Tianchen Qian is an Assistant Professor in the Department of Statistics at the University of California, Irvine. His research develops statistical methods and software for designing, analyzing, and optimizing adaptive digital interventions. A central focus of his work is the micro-randomized trial (MRT), an experimental design in which participants are repeatedly randomized to intervention options. He develops causal inference and semiparametric methods to learn whether, when, and for whom intervention components work, how their effects evolve over time, and how short-term behavioral changes contribute to longer-term health outcomes.

His work also addresses trial design, sample size, missing data, treatment effect heterogeneity, and policy learning. He collaborates with behavioral and health scientists on digital interventions targeting physical activity, substance use, smoking cessation, weight management, drinking behavior, and anger management. His broader research interests include longitudinal data, clinical trials, and Alzheimer’s disease and related dementias. Before joining UC Irvine, he completed postdoctoral training in Statistics at Harvard University and earned his PhD in Biostatistics from Johns Hopkins University.