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.
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. 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. Tunç is a computational scientist investigating the application of machine learning and statistical data analysis in various domains such as digital phenotyping, nature of psychopathology, and neuroimaging. He participates in studies using normative, developmental, and clinical samples to parse heterogeneity in psychiatric disorders by developing novel computational techniques.
Dr. Roberts investigates brain-wave scanning with magnetoencephalography (MEG) and works to identify biomarkers for neuropsychiatric disorders like autism. Those biomarkers are for diagnosis, prognosis, stratification, and response monitoring as well as substrate identification for targeted therapy. Putting the "bio" into biomarkers is a major emphasis of Dr. Roberts' research, for which he uses advanced diffusion magnetic resonance imaging (MRI) and edited spectroscopy.