Affiliation
Associate Professor, Johns Hopkins Bloomberg School of Public Health
Education
PhD, Biostatistics, Harvard University
MS, Statistical Genetics, Columbia Mailman School of Public Health
AB, Philosophy, Mathematics, Harvard University
Elizabeth Ogburn grew up in the Boston suburbs and always liked school and math. Family legend has it that, from a young age, her life plan was to stay in school forever.
In college, Ogburn studied philosophy and math. She loved reading, writing, and discussing philosophy and seriously considered applying to PhD programs in philosophy, but ultimately realized she would be happier with a more collaborative and applied career. Her interests skew abstract and esoteric, and she wanted to find a field that would keep her grounded and ensure that her research would be useful. It took Ogburn several years to figure out what that should be. Over summers and for two years after college, she dabbled in different academic research assistant positions: She tried doing bench science, epidemiology, and applied data analysis but nothing stuck until she attended a talk about causal inference and quickly realized that was the perfect fit. Shortly after attending that talk, she applied to biostatistics PhD programs and never looked back.
In graduate school, Ogburn worked on several disparate projects but never entirely found her footing. She was fortunate to get a postdoc position that afforded her complete freedom to learn new areas and develop an independent research program, and she spent at least half of her two-year postdoc reading, learning, and thinking about social networks and non-Euclidean statistical dependence. She wrote little during that time but articulated research questions that carried her through at least the first five years of being faculty. Knowing what she now knows about the life of an assistant professor, she is not sure how she would have developed a comparable research program without that experience. Ogburn’s postdoc was part of a vibrant causal inference group and she is still assimilating some of the innumerable lessons she learned from the other faculty, postdocs, and students.
After Ogburn’s postdoc, she joined the biostatistics department at Johns Hopkins, where she is part of a close-knit and intellectually diverse department—with talented and passionate students—and another vibrant causal inference group spanning computer science, epidemiology, and biostatistics. Being in a school of public health provides the opportunities for impactful and fulfilling research and collaborations she had hoped for when she was at a crossroads after college. She also loves being part of the broader causal inference community, with its connections to social science, computer science, theoretical and applied statistics, and, of course, public health.
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