Booil Jo, PhD, is Associate Professor of Biostatistics at the Department of Psychiatry and Behavioral Sciences at Stanford University School of Medicine. She has been at the lead in developing pragmatic statistical methods based on the intersection of causal inference and latent variable modeling. She has published on various methodological topics such as treatment noncompliance, handling of nested data such as from cluster randomized trials, causal mediation, missing data, propensity scores, and longitudinal heterogeneity. Her current program of research is focused on developing statistical methods that jointly utilize latent variable modeling, causal inference, and statistical learning with the goal of advancing the field of personalized medicine. She is also actively involved in biostatics education and collaborative work in various fields of psychiatry/mental health research. She has been a leading member of American Statistical Association and Prevention Science Methodology group.