Artificial Intelligence integrated with domain knowledge may help bridge the gap between climate science and stakeholder insights. Physics-informed machine learning and data sciences for Big and small data can have impact across disciplines.
– Auroop Ganguly, Core Member, EAI
Auroop R. 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 in interdisciplinary 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.