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Eric Polley is an expert in the field of Public Health Sciences, focusing extensively on Machine Learning, clinical trial design, and risk prediction. His research explores the application of advanced statistical methodologies to enhance clinical trial outcomes and improve prediction models in healthcare. He has contributed significantly to the understanding of genetic predisposition in various cancers, particularly breast cancer, by employing polygenic risk scoring systems. His work often combines population genetics with advanced statistical learning techniques to derive insights that drive public health initiatives and improve patient care. Polley has authored numerous peer-reviewed articles, contributing to the academic community's understanding of complex health-related issues. Additionally, he is involved in the development of innovative models that integrate large-scale genomic data to facilitate better decision-making in clinical settings.
University of Chicago • Chicago, IL
Teaching and conducting research in Public Health Sciences with a focus on Machine Learning and clinical data analysis.
Department of Philosophy