Zhenke Wu

Assistant Professor of Biostatistics

University of Michigan
Google Scholar


Zhenke Wu’s research involves the development of statistical methods that inform health decisions made by individuals. He is particularly interested in scalable Bayesian methods that integrate multiple sources of evidence, with a focus on hierarchical latent variable modeling. We have applied our methods to estimate the etiology of childhood pneumonia, autoantibody signatures for subsetting autoimmune disease patients and to predict whether a user is engaged with mobile applications.

Zhenke has developed original methods and software that are now used by investigators from research institutes such as US CDC and Johns Hopkins, as well as site investigators from developing countries, e.g., Kenya, South Africa, Gambia, Mali, Zambia, Thailand and Bangladesh.

Zhenke completed a BS in Math at Fudan University in 2009 and a PhD in Biostatistics from the Johns Hopkins University in 2014 and then stayed at Hopkins for his postdoctoral training. Since 2016, Zhenke is Assistant Professor of Biostatistics, and Research Assistant Professor in Michigan Institute for Data Science (MIDAS) at University of Michigan, Ann Arbor.


Department of Biostatistics
University of Michigan
1415 Washington Heights
4623 SPH-I (within Suite 4605)
Ann Arbor, MI 48109

Direction to my office: [.pdf]


Association of Polygenic Risk Scores for Multiple Cancers in a Phenome-wide Study Results from The Michigan Genomics Initiative

Bayesian Estimation of Pneumonia Etiology: Epidemiologic Considerations and Applications to the Pneumonia Etiology Research for Child Health Study

Estimating AutoAntibody Signatures to Detect Autoimmune Disease Patient Subsets

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer; development of a prognostic model through a crowdsourced challenge with open clinical data

Predicting Survival Time for Metastatic Castration Resistant Prostate Cancer; An Iterative Imputation Approach

Nested Partially-Latent Class Models for Dependent Binary Data; Estimating Disease Etiology

Rejoinder to "Deductive Derivation and Turing-Computerization of Semiparametric Efficient Estimation"

Deductive Derivation and Turing-Computerization of Semiparametric Efficient Estimation

Partially Latent Class Models for Case–Control Studies of Childhood Pneumonia Aetiology

Estimation of Treatment Effects in Matched-Pair Cluster Randomized Trials by Calibrating Covariate Imbalance between Clusters

Lack of Response after Initial Chemoembolization for Hepatocellular Carcinoma: Does It Predict Failure of Subsequent Treatment?


Testing MathJax