Qi Xu
I am a tenure-track assistant professor in the School of Statistics at the University of Minnesota. Prior to this, I was a postdoctoral researcher in the Department of Statistics & Data Science at Carnegie Mellon University, working with Kathryn Roeder and Jing Lei. Previously, I received my PhD from the University of California, Irvine, advised by Annie Qu. Before that, I earned an M.S. in Statistics from the University of Illinois Urbana-Champaign and a B.S. in Mathematics from Tongji University.
Research
- AI for Stat: foundation models for statistical problems
- Stat for AI: multi-modal AI and automated data analysis in the agentic loop
- Applications: genomics, EHR
Recent Updates
| Aug 04, 2026 | I will give a talk at the JSM Biometrics early career award session [slides]. |
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| Jun 14, 2026 | I will give a talk on semiparametric inference under blockwise missingness at the ICSA Applied Statistics Symposium [slides]. |
| Jun 09, 2026 | Our paper representation retrieval learning for heterogeneous data integration [Arxiv] is accepted in Journal of the American Statistical Association. We propose a sparse dictionary learning type method for multi-task learning, incorporating blockwise missing covariates. Our main finding is that the generalization error is governed by the sum of square-root of integrativeness of each representer in the dictionary. A selective integration penalty is proposed to encourage integrativeness of representers to improve prediction performance. |
| Feb 05, 2026 | New Preprint Available: sparse group principal component analysis [Arxiv]. This work proposes an efficient iterative algorithm with double thresholding steps to estimate principal components where data is collected from multi-view, especially multi-cell type in gene expression data. |
| Jan 11, 2026 | Our paper Blockwise Missingness meets AI [ArXiv] just won the ASA Biometrics Early Career Award. |