Office:
Phone: (000) 000-0000
Website: https://sites.google.com/view/tjzhou
Curriculum Vitae: https://sites.google.com/view/tjzhou/cv
Google Scholar: https://scholar.google.com/citations?user=_BCNzikAAAAJ&hl=en
Education
- Ph.D., Statistics, The University of Texas at Austin, 2017
- B.Sc., Statistics, University of Science and Technology of China, 2013
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
- On Bayesian Sequential Clinical Trial DesignsThe 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 ModelStatistics in Medicine, 41(15), 2745–2767, 2022
- Statistical Frameworks for Oncology Dose-Finding Designs with Late-Onset Toxicities: A ReviewStatistical Science, 39 (2), 243–261, 2024
- Incorporating External Data into the Analysis of Clinical Trials via Bayesian Additive Regression TreesStatistics in Medicine, 40(28), 6421–6442, 2021
- RoBoT: A Robust Bayesian Hypothesis Testing Method for Basket TrialsBiostatistics, 22(4), 897–912, 2021
- PoD-TPI: Probability-of-Decision Toxicity Probability Interval Design to Accelerate Phase I TrialsStatistics in Biosciences, 12, 124–145, 2020
- Semiparametric Bayesian Inference for the Transmission Dynamics of COVID-19 with a State-Space ModelContemporary Clinical Trials, 97, No. 106146, 2020
- A Semiparametric Bayesian Approach to Dropout in Longitudinal Studies with Auxiliary CovariatesJournal of Computational and Graphical Statistics, 29(1), 1–12, 2020
- RNDClone: Tumor Subclone Reconstruction Based on Integrating DNA and RNA Sequence DataThe Annals of Applied Statistics, 14(4), 1856–1877, 2020
- TreeClone: Reconstruction of Tumor Subclone Phylogeny Based on Mutation Pairs Using Next Generation Sequencing DataThe Annals of Applied Statistics, 13(2), 874–899, 2019
