Zhenke Wu’s research involves the development of statistical methods that inform health decisions made by individuals. He is particularly interested in scalable Bayesian methods that integrate multiple sources of evidence, with a focus on hierarchical latent variable modeling. He also works on sequential decision making by developing new statistical tools for reinforcement learning and micro-randomized trials. He has developed methods to estimate the etiology of childhood pneumonia, cause-of-death distributions using verbal autospy, autoantibody signatures for subsetting autoimmune disease patients, and to estimate time-varying causal effects of mobile prompts upon lagged physical, mental and behavioral health outcomes. Zhenke has developed original methods and software that are now used by investigators from research institutes such as US CDC and Johns Hopkins, as well as site investigators from low- and middle-income countries, e.g., Kenya, South Africa, Gambia, Mali, Zambia, Thailand and Bangladesh. Zhenke completed a BS in Math at Fudan University in 2009 and a PhD in Biostatistics from the Johns Hopkins University in 2014 and then stayed at Hopkins for his postdoctoral training. Since 2016, Zhenke is Assistant Professor of Biostatistics, and Research Assistant Professor in Michigan Institute for Data Science (MIDAS) at University of Michigan, Ann Arbor.