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
Semiparametric Bayesian Inference for the Transmission Dynamics of COVID-19 with a State-Space ModelContemporary Clinical Trials, 97, No. 106146, 2020
Emerging Methods for Oncology Clinical TrialsCHANCE, 33(3), 39–48, 2020
RNDClone: Tumor Subclone Reconstruction Based on Integrating DNA and RNA Sequence DataAnnals of Applied Statistics, forthcoming, 2020
RoBoT: A Robust Bayesian Hypothesis Testing Method for Basket TrialsBiostatistics, forthcoming, 2020
PoD-TPI: Probability-of-Decision Toxicity Probability Interval Design to Accelerate Phase I TrialsStatistics in Biosciences, 12, 124–145, 2020
A Semiparametric Bayesian Approach to Dropout in Longitudinal Studies with Auxiliary CovariatesJournal of Computational and Graphical Statistics, 29(1), 1–12, 2020
TreeClone: Reconstruction of Tumor Subclone Phylogeny Based on Mutation Pairs Using Next Generation Sequencing DataAnnals of Applied Statistics, 13(2), 874–899, 2019
PairClone: A Bayesian Subclone Caller Based on Mutation PairsJournal of the Royal Statistical Society: Series C (Applied Statistics), 68(3), 705–725, 2019
