I focus on developing novel statistical and AI methods tailored for handling complex structured data and addressing associated challenges. My research interests span data fusion, transfer learning, semi‑supervised learning, measurement error, and missing data.
If you are interested in my research, please feel free to email me for further discussion.
Publications
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Zhao, H. and Wang, T. (2026). “Simulation-free extrapolation for misspecified models induced by categorizing an error-prone continuous covariate”, Statistical Science, accepted.
- Zhao, H. and Wang, T. (2026). “Augmented transfer regression learning for completely missing covariates”, Journal of the Royal Statistical Society Series B: Statistical Methodology, published online.
- An earlier version won the 2025 IMS Hannan Graduate Student Travel Award.
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Zhao, H. and Wang, T. (2026). “Doubly robust transfer learning under sub-group shift for cohort-level missing indicator covariates”, Statistica Sinica, accepted.
- Zhao, H. and Wang, T. (2024). “Debiased high-dimensional regression calibration for errors-in-variables log-contrast models”, Biometrics, 80(4), ujae153.
Under Review/Working Papers
- Zhao, H. and Deng, K. (2026+). “Transfer learning for generalized linear models with completely missing data”.
- Zhao, H., Liu, M., and Wang, T. (2026+). “A data fusion framework for errors-in-variables”.
- Zhao, H. and Deng, K. (2026+). “A low-rank hierarchical ANOVA logistic model for imported food data studies”.
- Ma, X.*, Zhao, H.*, and Deng, K. (2026+). “A weighted deconvolution calibration method for categorized continuous variables subject to measurement error”. (* joint first authors)