Zhenke Wu Research

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

    [Publications] [CV-pdf] [one-page CV pdf] [CV-overleaf]

    [Google Scholar] [GitHub]

    [][Contact] [Bio]

    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:

    Tagline: AI for affordable and equitable individualized healthcare; computational and interventional digital health.

    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. I am also exploring the intersection between generative AI and biostatistics.

    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.


    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.

    Working Group:

    I collaborate closely with

    Published 16 Jul 2024
    Published 16 Jul 2024
    Published 06 May 2024
    Dynamic Statistical Inference in Massive Datastreams
    Wang et al (2024). Statistica Sinica
    Published 02 Apr 2024
    Published 25 Dec 2023
    Published 30 Nov 2023
    Published 17 Nov 2023
    Published 07 Nov 2023
    Published 07 Nov 2023
    Published 26 Oct 2023

    Congratulations to Mengbing Li for winning a 2024 ENAR Distinguished Student Paper Award for “Tree-Regularized Bayesian Latent Class Analysis for Improving Weakly Separated Dietary Pattern Subtyping in Small-Sized Subpopulation”. See paper on arXiv; R package ddtlcm on CRAN; Graphical user interface on Shinyapp; Software paper on arXiv.

    Posted 08 Dec 2023 by Zhenke Wu

    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
    Posted 28 Mar 2024 by Zhenke Wu
    Posted 16 Dec 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