Bio
Tianhai Zu is an Assistant Professor in the Department of Operations and Analytics at the Carlos Alvarez College of Business, University of Texas at San Antonio. He earned his PhD in Business Analytics from the University of Cincinnati's Carl H. Lindner College of Business. His research focuses on developing advanced statistical and machine learning methodologies for complex, high-dimensional, and networked data, with applications in healthcare, finance, and business analytics. He has developed several open-source R packages and published in top-tier journals such as Journal of the American Statistical Association, Biometrika, and Journal of Multivariate Analysis.
Prior to joining UT San Antonio, he gained industry experience in financial analysis and investment. He is passionate about teaching data-driven decision-making and mentoring students in analytics and machine learning. His work bridges theoretical advancements with practical business and healthcare insights.
Teaching
- Business Analytics and Data Mining
- Data Exploration with Python
- Textual and Network Analytics
Research Interests
- Machine Learning and Artificial Intelligence in Business
- Ultra-high Dimensional Variable Selection
- Network Analysis and Inference
- Uncertainty in Financial Bankruptcy
Degrees
- PhD University of Cincinnati
- MS Pennsylvania State University
- BS Southwestern University of Finance and Economics
Publications
- “Local Bootstrap for Networks,” with T. Zu and Y. Qin, Biometrika, 2024.
- “Ultra-high Dimensional Quantile Regression for Longitudinal Data: An Application to Blood Pressure Analysis,” with T. Zu, B. Green, H. Lian, and Y. Yu, Journal of the American Statistical Association, 2023.
- “Semiparametric Penalized Quadratic Inference Functions for Correlated Data in Ultra-high Dimensions,” with B. Green, H. Lian, Y. Yu, and T. Zu, Journal of Multivariate Analysis, 2023.
- “Ultra High-Dimensional Semiparametric Longitudinal Data Analysis,” with B. Green, H. Lian, Y. Yu, and T. Zu, Biometrics, Vol. 77, Issue 3, 2021, pp. 903–913.
- “The R Package geeVerse for Ultra-High-Dimensional Heterogeneous Data Analysis with Generalized Estimating Equations,” with T. Zu, B. Green, and Y. Yu, Journal of Data Science, 2025.