AI: Solution or Obstacle for Healthy Adaptation to Extreme Weather Events?
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Francesca Dominici, PhD
Clarence James Gamble Professor of Biostatistics, Population, and Data Science
Biostatistics, Harvard T.H. Chan School of Public Health
Francesca Dominici, PhD is the Director of the Harvard Data Science Initiative at Harvard University and the Clarence James Gamble Professor of Biostatistics, Population, and Data Science at the Harvard T.H. Chan School of Public Health. In 2024, she made the TIME100Health list for 2024: TIME100 Most Influential People in Global Health.
She is an elected member of the National Academy of Medicine and of the International Society of Mathematical Statistics. She leads an interdisciplinary group of scientists to address important questions in environmental health science, climate change, and health policy. She has published over 300 peer-reviewed articles and provided her knowledge on the topics on joint panels with New Jersey Senator Cory Booker and the European Commission). Dr. Dominici has provided the scientific community and policymakers with comprehensive and compelling evidence on the adverse health effects of air pollution, noise pollution, and climate change. Her studies have directly and routinely impacted 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. The New York Times, the Los Angeles Times, BBC, the Guardian, CNN, and NPR have covered her work. 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 advocates 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. She has led the Committee for the Advancement of Women Faculty at the Harvard T.H. Chan School of Public Health.
Summary
Artificial Intelligence is revolutionizing research, education, and business—unlocking unprecedented opportunities across the environment, health, and beyond. In our lab, we’re developing the first foundation model for healthy adaptation to extreme weather events, pre-trained on the complete US Medicare dataset and enriched with nationwide Census, weather, and pollution data. This model enables powerful “what-if” scenario forecasting, using synthetic ground-truth data to validate counterfactual predictions and guide impactful climate actions.
Yet, this AI-driven progress comes at a cost: energy-hungry data centers power these advances, raising concerns about their environmental footprint and the paradox of AI’s growing electricity demand in a world striving to reduce fossil fuel reliance. In this talk, I’ll share insights from our work and explore AI's uncertain, double-edged role in the fight against climate change.
Learning Objectives:
- The study designs to assess the health effects of air pollution
- The definition of a AI foundation model and examples
- The main data science tools that can be implemented to estimate the health effects of air pollution and climate change related exposures (e.g. heat waves)
- The approach to estimate the environmental and health impacts of data centers
This page was last updated on Tuesday, March 31, 2026