Tackling Climate Change with Machine Learning
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. In this talk, I will describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, I will describe high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. These recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.
Priya Donti is a Ph.D. student in Computer Science and Public Policy at Carnegie Mellon University, co-advised by Zico Kolter and Inês Azevedo. Her work lies at the intersection of machine learning, electric power systems, and climate change mitigation. Specifically, she am interested in creating novel machine learning techniques that incorporate domain knowledge (such as power system physics) to reduce greenhouse gas emissions from the electricity sector. Priya is a U.S. Department of Energy Computational Science Graduate Fellow and co-chair of Climate Change AI, a group of volunteers from academia and industry who believe in using machine learning, where it is relevant, to help tackle the climate crisis.