I’m Ruoyu Wang, a tenure-track assistant professor in Department of Statistics and Data Science at Tsinghua University. Before joining Tsinghua, I was a postdoc in the Department of Biostatistics at Harvard University working with Prof Xihong Lin. My research focuses on methodology development for data integration problems with biased/heterogeneous data sources and causal inference with unmeasured confounders.

I am recruiting Ph.D. students with interests in data integration, transfer learning, causal inference, and the intersection of AI and precision medicine. Please feel free to contact me (ruoyuwang@mail.tsinghua.edu.cn) if you are interested.

Research Interests

Data Fusion, Causal Inference, Domain Generalization, Missing Data, Sampling Design, Large-scale Data Analysis

Work Experience

• Assistant Professor in the Department of Statistics and Data Science, Tsinghua University, Sept 2026 ~ present
• Postdoctoral Fellow in the Department of Biostatistics, Harvard University, Sept 2022 ~ Aug 2026

Education

• Ph.D. in Probability and Mathematical Statistics, Academy of Mathematics and Systems Science, 2022
• B.S. in Statistics, Nankai University, 2017

Representative Papers

1. Wang, R., Wang Q.^*, and Miao, W. (2023), A robust fusion-extraction procedure with summary statistics in the presence of biased sources. Biometrika, 110, 1023–1040.
2. Wang, R., Su, M., and Wang, Q.^* (2023), Distributed nonparametric imputation for missing response problems with massive data. Journal of Machine Learning Research (JMLR), 68, 1–52.
3. Hu, W.¹, Wang, R.¹, Li, W.^*, and Miao, W.^* (2026), Semiparametric efficient fusion of individual data and summary statistics. Journal of the American Statistical Association: T&M (JASA T&M) , in press.
4. Wang, R.¹, Yi, M.¹, Chen, Z., and Zhu, S. (2022), Out-of-distribution generalization with causal invariant transformations. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 375–385.
5. Wang, R., Zhang, H., and Lin X.^* (2026+), Debiased estimating equation method for versatile and efficient Mendelian randomization using a large number of correlated weak and invalid instruments. Revision invited by Journal of the American Statistical Association: T&M (JASA T&M). arXiv:2408.05386.
6. Wang, R. and Lin X.^* (2026+), Divide-and-shrink: An efficient and heterogeneity-agnostic approach for transfer estimation using summary statistics. Revision invited by Journal of the Royal Statistical Society: Series B.
7. Su, M. and Wang, R.^* (2026+), A moment-assisted approach for improving subsampling-based MLE with large-scale data. Revision invited by Journal of Machine Learning Research. arXiv:2309.09872.
8. Yang, H.¹, Wang, R.¹, Lin, Y., and Lin, X.^* (2026+), Tail likelihood ratio method for large-scale causal mediation testing in epigenome-wide studies. Revision invited by Journal of the American Statistical Association: ACS (JASA ACS).
9. Wang, R. and Miao, W.^* (2026+), Causal Effect Identification and Inference with Endogenous Exposures and a Light-tailed Error. Under review. arXiv:2408.06211.

¹ : equal contribution; ^* : corresponding author. A full list of publications can be found in the “Research” section in the upper-left corner of this website.

Visit

Department of Statistics, Rutgers University. March 2025.

Service

• Reviewer for Biometrika, Journal of Machine Learning Research, Journal of the American Statistical Association: T&M, Journal of the American Statistical Association: ACS, Transactions on Pattern Analysis and Machine Intelligence (TPAMI); Biometrics; Journal of Computational and Graphical Statistics; Statistics in Medicine, IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
• Session Chair for Joint Statistical Meeting, Portland, OR, 2024.

Curriculum Vitae

Download Curriculum Vitae (Last update: June 18th, 2026)