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. Tasian and his research team use an epidemiologic framework, including randomized trials and multi-institutional observational studies, to examine the etiology of kidney stone disease and the comparative effectiveness of surgical interventions. He also employs machine learning of complex data to improve diagnosis, risk stratification, and prediction of treatment response for children and adults with benign urologic disease.
Dr. Pasha studies the mechanism of spinal deformity development in the pediatric population. She uses analytical and computational methods to better understand the pathogenesis of pediatric scoliosis. Her lab develops medical devices and software packages that can be used in orthopedic clinics worldwide.
Dr. Zhou’s outstanding research interests include mitosis-coupled DNA methylation drift and inference of cell-type-specific epigenetic signals. He developed multiple computational tools for analyzing DNA methylation data and has actively contributed to cancer genomics data analysis.
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.
The Arcus team at the Research Institute is solving a number of challenges at once: Decrease the time it takes for researchers to access data, increase the reproducibility of research, ensure data security, and speed up the rate of breakthroughs.