Title: 

ISyE Statistic Seminar – Xiaotong Shen

Abstract: 

Synthetic data generation is reshaping data science by addressing challenges of scarcity, privacy, and imbalance. Recent advances in generative modeling enable the creation of high-fidelity datasets that capture complex distributions across modalities. These models not only expand data volume but also improve prediction accuracy, often outperforming conventional predictive methods, and can serve as a resampling tool for inference, much like the bootstrap. Moreover, they enhance multimodal analysis and provide targeted data augmentation for applied problems such as class imbalance.

Through case studies in sentiment analysis, predictive modeling, and tabular inference, we demonstrate how generative models enrich supervised learning, strengthen statistical inference, and provide scalable solutions when raw data are limited or biased.

This work is joint with Y. Liu, R. Shen, and X. Tian.

Bio: 

Xiaotong T. Shen is the John Black Johnston Distinguished Professor in the College of Liberal Arts at the University of Minnesota. He earned his Ph.D. in Statistics from the University of Chicago in 1991.

Professor Shen specializes in machine learning and data science, high-dimensional inference, non/semi-parametric inference, causal relations, graphical models, explainable Machine Intelligence (MI), personalization, recommender systems, natural language processing, generative modeling, and nonconvex minimization. His current research efforts are devoted to further developing causal and constrained inference, generative inference and prediction for black-box learners, and diffusion, normalizing flows, and summarization. The targeted application areas are biomedical sciences, artificial intelligence, and engineering.

He has served on the editorial boards of leading journals—including JMLR, JASA, and the Annals of Statistics—and remains deeply involved in professional service, currently as Chair-Elect of the ASA’s Section on Statistical Learning and Data Science. His distinctions include election as a Fellow of the Institute of Mathematical Statistics, the American Statistical Association, and AAAS, along with honors such as the “Scholar of the College” award and the ICSA Distinguished Achievement Award.