Professor Francesca Dominici
T.H Chan School of Public Health
Harvard University
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.
General Lecture
Tuesday October 29, 2024, 4:00PM to 5:00PM
Title:
Is Artificial Intelligence a Solution or a Challenge in the Fight Against Climate Change?
Abstract:
AI has the potential to transform completely how we conduct research, education, and business. On one hand, the opportunities are endless, e.g., in climate, health, education, and practically all areas of human life. For example. which action may have the most significant impact on climate? Which subpopulations are most vulnerable to the adverse effects of climate-related stressors (e.g., heat waves, wildfires, tropical cyclones)? However, data centers are energy-intensive facilities, with computational power and cooling being the most energy-hungry processes. Depending on the task, data center servers require substantial energy to perform their computations, and this computing process can generate significant heat. Therefore, extensive energy-hungry cooling systems are often needed to avoid overheating of the hardware of computers and maximize their performance, stability, and lifespan, especially in high-performance systems. There is an increasing concern that the explosion of AI and its electricity demand is slowing down the progress of relying less and less on fossil fuel combustion for electricity generation. In this talk, I will provide an overview of the work conducted in my lab, hoping to shed some light on the controversial role of AI in the fight against climate change.
Technical Lecture
Wednesday, October 30, 2024, 4:00PM to 5:00PM
Title:
Bayesian causal inference to inform environmental policy
Abstract:
In this talk, I will present a comprehensive overview of data science methods, focusing on Bayesian analysis, causal inference, and machine learning and their applications in shaping climate and environmental policy. The foundation of this research is built upon the analysis of an unprecedented data platform comprising over 500 million observations related to the health experiences of more than 95% of the US population aged 65 years and older. This data is linked to critical factors, including air pollution exposure, climate exposure (heat, wildfire exposure, and tropical cyclones), and other pertinent confounders.
The vast scope of this data platform allows us to gain valuable insights into the interplay between environmental factors and public health on a scale never seen before. Key highlights of the talk include introducing innovative Bayesian methods for causal inference and discovering and estimating heterogeneous causal effects. By doing so, we can better understand how diverse populations are uniquely affected by environmental influences, enabling more targeted and effective policy interventions.
The outcomes of this research hold great promise for informing evidence-based decision-making in climate and environmental policy. By harnessing the power of data science, we can drive meaningful changes toward a greener, healthier, and more sustainable future for all.