Name in Chinese: 吴振科 . Pronounced: “Jen-Kuh Wu”.
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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.
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.
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.
I collaborate closely with
D3 Lab: Data Science for Dynamic Intervention Decision-Making Lab
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.
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!
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.