Dr. Wu’s 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, he focuses on two lines of methodological research: a) structured Bayesian latent variable models for clustering and disease subtyping, and b) study design and causal methods for evaluating sequential interventions that tailor to individuals’ changing circumstances, such as in mobile health studies. He is 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.