Dr. Parish-Morris investigates social communication, specifically how vocal communication develops in children and adolescents with autism spectrum disorder. She uses computational approaches and machine learning to identify objective and reliable behavioral markers for use in screening, treatment and intervention response tracking, and to advance biological research.
Whitney Guthrie, PhD, is a clinical psychologist and scientist at the Center for Autism Research. Dr. Guthrie’s research focuses on the early developmental trajectories that characterize autism spectrum disorder with the ultimate goal of improving early detection and intervention.
Dr. Wallis explores socio-demographic disparities in the diagnosis of developmental disorders and autism spectrum disorder (ASD), and the process of screening for these conditions in pediatric primary care. She aims to develop and test strategies to improve developmental outcomes for all children and to bridge gaps in identification and care for low-income and minority children and girls with developmental delays and autism spectrum disorder.
Dr. Schultz's research involves using magnetic resonance imaging to understand brain mechanisms and to create biomarkers that predict who has autism spectrum disorder (ASD), who will develop the disorder, and who will respond well to different interventions. More recently, he has developed a technology and innovation lab to exploit advances in perceptual computing, in order to develop more robust measurements of quantitative traits.
Dr. Miller's research focuses on the diagnostic and classification issues most pressing to autism spectrum disorder (ASD) research, including differentiating ASD from other genetic and psychiatric conditions, diagnosis across the lifespan, and early identification and screening.
Dr. Shults works to develop statistical methods for longitudinal data that include semi-parametric approaches to account for subject/cluster level associations and maximum likelihood-based approaches for simulation and analysis of discrete longitudinal outcomes that may have overdispersion.