Name in Chinese: 吴振科 . Pronounced: “Jen-Kuh Wu”.
[Publications] [CV-overleaf] [CV-pdf] [Contact] [Bio]
[Google Scholar] [GitHub] [X].
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:
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.
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; 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.
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 ($74,000 annually for 2022-23) plus benefits. Travel to funder’s AI in Science events will also be covered.
Working Group:
I collaborate closely with
D3 Lab: Data Science for Dynamic Intervention Decision-Making Lab
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!
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!
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.
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!
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.