I’m Ke Zhu, a Postdoctoral Research Scholar jointly affiliated with North Carolina State University and Duke University, mentored by Dr. Shu Yang and Dr. Xiaofei Wang. I received my PhD from Tsinghua University, where I was advised by Dr. Hanzhong Liu.
My research focuses on developing statistical methodology and theory for causal inference, data integration, and uncertainty-aware machine learning. Driven by real-world challenges, I apply these methods to clinical trials, precision medicine, and policy evaluation.
I am actively seeking faculty positions on the 2025–2026 academic job market. My CV is available here.
News
Our invited session, Learning Across Boundaries: Statistical and Machine Learning Methods for Biomedical Data Fusion, has been accepted for JSM 2026.
Our invited session, Conformal Inference for Uncertainty-Aware Machine Learning in Biomedical Research, has been accepted for DISS 2026.
My oral presentation, “Robust Estimation and Inference in Small Hybrid Controlled Trials with Conformal Selective Borrowing,” has been accepted for ENAR 2026.
Professional Experience
Guest Associate Editor, Statistics in Biopharmaceutical Research, 2026–2027
Special Issue on “Externally Controlled Trials: Tutorials, Reviews, and Practical Guidance”Fellow, FDA-OCE-ASA Oncology Educational Fellowship, 2025–2026
Member / Sub-team Co-leader, ASA Biopharmaceutical Section Real World Evidence Scientific Working Group, 2024–present
Hybrid Trial Designs Team
Publications
(† indicates co-first author; * indicates corresponding author)
[1] Liu, Y., Levis, A. W., Zhu, K., Yang, S., Gilbert, P. B., & Han, L.* (2026). Privacy-protected causal survival analysis under distribution shift. Proceedings of the 14th International Conference on Learning Representations (ICLR), PMLR, in press.
[2] Liu, Y., Zhu, K., Han, L., & Yang, S.* (2026). COADVISE: Covariate adjustment with variable selection in randomized controlled trials. Journal of the Royal Statistical Society: Series A, in press. [Package]
[3] Zhu, K., Yang, S.*, & Wang, X. (2025). Enhancing statistical validity and power in hybrid controlled trials: A randomization inference approach with conformal selective borrowing. Proceedings of the 42nd International Conference on Machine Learning (ICML), PMLR, 267: 80282-80309. [Slides] [Poster] [Package] [Code]
** Winner of the NISS New Researcher Presentation Award at the NISS Virtual New Researchers Conference
** Winner of the NSF Travel Award for the 10th Workshop on Biostatistics and Bioinformatics
[4] Zhu, K.†, Chu, J.†, Lipkovich, I., Ye, W., & Yang, S.* (2025). Doubly robust fusion of many treatments for policy learning. Proceedings of the 42nd International Conference on Machine Learning (ICML), PMLR, 267: 79772-79789. [Slides] [Poster]
[5] Yu, H., Zhu, K.*, & Liu, H. (2025). Sharp variance estimator and causal bootstrap in stratified randomized experiments. Statistics in Medicine, 44(13-14): e70139. [Paper] [Package] [Code]
[6] Zhu, K., Liu, H.*, & Yang, Y.* (2025). Design-based theory for lasso adjustment in randomized block experiments and rerandomized experiments. Journal of Business & Economic Statistics, 43(3): 544-555. [Paper] [Slides] [Code]
[7] Zhu, K. & Liu, H.* (2024). Rejoinder to reader reaction “On exact randomization-based covariate-adjusted confidence intervals” by Jacob Fiksel. Biometrics, 80(2): ujae052. [Paper] [Code]
[8] Zhu, K. & Liu, H.* (2023). Pair-switching rerandomization. Biometrics, 79(3): 2127-2142. [Paper] [Code]
[9] Zhu, K. & Liu, H.* (2022). Confidence intervals for parameters in high-dimensional sparse vector autoregression. Computational Statistics & Data Analysis, 168: 107383. [Paper]
Submitted Papers
[10] Li, C., Zhu, K., Yang, S., & Wang, X.* (2026). Selective Information Borrowing for Region-Specific Treatment Effect Inference under Covariate Mismatch in Multi-Regional Clinical Trials. Submitted.
[11] Liu, Y., Levis, A. W., Zhu, K., Yang, S., Gilbert, P. B., & Han, L.* (2025). Targeted Data Fusion for Causal Survival Analysis. Submitted.
[12] Lu, X., Fu, W., Li, H., Yu, H., Zhang, H., Zhu, K.*, & Liu, H. (2025). Design-based Theory for Causal Inference (in Chinese). Submitted.
[13] Liu, J.†, Zhu, K.†, Yang, S., & Wang, X.* (2025). Robust estimation and inference in hybrid controlled trials for binary outcomes: A case study on non-small cell lung cancer. Submitted. [Package]
[14] Han, T.†, Zhu, K.†, Liu, H.*, & Deng, K.* (2025). Imputation-based randomization tests for randomized experiments with interference. Submitted. [Package]
[15] Lu, J., Zhang, T., & Zhu, K.* (2025) Fast rerandomization for balancing covariate in randomized experiments: A Metropolis–Hastings framework. Submitted.
Interdisciplinary Collaborations
[16] Xia, F., Zhu, K., Ren, Y., & Wang, N.* (2024). Efficacy of the automated mechanical repositioning chairs treatment for patients with benign paroxysmal positional vertigo. The Journal of Laryngology and Otology, in press.
[17] Hu, C., Zhu, K., Huang, K., Yu, B., Jiang, W., Peng, K., & Wang, F.* (2022). Using natural intervention to promote subjective well-being of essential workers during public-health crises: A study during the COVID-19 pandemic. Journal of Environmental Psychology, 79: 101745.
[18] Zhang, H., Zhu, K., Wang, J., & Lv, X.* (2022). The use of a new classification in endovascular treatment of dural arteriovenous fistulas. Neuroscience Informatics, 2(2): 100047.
[19] Zhu, K.†, Jiang, Y.†, Wang, X., Shi, Z., Yang, C.*, Liu, H.*, & Deng, K.* (2022). A new framework of customized production product certification based on the combination of domain knowledge and data inference (in Chinese). Chinese Journal of Applied Probability and Statistics, 38(4), 581–602.