08 Dec 2023 by Zhenke Wu

    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.

    28 Aug 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!

    15 Jun 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!

    18 May 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.

    28 Apr 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!

    09 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.

    29 Mar 2023 by Zhenke Wu

    Congratulations to PhD student Mengbing Li who has been selected to receive an Institute of Mathematical Statistics Hannan Graduate Student Travel Award. She will be highlighted in the upcoming IMS bulletin, social media pages, and during IMS presidential address at JSM in Toronto. Congrats, Mengbing!

    Media coverage
    27 Mar 2023 by Zhenke Wu

    Washington Post, USA Today, CNN, New York Times, The Guardian covered our work on quantifing how many UTI’s in the US are likely from meat people consumed/came in contact with. For this, we developed a statistical method that combine phylogenetics and Bayesian latent class models for mobile genetic elements.

    21 Mar 2023 by Zhenke Wu

    Congratulations to Mengbing Li for winning 2023 IBS ENAR Poster Award Competition!

    02 Jan 2023 by Zhenke Wu

    Congratulations to Hera Shi (co-advisor: Walter Dempsey) for receiving a competitive 14th ICHPS student paper award for her work.

    congrats to students
    02 May 2022 by Zhenke Wu

    Congratulations to Tsung-Hung Yao (co-advisor: Veera Baladandayuthapani) for receiving a competitive travel award to 2022 International Society of Bayesian Analysis (ISBA), Montreal. This is based on his work “Probabilistic Learning of Treatment Trees in Cancer”.

    Congratulations to Hera Shi (co-advisor: Walter Dempsey) for receiving a competitive Junior Researcher Travel Grant to attend 2022 American Causal Inference Conference (ACIC) at Berkeley, CA. This is based on her work “Assessing Time-Varying Causal Effect Moderation in the Presence of Cluster-Level Treatment Effect Heterogeneity”.

    baker on CRAN!
    02 Feb 2022 by Zhenke Wu

    baker has a first public release (v1.0.0) at CRAN! Discussion related to version 1.0.0 can be submitted to here; [vignette] [source code].

    The definitive reference for baker R package can be found here.

    This vignette describes and illustrates the functionality of the baker R package. The package provides a suite of nested partially-latent class models (NPLCM) for multivariate binary responses that are observed under a case-control design. The baker package allows researchers to flexibly estimate population- and individual-level class distributions that may also depend on additional explanatory covariates.

    Functions in baker implement recent methodological developments in our group (here, here, and here). Estimation is accomplished by calling a cross-platform automatic Bayesian inference software JAGS through a wrapper R function that parses model specifications and data inputs. The baker package provides many useful features, including data ingestion, exploratory data analyses, model diagnostics, extensive plotting and visualization options, catalyzing vital communications between practitioners and domain scientists. Package features and workflows are illustrated using simulated and real data sets.

    The focus of this document is on guiding a new user to utilize some useful functions in baker for simulation studies and data analyses, aided by other powerful R packages. We refer readers of this document to the accompanying main software paper for more details about the software design considerations and review of model formulations. Since baker’s first appearance on Github, the authors have not been able to track other recent substantive publications that have used this package; we hope the main software paper and this vignette serve as the definitive reference for future scientific studies that find the baker package useful.

    Newly funded data science project
    28 May 2021 by Zhenke Wu

    An exciting new collaboration is supported by 2021 Propelling Original Data Science (PODS) grant from Michigan Institute for Data Science (MIDAS). The project title is “Structured Latent Variable Methods for High-Dimensional Electronic Health Records and Administrative Claims Data”. The faculty investigators are Zhenke Wu (PI; Biostatistics, Public Health), Jordan Schaefer (Co-I; Hematology, Internal Medicine), and Andrew Ryan (Co-I; Health Management and Policy, Public Health). The primary data source will be based on UnitedHealthcare OptumInsight claims data via Data and Methods Hub (DMH) at UM Institute for Healthcare Policy and Innovation (IHPI).

    Congrats to Irena and Hera
    01 Sep 2020 by Zhenke Wu

    Huge congrats to Irena and Hera for passing their PhD qualifying exams!!

    Congrats to Mengbing and Irena
    15 Nov 2019 by Zhenke Wu

    Huge congrats to Mengbing for winning “Best Doctoral Qualifying Exam Award”.

    Another huge congrats to Irena for winning a poster award today in the 2019 MIDAS Annual Symposium in the category of “Most Likely to Make an Impact in the Field” for her poster “Regression Analysis for Probabilistic Cause-of-disease Assignment using Case-control Diagnostic Tests: A Hierarchical Bayesian Approach”!

    Way to go!!

    PERCH Main Results out in The Lancet
    28 Jun 2019 by Zhenke Wu

    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.

    Congrats to Tim and Mengbing
    13 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!

    U of Michigan Precision Health Investigator Award
    24 Oct 2018 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.

    Michigan Student Symposium for Interdisciplinary Statistical Sciences
    04 Apr 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.

    Electronic Health Records Projects
    08 Aug 2017 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!

    Our team receives Data Science Challenge Initiative award in mobile health analytics
    09 Apr 2017 by Zhenke Wu

    Frequent interactions of individuals with mobile devices have opened new doors to behavioral and mental health research. The real-time individual-level data streams unleashed by mobile technologies have greatly improved our potential to understanding behaviors and improving health. For example, mobile technologies can capture the opportunistic windows during the day for maintaining healthy behaviors, push actionable suggestion messages, and help individuals develop and maintain long-term changes beneficial to their health. For the current project, we hope the privacy-protected data will ultimately refine our understanding of how life stress leads to depression and hence transform our ability to prevent and treat depression.

    See the project award notice for more information about our project titled Identifying Real-Time Data Predictors of Stress and Depression Using Mobile Technology.

    Statisticians Impacting Global Public Health
    24 Feb 2017 by Zhenke Wu

    There are many statistical innovations going on in Global Health. Here is the newsletter pointing to some ongoing research activities and consulting services available at Global Pubic Health, Michigan.

    Teaching a special topics course about graphical models
    21 Sep 2016 by Zhenke Wu

    I am now teaching a PhD-level special topics course: BIOSTAT830 Statistical and Computational Methods for Learning Through Graphical Models, which will cover representation, inference, learning and causality demonstrated by case studies on real problems. Feel free to get what you need or comment on the course webpage.

    Will join U of Michigan Biostat and MIDAS
    05 Jul 2016 by Zhenke Wu

    I will join Department of Biostatistics at Univeristy of Michigan as Assistant Professor, with joint appointment at Michigan Institute for Data Science (MIDAS), starting from September 1st, 2016.

    Visualization for Individualized Health
    11 May 2016 by Zhenke Wu

    As part of their final project for Data Visualization for Individualized Health, Audrey Garman, Bengucan Gunen, Ruthe Huang, and Marcus Spearman, majored in public health studies, have built an excellent childhood pneumonia prediction visualization tool using Shiny. See the Hopkins inHealth and the HUB stories.

    06 Oct 2015 by Zhenke Wu

    Theory and experiment, the pillars of 19-20th century science, have been joined by computation and big data in the 21st.

    I am always looking for people who take a quantitative approach to studying public health, biology and biomedicine. Particular emphasis is placed on scalable Bayesian (graphical) modeling, latent variables, causal inference and especially their intersection. If you think you may be interested, please get in touch.

    I’d be happy to answer questions on research and software!