Advances in genetics, molecular biology, and cognitive neuroscience offer hope for personalized treatment and improved outcomes in those with autism spectrum disorder (ASD). However, the promise of precision medicine is limited by a lack of mechanistic models that explain phenotypic and etiological heterogeneity; instead of using such models to identify subgroups likely to respond to specific treatments, the field relies on service availability, trial-and-error, and clinical judgment to make treatment decisions. In line with the computational psychiatry objective, my research integrates mathematical models of behavior and brain activity to establish neurocognitive models that can successfully predict individual social and nonsocial learning profiles. Specifically, I am formally comparing the suitability of various computational models to capture selective deficits in social learning of individuals with ASD, as well as variability in both social and nonsocial learning across typically developing youth and those with ASD. Identifying how these model-based predictions are implemented in the brain will allow us to identify neural architecture underlying learning in therapeutically relevant contexts. The long-term goal of this research line is to apply these computational models to inform, refine, and individualize diagnosis, education, and treatment of youth with ASD.
I am an Assistant Professor of cognitive neuroscience in the Psychology department at George Washington University (GWU). I am also affiliated with the Autism and Neurodevelopmental Disorders Institute at GWU. My research combines computational and neuroscientific methods to understand the neurobiological mechanisms underlying learning in neurotypical and clinical populations, especially autism spectrum disorder. I have expertise in designing naturalistic tasks to assess social decision making in behavior and brain function, conducting longitudinal clinical studies, computational modeling and developmental cognitive neuroscience. I have recently been awarded the Bridge to Independence Award by the Simons Foundation for Autism Research to study learning in autism with a computational neuroscientific approach and its implications for treatment.