Mission: To increase the clinical impact of research by targeting key mechanisms and individual needs.
My deep interest in the complex presentation and course of psychopathology arose from clinical work with populations disproportionately impacted by trauma. Psychiatric comorbidity is the norm and symptom profiles are highly heterogeneous. This underlies the challenge of answering: What mechanisms should treatments target? How can we target interventions to the specific needs of individuals?
The Targeted Interventions Lab seeks to address these challenges by developing targeted prevention and intervention strategies for mental health outcomes including addiction, anxiety, and stress-related psychopathology.
Current Research Priorities
Predictive Modeling to Facilitate Targeted Intervention Strategies | This line of research applies machine learning methods to develop models that predict suicide, PTSD, and other adverse psychiatric outcomes at critical intervention points. By identifying individuals at high-risk for these outcomes, such models may facilitate development and testing of targeted prevention or early intervention strategies.
Development of Interventions that Target Transdiagnostic Mechanisms | This line of research applies a translational approach to understand transdiagnostic mechanisms of anxiety, stress, and addiction, and leverages technology to intervene on these mechanisms. Virtual reality and mobile health technology are used to target mechanisms more precisely with the aim of enhancing treatment effects while increasing the efficiency and accessibility of interventions.
Integrating Genomic Data to Estimate Risk for Adverse Psychiatric Outcomes | This line of research seeks to enhance the clinical potential of polygenic risk scores from large GWAS studies by applying statistical methods that incorporate complex mixtures of exposures and outcomes.
Rigorous Methods for Causal Inference | Across these research priorities, we use rigorous methods including randomized controlled design (including large cluster-randomized multi-site trials) and doubly-robust statistical approaches that leverage machine learning to estimate intervention effects with observational data. We strive to maximize open science practices including pre-registration and data sharing policies that benefit researchers and protect participant privacy.
