Qi Xu

I am currently a postdoctoral researcher in the Department of Statistics & Data Science at Carnegie Mellon University, working with Kathryn Roeder and Jing Lei.

Previously, I obtained my PhD degree from University of California, Irvine, under supervision of Annie Qu. Prior to that, I got my Master and Bachelor degree from University of Illinois at Urbana Champaign and Tongji University.

Recent research interests

Direction 1: Integrating heterogeneous datasets for prediction, estimation and inference
Direction 2: Integrating AI (predictive, generative) models in the loop of statistical analysis
Tools: Semiparametric theory, representation learning, high-dimensional statistics

news

Jan 11, 2026	Our paper Blockwise Missingness meets AI [ArXiv] just won the ASA Biometrics Early Career Award.
Dec 03, 2025	A new paper [Arxiv] on deep learning algorithm for multi-source blockwise missingness problem is accepted in Journal of Computational and Graphical Statistics.
Dec 01, 2025	A new paper on sleep pattern change over pregnancy is accepted in [Frontiers in Global Women’s Health]. A series of work for maternal health can be found in [paper 1], [paper 2], [paper 3] and [abstract].
Sep 30, 2025	New Preprint Available: Blockwise Missingness meets AI [Arxiv]. Our new work tackles a long-standing challenge in statistics: semiparametric inference for data with non-monotone missing patterns. For over 30 years, the theoretically optimal estimator has been known but considered computationally intractable for practical use. We introduce an elegant RAY approximation to the optimal estimating equation, striking a crucial balance between statistical efficiency and computational feasibility. This approach can be seamlessly integrated into both classical semiparametric and modern prediction-powered inference frameworks, offering a powerful new tool for researchers.