I’m Ruoyu Wang, 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.

Currently, I’m on the job market. I am open to beginning my next adventure anywhere in the world. Please don’t hesitate to contact me (email) if you are interested in my research or would like to share any comments/ideas!

Research Interests

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

Work Experience

• Postdoctoral Fellow in Department of Biostatistics, Harvard University, Sept 2022 ~ present

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. 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.
4. Wang, R., Zhang, H., and Lin X.^* (2025+), 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.
5. Hu, W.¹, Wang, R.¹, Li, W.^*, and Miao, W.^* (2025+), Semiparametric efficient fusion of individual data and summary statistics. Revision invited by Journal of the American Statistical Association: T&M (JASA T&M). arXiv:2210.00200.
6. Wang, R. and Lin X.^* (2025+), 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.^* (2025+), 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.^* (2025+), 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.^* (2025+), 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 Journal of the American Statistical Association: T&M, 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: August 5th, 2025)