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
| 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. |
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selected publications
- Blockwise Missingness meets AI: A Tractable Solution for Semiparametric InferencearXiv preprint arXiv:2509.24158, 2025
- Representation retrieval learning for heterogeneous data integrationarXiv preprint arXiv:2503.09494, 2025