Francesca Dominici
Kempner Faculty Steering Committee
Clarence James Gamble Professor of Biostatistics, Population, and Data Science
She/Her
Contact Information
About
Francesca Dominici, PhD is the Director of the Harvard Data Science Initiative at Harvard University, the Clarence James Gamble Professor of Biostatistics, Population and Data Science at the Harvard T.H. Chan School of Public Health, and Co-Editor in Chief of the Harvard Data Science Review. She is an elected member of the National Academy of Medicine and of the International Society of Mathematical Statistics. Dr. Dominici leads an interdisciplinary group of scientists to address important questions in environmental health science, climate change, and health policy. Her contributions to the field have been remarkable, including more than 250 peer-reviewed published articles; and she has provided her knowledge on joint panels with New Jersey Senator Cory Booker and the European Commission.
Dr. Dominici has provided the scientific community and policy makers with comprehensive and compelling evidence on the adverse health effects of air pollution, noise pollution, and climate change, with her studies directly and routinely impacting air quality policy. Dr. Dominici was recognized in Thomson Reuter’s 2019 list of the most highly cited researchers—ranking in the top 1% of cited scientists in her field. Her work has been covered by The New York Times, the Los Angeles Times, BBC, The Guardian, CNN, and NPR. In April 2020, she was awarded the Karl E. Peace Award for Outstanding Statistical Contributions for the Betterment of Society by the American Statistical Association. She is an advocate for the career advancement of women faculty, and her work on the Johns Hopkins University Committee on the Status of Women earned her the campus Diversity Recognition Award in 2009. At the Harvard T.H. Chan School of Public Health, she has led the Committee for the Advancement of Women Faculty.
Research Focus
Dr. Dominici’s current research focuses on causal inference, machine learning, Bayesian statistics, national epidemiological studies of health effects of air pollution, and climate change and health.
