Tianjian Zhou Assistant Professor


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


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


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.


Tracking the Transmission Dynamics of COVID-19 with a Time-Varying Coefficient State-Space ModelKeller, J. P., Zhou, T., Kaplan, A., Anderson, G. B. and Zhou, W.arXiv, 2021
Semiparametric Bayesian Inference for the Transmission Dynamics of COVID-19 with a State-Space ModelZhou, T. and Ji, Y.Contemporary Clinical Trials, 97, No. 106146, 2020
Emerging Methods for Oncology Clinical TrialsZhou, T. and Ji, Y.CHANCE, 33(3), 39–48, 2020
RNDClone: Tumor Subclone Reconstruction Based on Integrating DNA and RNA Sequence DataZhou, T., Sengupta, S., Müller, P. and Ji, Y.Annals of Applied Statistics, 14(4), 1856–1877, 2020
RoBoT: A Robust Bayesian Hypothesis Testing Method for Basket TrialsZhou, T. and Ji, Y.Biostatistics, forthcoming, 2020
PoD-TPI: Probability-of-Decision Toxicity Probability Interval Design to Accelerate Phase I TrialsZhou, T., Guo, W. and Ji, Y.Statistics in Biosciences, 12, 124–145, 2020
A Semiparametric Bayesian Approach to Dropout in Longitudinal Studies with Auxiliary CovariatesZhou, T., Daniels, M. J. and Müller, P.Journal of Computational and Graphical Statistics, 29(1), 1–12, 2020
TreeClone: Reconstruction of Tumor Subclone Phylogeny Based on Mutation Pairs Using Next Generation Sequencing DataZhou, T., Sengupta, S., Müller, P. and Ji, Y.Annals of Applied Statistics, 13(2), 874–899, 2019
PairClone: A Bayesian Subclone Caller Based on Mutation PairsZhou, T., Müller, P., Sengupta, S. and Ji, Y.Journal of the Royal Statistical Society: Series C (Applied Statistics), 68(3), 705–725, 2019