My Google Scholar

With the increasingly large and complex data generated and need to be analyzed, statistics is highly interdisciplinary and rapidly expanding among the sciences. My career goal includes developing state-of-the-art statistical and machine learning methods for solving data-driven problems, advancing statistical theory in the emerging field of data science, and harnessing real-world evidence for decision-making and precision medicine. My primary research is centered around developing statistical theories and methodologies to promote statistical learning in complex data, especially on data heterogeneity and measurement errors. Much of my methodology research applies to genetic and genomic data analysis, microbiome data analysis, epidemiologic research, and environmental statistics.

I view open science practices as an important way to increase access and participation in academic research. Please check all open-source software related to my articles here.

underline indicates a student working under my supervision, with denoting an undergraduate student mentee; indicates the corresponding author.

2025

  • Wang, Z., Ling, W., and Wang, T. (2025). “A Semiparametric Quantile Regression Rank Score Test for Zero-inflated Data”, Biometrics, accepted.

  • Wang, T. (2025). “Moving Beyond Mean: Harnessing Big Data for Health Insights by Quantile Regression,” Big Data Analysis, Biostatistics and Bioinformatics (Eds. Chen, D. and Zhao, Y.), Springer, New York, in press.

  • 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.

2024

2023

2022

2021

2020 and before