by Amanda K. Hirsch
Northeastern University Professor and Institute for Experiential AI faculty member Auroop Ganguly will participate in a panel at an event hosted by the National Academies of Sciences, Engineering, and Medicine happening Monday, February 7, 10, and 11, 2022.
The event titled, Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges — A Workshop, brings together presenters, panelists, and attendees from the research and practice communities who are at the forefront of developing artificial intelligence (AI) and machine learning (ML) methods for both predictive understanding of earth system sciences and the cross-linkages of these sciences with human-engineered and social systems.
Earth system sciences involve the study of our planet’s natural make-up; the ground, the water, the air, and how the physics and biogeochemistry of these elements react when designing climate or Earth system models.
Human-engineered systems range from physical, human-made structures on Earth, like buildings and bridges, to power plants, irrigation systems, transportation networks, and more. Social systems include human institutions and communities. Together, these separate disciplines affect and influence global concerns such as climate change, food-energy-water sustainability, and biodiversity.
In his presentation titled, “Artificial Intelligence and the Convergence of Complexities in Coupled Complex Natural Engineered Human Systems,” Ganguly discusses how the shortcomings of each of these disciplines, when combined, make their challenges that much more difficult. He also shares how AI systems informed by scientific theory, engineering principles, social science knowledge, and policy imperatives can provide credible solutions to these problems.
“I am hoping the audience takeaways will be both the enormous barriers as well as the significant opportunities that exist in both research directions and best practices in this truly interdisciplinary area,” said Ganguly. “The audience will hopefully also see how despite many remaining challenges, AI/ML can offer hope of serious progress, but only when informed by theory and domain understanding.”
Learn more about Auroop Ganguly’s work at Northeastern University and the Institute for Experiential AI by watching this video featured in the institute’s Faculty Friday series or contact the institute to see how our experts can help with your next AI project.
AuroopR. Ganguly is a professor of Civil and Environmental Engineering at Northeastern University. He has affiliate appointments with the Khoury College of Computer Science and the School of Public Policy and Urban Affairs.
Ganguly’s research intersects weather and hydrologic extremes under climate change, lifeline infrastructures resilience under compound extremes, as well as spatiotemporal machine learning and nonlinear physics. He has a joint role as a Chief Scientist at the Pacific Northwest National Laboratory. He is also co-founder and chief scientific advisor of the Boston-based climate analytics startup risQ.
Prior to joining Northeastern, Ganguly worked at the Oak Ridge National Laboratory and Oracle Corporation. He is a fellow of the American Society of Civil Engineers and obtained a doctorate from the Massachusetts Institute of Technology in Cambridge, MA.
Ganguly has been in review panels for the United Nations Environmental Programme and other state and global agencies. He contributed to the United Nations Association Climate 2020 report for the United Kingdom and has ongoing collaborations with the NASA Ames and the NASA Earth Exchange, the US Department of Energy Oak Ridge National Laboratory, and the Pacific Northwest National Laboratory.
He has published ininterdisciplinary journals such as Nature, Nature Climate Change, Proceedings of the National Academy of Sciences of the United States of America, PLOS One, and Scientific Reports. He won Best Paper awards in highly selective machine learning/AI and data science conferences such as Association for Computing Machinery (ACM), Knowledge Discovery and Data mining (KDD), and SIAM Data Mining (SDM), among numerous other awards and publications.