Yuliang Xu is an assistant professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University, with a secondary appointment in the Department of Biostatistics. Before joining Johns Hopkins, she was a postdoctoral scholar in the Department of Statistics and the Data Science Institute at the University of Chicago, and previously a postdoctoral researcher in the Department of Statistical Science at Duke University. She received her Ph.D. in Biostatistics from the University of Michigan in 2024, her M.S. in Statistics from the University of Waterloo in 2019, and her B.S. in Mathematics and Applied Mathematics from South China University of Technology in 2017.
Xu develops statistical methods for high-dimensional and complex data, bringing together modern Bayesian inference, machine-learning–driven generative modeling, and scientific applications. A central theme of her research is to create rigorous and interpretable tools for evaluating, understanding, and improving generative models, so that advances in generative models can be grounded in statistical theory and trusted in scientific studies. Her work also develops high-dimensional Bayesian methods for biomedical data, including neuroimaging, microbiome, and sequencing studies, where complex spatial structures, sparse signals, and large-scale computation require both methodological innovation and scalable algorithms. More broadly, her long-term research vision is to bridge cutting-edge generative modeling with robust statistical inference, enabling trustworthy machine learning methods for complex scientific discovery.