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1:00 PM (CT)

U.S.

Zoom link

https://us06web.zoom.us/j/81940252400


Assistant Professor

School of Computer Science, 

McGill University

 

The use of emulators for uncertainty characterization and integrated modeling 

 

Abstract: Machine learning (ML) can be a useful tool in helping society reduce greenhouse gas emissions and adapt to a changing climate. In this talk, we will explore opportunities and challenges in ML for climate action, from optimizing electrical grids to monitoring crop yield, with an emphasis on how to incorporate domain-specific knowledge into machine learning algorithms. We will also consider ways that ML is used in ways that contribute to climate change, and how to better align the use of ML overall with climate goals.


Speaker Biography

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, where his work focuses on applications of machine learning to help address climate change. He is a Co-founder and Chair of Climate Change AI and Scientific Co-director of Sustainability in the Digital Age. Dr. Rolnick received his Ph.D. in Applied Mathematics from MIT. He is a former NSF Mathematical Sciences Postdoctoral Research Fellow, NSF Graduate Research Fellow, and Fulbright Scholar, and was named to the MIT Technology Review's 2021 list of "35 Innovators Under 35."




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