across 10 species and 50 tissue types. This resource has been downloaded over 200,000 times on HuggingFace and has facilitated the training of multiple foundation models.

  Dr. Li is Assistant Professor of Biostatistics at the University of North Carolina at Chapel Hill, with secondary affiliations in the Department of Statistics and Operations Research, the Carolina Center for Interdisciplinary Applied Mathematics, and the Lineberger Comprehensive Cancer Center. He received a PhD in mathematics from Duke, completed postdoctoral training at Princeton Computer Science and UCLA Biostatistics, and was a visiting scholar at the Gladstone Institute. His research focuses on theory and methods development for robust inference with complex and high-dimensional data, specifically in manifold learning, nonparametric Bayes, information geometry, and spatial statistics. He has applied these methods to electronic health record data, large-scale genetic data, and spatial transcriptomics.

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  Wednesday, February 25, 2026

  1:30 - 2:30 pm Central Time online

  Statistical Modeling and Inference for Gene Networks from Single-Cell Data

  We are excited about our upcoming session with Emma Jingfei Zhang, PhD, FASA, where she will discuss how advances in single-cell RNA sequencing and multimodal technologies have opened new opportunities for inferring gene networks and regulatory relationships in specific cell types, enriching our understanding of cell-type-specific biological functions. However, unique data characteristics such as sequencing depth variation, high data sparsity, and measurement error present significant challenges. In this talk, Dr. Zhang will present two statistical methods that address these challenges. CS-CORE infers cell-type-specific gene co-expressions from scRNA-seq data and scMultiMap maps enhancer-gene pairs from paired scRNA-seq and scATAC-seq data. Both methods achieve accurate type-I error control, high reproducibility, scalability, and provide new insights into Alzheimer’s disease mechanisms.

  Dr. Zhang is the Goizueta Foundation Term Chair Professor of Information Systems & Operations Management (ISOM) at Emory University’s Goizueta Business School, where she currently serves as area chair for ISOM. She also holds a secondary appointment in Biostatistics & Bioinformatics at the Rollins School of Public Health. Her research focuses on the statistical analysis of networks, graphs and tensors, with applications in business and biomedical sciences. She is an elected member of the International Statistical Institute (ISI) and a Fellow of the American Statistical Association (ASA). She currently serves as Associate Editor for the Journal of the American Statistical Association, Annals of Applied Statistics, and Journal of the Royal Statistical Society Series B.

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