Ruoyu Wang

Ruoyu Wang

Postdoc in Biostatistics

About me

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.

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. 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.* (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.
  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 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: December 13th, 2025)