Lily Koffman

Lily Koffman is a biostatistician who develops methods to extract meaningful insights from massive and messy data generated by wearable devices and clinical monitoring systems.

Koffman’s research focuses on making high-resolution health data useful for public health research and clinical interventions. This involves creating scalable approaches for identifying individuals from their walking patterns (walking fingerprinting), rigorously evaluating step counting algorithms in large epidemiologic studies, and developing methods for analyzing hemodynamic data collected during cardiac surgery. Her work sits at the intersection of functional data analysis, machine learning, and statistical modeling, and emphasizes building efficient, open-source pipelines designed for broad use.

Koffman holds a Ph.D. in Biostatistics from Johns Hopkins University, a Master of Science in Biostatistics from the Harvard T.H. Chan School of Public Health, and an A.B. in Statistics from Harvard University.