Results for Statistical Machine Learning

Dr. Obstfeld’s research focuses on utilizing clinical and pre-clinical laboratory data sets for predicting diagnosis and prognosis using statistical and machine learning techniques.

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.

Dr. Campbell is interested in approaches to improve diagnosis of children with rare genetic diseases. He also focuses on better understanding of the full spectrum of symptoms associated with genetic diseases as well as discovering new diseases.