Name in Chinese: 吴振科 . Pronounced: “Jen-Kuh Wu”.
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.
I collaborate closely with
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.
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.
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!
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.
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.