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Miguel Hernan is a leading scholar in the field of biostatistics and epidemiology, known for his work in causal inference and comparative effectiveness research. He holds a Kolkotrones Professorship at the Harvard T.H. Chan School of Public Health. Dr. Hernan's research is particularly focused on the application of data science methodologies to improve understanding of causal relationships in health data. With degrees including a Doctor of Public Health and a Master of Science in Biostatistics from Harvard University, he has contributed significantly to the advancement of statistical methods in epidemiology. His work spans the design and analysis of randomized trials, as well as the use of observational data for health policy decision-making. Dr. Hernan has also led the Causa Lab, which aims to harness innovative causal inference techniques for public health research.
Harvard T.H. Chan School of Public Health • Cambridge, MA
Chair of the Department of Biostatistics and Epidemiology.
The listed clinical and basic science departments (Radiology, Medicine, Genetics, etc.) participate in the PhD training through the Harvard Division of Medical Sciences (DMS) and the Harvard-MIT Health Sciences and Technology (HST) program. Requirements are standardized across these interdisciplinary PhD umbrellas.