Zhenke Wu Research

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

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The best way to contact me is email (zhenkewu [arroba] umich [punto] edu). Direction to my office is here.

I am an Associate Professor (with tenure) in the Department of Biostatistics at University of Michigan and a faculty affiliate at Michigan Institute for Data Science (MIDAS).

Research Theme:

I am deeply passionate about advancement of modern Bayesian latent variable methods, with a keen focus on developing tools that address pivotal public health challenges faced predominantly by low and middle-income countries (LMIC). My contributions include the development of general methods and the creation of publicly-accessible software tailored for world’s most updated pediatric pneumonia etiology estimates across seven sub-Saharan African and Southeast Asian countries. Additionally, in collaboration with demographers and statisticians, I’ve pioneered domain-adaptive mortality estimation for deaths occurring outside the civil registration and vital statistics systems using computer-coded verbal autopsy. Recently, I’ve been working to advance digital mental health for healthcare workers in Kenya, in collaboration with the Data Science Initiative in Africa. This effort draws upon my expertise in interventional and predictive mobile health, honed through pioneering studies in the US (Intern Health Study - world’s largest multi-year microrandomized trial, and Caregiver Quality of Life Study). I enjoy embracing and navigating the unique challenges presented within the LMIC contexts and seizing the opportunity therein to shape how statistics can effect meaningful change.

My research is motivated by biomedical and public health problems and is centered on the design and application of statistical methods that inform health decisions made by individuals, or precision medicine. Towards this goal, I focus on two lines of methodological research: a) structured Bayesian latent variable models for clustering and disease subtyping, and b) study design, causal and reinforcement learning methods for evaluating sequential interventions that tailor to individuals’ changing circumstances such as in interventional mobile health studies. I am committed to developing robust, scalable, and interpretable statistical methods to harness real-world, high-dimensional, dynamic data for individualized health. The methods and software developed so far have supported studies in diverse scientific fields including infectious disease epidemiology, autoimmune diseases, mental health, behavioral health, and cancer.


Advising: We are recruiting motivated and hard-working people interested in Bayesian methods and computation, graphical models, causal inference, sequential decision making, reinforcement learning and large-scale health data analytics. If you want to get involved, please say hi.

Check this out and send me an email if interested in collaborating!

AI in Science Postdoctoral Fellowship Program; The program will pay a competitive salary plus benefits. Travel to funder’s AI in Science events will also be covered.

Working Group:

I collaborate closely with

Published 30 Nov 2023
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Published 30 Jun 2023

Congratulations to Dr. Tsung-Hung Yao and Dr. Irena Chen for successfully defending their theses! Dr. Yao’s thesis is entitled: “Bayesian Learning of Structured Covariances, with Applications to Cancer Data”; Dr. Chen’s thesis is entitled: “Joint Modeling Methods for Individual-level Variances as Predictors of Health Outcomes”. Good luck to your next stage of careers at MD Anderson Biostatistics and Max Planck!

Posted 28 Aug 2023 by Zhenke Wu

Congratulations to Dr. Jieru Shi for successfully defending her thesis entitled “Statistical Methods for Assessing Time-varying Causal Effects: Novel Estimands and Inference” (Co-Chair with Walter Dempsey). Best wishes to your upcoming postdoctoral studies at University of Cambridge!

Posted 15 Jun 2023 by Zhenke Wu

Thrilled to receive tenure! Heartfelt gratitude to family, students, collaborators, mentors, and reviewers for your help in reaching this milestone. Truly honored to be part of the esteemed umich community.

Posted 18 May 2023 by Zhenke Wu

We are organizing the 2023 ICSA Applied Statistics Symposium, which will be held from Sunday, June 11 to Wednesday, June 14, 2023 in Ann Arbor, Michigan. I am co-charing the local organizing committee. Please consider attending!

Posted 28 Apr 2023 by Zhenke Wu

Congratulations to MS student Abby Loe who has been accepted into doctoral program at Michigan Biostat! She has been working on the intersections between machine learning and classical statistical time-to-event and recurrent event data analysis.

Posted 09 Apr 2023 by Zhenke Wu
Posted 11 Sep 2020 by Zhenke Wu
Posted 11 Jul 2019 by Zhenke Wu
Testing MathJax
This blog tests math compatibility on this site
Posted 01 Nov 2015 by Zhenke Wu