Colorado State University

Tianjian Zhou

Assistant Professor

Tianjian.Zhou@colostate.edu

Office: Statistics Building 222

Phone: 

Education:

• Ph.D., Statistics, The University of Texas at Austin, 2017
• B.Sc., Statistics, University of Science and Technology of China, 2013

Website: https://sites.google.com/view/tjzhou

Curriculum Vitae: View Curriculum Vitae

Google Scholar: View Google Scholar Profile

About

Tianjian’s research is focused on Bayesian methods motivated by applications in clinical trials, cancer genomics, missing data, causal inference, and infectious diseases. His specific methodological research interests include nonparametric Bayesian modeling (for factor analysis, regression, clustering, etc.), Bayesian hierarchical modeling, and Bayesian hypothesis testing. Before joining CSU, he held postdoctoral appointments at the University of Chicago and NorthShore University HealthSystem.

Publications

• “Statistical Frameworks for Oncology Dose-Finding Designs with Late-Onset Toxicities: A Review” Zhou, T. and Ji, Y. Statistical Science, 39 (2), 243–261, 2024
• “On Bayesian Sequential Clinical Trial Designs” Zhou, T. and Ji, Y. The New England Journal of Statistics in Data Science, 2(1), 136–151, 2024
• “Tracking the Transmission Dynamics of COVID-19 with a Time-Varying Coefficient State-Space Model” Keller, J. P., Zhou, T., Kaplan, A., Anderson, G. B. and Zhou, W. Statistics in Medicine, 41(15), 2745–2767, 2022
• “Incorporating External Data into the Analysis of Clinical Trials via Bayesian Additive Regression Trees” Zhou, T. and Ji, Y. Statistics in Medicine, 40(28), 6421–6442, 2021
• “RoBoT: A Robust Bayesian Hypothesis Testing Method for Basket Trials” Zhou, T. and Ji, Y. Biostatistics, 22(4), 897–912, 2021
• “PoD-TPI: Probability-of-Decision Toxicity Probability Interval Design to Accelerate Phase I Trials” Zhou, T., Guo, W. and Ji, Y. Statistics in Biosciences, 12, 124–145, 2020
• “Semiparametric Bayesian Inference for the Transmission Dynamics of COVID-19 with a State-Space Model” Zhou, T. and Ji, Y. Contemporary Clinical Trials, 97, No. 106146, 2020
• “A Semiparametric Bayesian Approach to Dropout in Longitudinal Studies with Auxiliary Covariates” Zhou, T., Daniels, M. J. and Müller, P. Journal of Computational and Graphical Statistics, 29(1), 1–12, 2020
• “RNDClone: Tumor Subclone Reconstruction Based on Integrating DNA and RNA Sequence Data” Zhou, T., Sengupta, S., Müller, P. and Ji, Y. The Annals of Applied Statistics, 14(4), 1856–1877, 2020
• “TreeClone: Reconstruction of Tumor Subclone Phylogeny Based on Mutation Pairs Using Next Generation Sequencing Data” Zhou, T., Sengupta, S., Müller, P. and Ji, Y. The Annals of Applied Statistics, 13(2), 874–899, 2019