Application-Driven Machine Learning for Climate Action

The Rohatyn Center for Global Affairs program on Science, Technology, Environment and Global Affairs presents “Application-Driven Machine Learning for Climate Action” with David Rolnick. 

Machine learning is increasingly being called upon to help address the climate and biodiversity crises. Such settings represent an important frontier for machine learning innovation, where traditional paradigms of large, general-purpose datasets and models often fall short. In this talk, we show how an application-driven paradigm for algorithm design can respond to problem-specific goals and incorporate relevant domain knowledge. We consider problems from mapping crops to modeling species distributions, in which novel machine learning techniques are required to meet user needs.

David Rolnick is an Assistant Professor and Canada CIFAR AI Chair in the School of Computer Science at McGill University and at Mila – Quebec AI Institute. He is a Co-founder and Chair of Climate Change AI and serves as Scientific Co-director of Sustainability in the Digital Age and co-lead of the Global Center on AI and Biodiversity Change (ABC). Dr. Rolnick is a Sloan Research Fellow and an AI2050 Early Career Fellow and was named to the MIT Technology Review’s 2021 list of “35 Innovators Under 35” for his work in building the field of AI and climate change. He received his Ph.D. in Applied Mathematics from MIT and is a former Fulbright Scholar, NSF Graduate Research Fellow, and NSF Mathematical Sciences Postdoctoral Research Fellow.

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