Zhenke Wu, PhD

Zhenke Wu Research

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

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 and AI in Society (MIDAS).

Research Theme:

Tagline: AI for affordable and 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.

Keywords:

Statistical: Hierarchical Bayesian models; Latent variable models; Nonparametric Bayes; Bayesian scalable computation; Causal inference; Reinforcement learning.
Substantive: Precision medicine; Wearable device data; Mobile health; Infectious diseases dynamics; Mental health; Electronic health records/claims data; Healthcare policy; Clinical trials; Just-in-time adaptive interventions for behaviorial and psychiatric research; Computational Social Science.

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.

Please consider providing relevant information via Google Form here.
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:

Michigan Statistics for Individualized-healthcare Lab (MiSIL) weekly meeting schedules

I currently collaborate closely with