Zhenke Wu Research

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

Here are my publication samples. My CV is here, contact info is here. My GitHub is here and Bio is here. A recent faculty profile is here.

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


I am an 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 am also Faculty Associate in Quantitative Methodology Program, Survey Research Center of Institute for Social Research (ISR), University of Michigan.


I am interested in 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).


Advising: We are recruiting motivated and hard-working students interested in Bayesian methods and computation, graphical models and large-scale health data analytics. If you are interested in joining the group, please apply to Biostatistics at the University of Michigan, Ann Arbor. If you are an undergrad or grad student at the University of Michigan, and you are interested in any of the papers or projects listed on this website, send me an email with your interests and CV.

Statistical Learning and Computing Reading Group, Winter 2019


Research interests:


I collaborate closely with



Dynamic Tracking and Screening in Massive Datastreams
Wang et al (2019+). Revision Submitted
Published 19 Aug 2019
Published 28 Jun 2019
Published 15 Jun 2019
Published 13 Jun 2019
Published 10 Jun 2019
Published 24 Aug 2018
Micro-Randomized Trial
Xu, Wu and Murphy (2018). Wiley StatsRef Statistics Reference Online
Published 18 May 2018
Published 21 Oct 2017
Published 30 May 2017
Published 18 Apr 2017

The Main PERCH paper appeared in The Lancet today. We developed novel integrative Bayesian methods here, here, here, and more recently, here. Also check out the open-source R pacakge baker.

The visualization of PERCH results can be found here.

A press release is here.

Posted 28 Jun 2019 by Zhenke Wu

Huge congrats to Tim for having successfully defended his thesis on sequential randomized trials motivated by mental health and online education problems!

Another huge congrats to Mengbing for finishing her MS degree in biostatistics!

Previliged to have worked with you together in the past two years. Way to go!

Posted 13 Jun 2019 by Zhenke Wu

Our two-year project “Bayesian Hierarchical Models for Using Mobile Technology to Individualize Care in Mental Health” is funded by Precision Health at University of Michgian for developing analytical capacities and software tools suitable for mobile data streams. It is motivated by an ongoing multi-institution study of depression among first year medical interns.

Posted 24 Oct 2018 by Zhenke Wu

Tim Necamp won the Best Speed Oral Presentation at MSSISS 2018 for his work with Intern Health Study. His poster is entitled Predicting mood using multivariate mobile sensor data streams for medical interns. I also had great fun delivering junior faculty keynote talk titled Bayesian Hierarchical Methods to Power Disease Discovery and Improve Clinical Decisions. Thanks for the fantastic student organizing committee and faculty advisory committee from Biostat, EECS, IOE, Stat and Survey Methodology for showcasing the diverse statistical/data science work at Michigan.

Posted 04 Apr 2018 by Zhenke Wu

For six weeks, 44 undergraduate students from across the country met at Ann Arbor to wrestle with big data. The hands-on projects, in addition to lectures about statistics, informatics and professional development, are part of their training at 2017 Big Data Summer Institute hosted by our department. Among them, eleven students learned and applied methods to analyzing the Electronic Health Records (EHR) data. In four groups, they created their own data sets from Michigan Genomics Initiative that contains genomic information, longitudinal diagnoses, procedures and lab measurements and more to investigate heart failure, phenomewide association for lab values, Type 2 Diabetes and infectious diseases. It was great pleasure working with you!

Posted 08 Aug 2017 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