My applied work is motivated by scientific problems in which data are large, heterogeneous, and imperfect, and where classical modeling assumptions are often violated. I use applications across statistical genetics and genomics, electronic health records, epidemiology, microbiome studies, and climate science as testbeds to drive methodological development and to demonstrate practical impact.
Rather than treating applications as isolated case studies, I focus on recurring data challenges—such as distribution shift, outcome heterogeneity, and measurement error—that arise across domains. Selected application-driven papers are listed below. For a complete and up-to-date list, please refer to the Publications page.
underline indicates a student co-author under my (co)supervision, with ♦ denoting an undergraduate student mentee; ✉ indicates the corresponding author.
Statistical genetics and genomics
Impact: Scalable discovery and interpretation of heterogeneous genetic effects in large biobank and omics studies.
- Wang, T.✉, Ionita-Laza, I., and Wei, Y. (2025). “A unified quantile framework for nonlinear heterogeneous transcriptome-wide associations”, Annals of Applied Statistics, 19(2), 967–985.
- Wang, T.✉, Ionita-Laza, I., and Wei, Y. (2022). “Integrated Quantile RAnk Test (iQRAT) for gene-level associations”, Annals of Applied Statistics, 16(3), 1423–1444.
- Liu, Y. and Wang, T.✉ (2025). “A powerful transformation of quantitative responses for biobank-scale association studies”, Journal of the American Statistical Association, 1–12.
- Wang, C., Wang, T., Kiryluk, K., Wei, Y., Aschard, H., and Ionita-Laza, I. (2024). “Genome-wide discovery for biomarkers using quantile regression at biobank scale”, Nature Communications, 15(1), 6460.
Electronic health records and epidemiology
Impact: Robust inference and transportable learning in the presence of missing data, cohort shift, and measurement error.
- 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.✉ (2026+). “Augmented transfer regression learning for handling completely missing covariates”, under review.
- Wang, T.✉, Ma, Y., and Wei, Y. (2026+). “Time-varying Quantile Regression with Multi-outcome Latent Groups”, under review.
- Zhao, H. and Wang, T.✉ (2026+). “A simulation-free extrapolation method for misspecified models with errors-in-variables in epidemiological studies”, under review.
- Blas Achic, B.♯, Wang, T.♯, Su, Y., Kipnis, V., Dodd, K., and Carroll, R. J. (2018). “Categorizing a Continuous Predictor Subject to Measurement Error”, Electronic Journal of Statistics, 12(2), 4032–4056.
Microbiome and compositional data
Impact: Distribution-aware and robust association analysis for zero-inflated and compositional microbiome data.
- Wang, Z., Ling, W., and Wang, T.✉ (2025). “A Semiparametric Quantile Regression Rank Score Test for Zero-inflated Data”, Biometrics, 81(2), ujaf050.
- Zhao, H. and Wang, T.✉ (2024). “A high-dimensional calibration method for log-contrast models subject to measurement errors”, Biometrics, 80(4), ujae153.
- Jiang, R.♦, Zhan, X.✉, and Wang, T.✉ (2023). “A Flexible Zero-Inflated Poisson-Gamma Model with Application to Microbiome Read Count Data”, Journal of the American Statistical Association, 118(542), 792–804.
- Wang, T., Ling, W., Plantinga, A., Wu, M., and Zhan, X. (2022). “Testing microbiome association using integrated quantile regression models”, Bioinformatics, 38(2), 419–425.
Climate science
Impact: Robust detection and attribution of climate signals under measurement error and extreme-value behavior.
- Li, Y., Wang, T.✉, Yan, J., and Zhang, X. (2025). “Improved Optimal Fingerprinting Based on Estimating Equations Reaffirms Anthropogenic Effect on Global Warming”, Journal of Climate, 38(8), 1779–1790.
- Lau, Y., Wang, T.✉, Yan, J., and Zhang, X. (2023). “Extreme Value Modeling with Errors-in-Variables in Detection and Attribution of Changes in Climate Extremes”, Statistics and Computing, 33(6), 125.
- Ma, S., Wang, T.✉, Yan, J., and Zhang, X. (2023). “Optimal Fingerprinting with Estimating Equations”, Journal of Climate, 36(20), 7109–7122.