Tackling Climate Change with Machine Learning – A Summary


Tackling climate change requires concerted societal action, and in some cases machine learning can play an impactful role. This talk describes various ways in which machine learning can be used to reduce and respond to climate change, across areas such as energy, land use, and societal adaptation. This talk is a summary of the paper “Tackling Climate Change with Machine Learning,” available at https://dl.acm.org/doi/10.1145/3485128

The talk is presented by current and former members of Climate Change AI, a nonprofit initiative to catalyze impactful work at the intersection of climate change and machine learning. It was presented as part of the TEDxClimateChangeAI Countdown event on October 17, 2020 (https://www.climatechange.ai/events/tedx ).

* David Rolnick, Assistant Professor in the School of Computer Science at McGill University and the Mila Quebec AI Institute
* Priya Donti, PhD student in Computer Science & Public Policy at Carnegie Mellon University
* Lynn Kaack, Postdoctoral Researcher in the Energy Politics Group at ETH Zürich
* Nikola Milojevic-Dupont, PhD student in Land Use, Infrastructure and Transport at the MCC Berlin and at the TU Berlin
* Anna Waldman-Brown, PhD student in Political Economy at the Department of Urban Studies and Planning at MIT
* Alexandre Lacoste, Research Scientist at ServiceNow
* Evan D. Sherwin, Postdoctoral Research Fellow, Energy and Resources Engineering at Stanford University
* Kelly Kochanski, Data Scientist in Climate Analytics at McKinsey & Company
* Kris Sankaran, Assistant Professor in the Department of Statistics at UW Madison
* Natasha Jaques, Research Scientist at Google Research (Brain Team), Postdoctoral Researcher at UC Berkeley
* Sasha Luccioni, Postdoctoral researcher at Université de Montréal and Mila


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