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Tackling Climate Change with ML
David Dao · Evan Sherwin · Priya Donti · Lauren Kuntz · Lynn Kaack · Yumna Yusuf · David Rolnick · Catherine Nakalembe · Claire Monteleoni · Yoshua Bengio

Fri Dec 11 03:00 AM -- 04:00 PM (PST) @ None
Event URL: https://www.climatechange.ai/events/neurips2020 »

Climate change is one of the greatest problems society has ever faced, with increasingly severe consequences for humanity as natural disasters multiply, sea levels rise, and ecosystems falter. Since climate change is a complex issue, action takes many forms, from designing smart electric grids to tracking greenhouse gas emissions through satellite imagery. While no silver bullet, machine learning can be an invaluable tool in fighting climate change via a wide array of applications and techniques. These applications require algorithmic innovations in machine learning and close collaboration with diverse fields and practitioners. This workshop is intended as a forum for those in the machine learning community who wish to help tackle climate change. Building on our past workshops on this topic, this workshop aims to especially emphasize the pipeline to impact, through conversations about machine learning with decision-makers and other global leaders in implementing climate change strategies. The all-virtual format of NeurIPS 2020 provides a special opportunity to foster cross-pollination between researchers in machine learning and experts in complementary fields.

Author Information

David Dao (ETH Zurich)

David Dao is a PhD student at ETH Zurich and the founder of GainForest, a non-profit working on decentralized technology to prevent deforestation. His research focuses on the deployment of novel machine learning systems for sustainable development and ecosystem monitoring. David served as a workshop co-organizer at ICLR, ICML and NeurIPS, and is a core member at Climate Change AI, a Global Shaper at World Economic Forum and a Climate Leader at Climate Reality. He is a research intern with Microsoft and was a former researcher at UC Berkeley and Stanford University.

Evan Sherwin (Stanford University)

I have devoted my professional career to evaluation of pathways toward a very low-carbon global energy system, developing expertise in energy modeling, statistics, machine learning, econometrics, and numerous engineering disciplines, economics, and policy domains as needed.

Priya Donti (Carnegie Mellon University)
Lauren Kuntz (Gaiascope)
Lynn Kaack (ETH Zurich)
Yumna Yusuf (City University London)
David Rolnick (McGill / Mila)
Catherine Nakalembe (University of Maryland)
Claire Monteleoni (University of Colorado Boulder)
Yoshua Bengio (Mila / U. Montreal)

Yoshua Bengio is Full Professor in the computer science and operations research department at U. Montreal, scientific director and founder of Mila and of IVADO, Turing Award 2018 recipient, Canada Research Chair in Statistical Learning Algorithms, as well as a Canada AI CIFAR Chair. He pioneered deep learning and has been getting the most citations per day in 2018 among all computer scientists, worldwide. He is an officer of the Order of Canada, member of the Royal Society of Canada, was awarded the Killam Prize, the Marie-Victorin Prize and the Radio-Canada Scientist of the year in 2017, and he is a member of the NeurIPS advisory board and co-founder of the ICLR conference, as well as program director of the CIFAR program on Learning in Machines and Brains. His goal is to contribute to uncover the principles giving rise to intelligence through learning, as well as favour the development of AI for the benefit of all.

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