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. Zee's statistical methods research includes topics in survival analysis, measurement error, observational data methods, and machine learning. Her clinical research is focused on kidney disease, specifically in clinical and pathology markers of glomerular and chronic kidney disease progression.
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.
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.
Where Discovery Leads is a multimedia storytelling project that delves into key research themes at CHOP Research Institute. This is part one of a three-part series that focuses on novel diagnostic tools and approaches being developed under the leadership of the Center for Autism Research at CHOP. See part 2 and part 3 of the series.
Automated programs can identify which sick infants in a neonatal intensive care unit (NICU) have sepsis hours before clinicians recognize the life-threatening condition. A team of data researchers and physician-scientists tested machine-learning models in a NICU population, drawing only on routinely collected data available in electronic health records (EHRs).