Tackling Climate Change with Machine Learning

Cambridge Social Dynamics Team

Join us for a thought-provoking seminar on Responsible AI, where we will explore machine learning for tackling climate change.

<h3>Tackling climate change with machine learning: An opportunity for application-driven innovation</h3><p><br></p><p>Machine learning is increasingly being called upon to help address climate change, from processing satellite imagery to modeling Earth systems. 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 introduce novel techniques that leverage the structure of the problem (such as physical constraints and multi-modal self-supervision) to improve accuracy and usability across applications, including monitoring land use with remote sensing, designing chemical catalysts for the energy transition, and downscaling climate data.</p><p><br></p><p>Join us online: Link will be shared the day before the talk</p>

<h3>About the speaker:</h3><p>David Rolnick is Assistant Professor of Computer Science and Canada CIFAR AI Chair 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 Co-founder and Chair of Climate Change AI, Scientific Co-director of Sustainability in the Digital Age, and co-lead of the NSF-NSERC Global Center on AI and Biodiversity. 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.”</p>

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Tackling Climate Change with Machine Learning

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Tackling Climate Change with Machine Learning

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