Zhenke Wu Research

Name in Chinese: 吴振科 . Transliteration: “Jen-Kuh Wu”.

Here are my publication samples. My CV is here. My contact info is here. My GitHub is here.

The best way to contact me is email or mobile. Direction to my office is here.


My main research interests include:


I am a Assistant Professor in the Department of Biostatistics at University of Michigan, with joint appointment as Research Assistant Professor in Michigan Institute for Data Science (MIDAS).

I conduct research on the design and application of statistical methods that inform health decisions made by individuals, or precision medicine. My current focus is on latent variable and causal inference methods that can support disease etiology studies, medical diagnosis, and health policy evaluation. Broadly, the statistical goal is to discover simple latent structures that improve inferences and population parameters and individual latent states. I have also worked on causal inference methods 1) to evaluate novel treatment rules under special designs like matched-pair cluster randomized design, as these designs are useful for interventions that can only be applied at cluster level; and 2) to facilitate the inference for novel estimands in semiparametric models by automating and unifying the derivation of efficient influence functions (EIF) and ensuing estimation.

Currently a major focus of my work is on the analysis of multiple mixed-type longitudinal measurements with feedbacks in treatment assignments. I am working on hierarchical Bayesian methods to infer latent trajectories that represent individual disease progressions that have direct applications to childhood pneumonia etiology studies, disease surveillance and just-in-time adaptive interventions (JITAI).

I collaborate closely with



Frequent interactions of individuals with mobile devices have opened new doors to behavioral and mental health research. The real-time individual-level data streams unleashed by mobile technologies have greatly improved our potential to understanding behaviors and improving health. For example, mobile technologies can capture the opportunistic windows during the day for maintaining healthy behaviors, push actionable suggestion messages, and help individuals develop and maintain long-term changes beneficial to their health. For the current project, we hope the privacy-protected data will ultimately refine our understanding of how life stress leads to depression and hence transform our ability to prevent and treat depression.

See the project award notice for more information about our project titled Identifying Real-Time Data Predictors of Stress and Depression Using Mobile Technology.

Posted 09 Apr 2017 by Zhenke Wu

There are many statistical innovations going on in Global Health. Here is the newsletter pointing to some ongoing research activities and consulting services available at Global Pubic Health, Michigan.

Posted 24 Feb 2017 by Zhenke Wu

I am now teaching a PhD-level special topics course: BIOSTAT830 Statistical and Computational Methods for Learning Through Graphical Models, which will cover representation, inference, learning and causality demonstrated by case studies on real problems. Feel free to get what you need or comment on the course webpage.

Posted 21 Sep 2016 by Zhenke Wu

I will join Department of Biostatistics at Univeristy of Michigan as Assistant Professor, with joint appointment at Michigan Institute for Data Science (MIDAS), starting from September 1st, 2016.

Posted 05 Jul 2016 by Zhenke Wu

As part of their final project for Data Visualization for Individualized Health, Audrey Garman, Bengucan Gunen, Ruthe Huang, and Marcus Spearman, majored in public health studies, have built an excellent childhood pneumonia prediction visualization tool using Shiny. See the Hopkins inHealth and the HUB stories.

Posted 11 May 2016 by Zhenke Wu
Testing MathJax
This blog tests math compatibility on this site
Posted 01 Nov 2015 by Zhenke Wu