Dr. Lina Montoya is a postdoctoral fellow at the University of North Carolina, Chapel Hill (supervisor: Dr. Michael Kosorok) and the University of California, Berkeley (supervisor: Dr. Jennifer Skeem). Her methodological research is at the intersection of causal inference, statistics, and machine learning to develop ways of estimating, evaluating, and implementing optimal dynamic treatment rules, i.e., rules that determine which interventions work best for which patients.
Her applied research focuses on uncovering such heterogeneous treatment effects for 1) interventions aimed at increasing care retention among individuals with HIV in East Africa; and 2) interventions for reducing recidivism among justice-involved adults with mental illness in the United States.
