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Tackling Climate Change with Machine Learning
Peetak Mitra · Maria João Sousa · Mark Roth · Jan Drgona · Emma Strubell · Yoshua Bengio

Fri Dec 09 07:00 AM -- 06:00 PM (PST) @ Virtual
Event URL: https://www.climatechange.ai/events/neurips2022 »

The focus of this workshop is the use of machine learning to help address climate change, encompassing mitigation efforts (reducing greenhouse gas emissions), adaptation measures (preparing for unavoidable consequences), and climate science (our understanding of the climate and future climate predictions). Specifically, we aim to: (1) showcase high-impact applications of ML to climate change mitigation, adaptation, and climate science, (2) discuss related research directions to which the ML community can contribute, (3) brainstorm mechanisms to scale early academic research to successful, viable deployments, and (4) encourage fruitful collaboration between the ML community and a diverse set of researchers and practitioners from climate change-related fields. Building on our past workshops on this topic, this workshop particularly aims to explore the theme of climate change-informed metrics for AI, focusing both on (a) the domain-specific metrics by which AI systems should be evaluated when used as a tool for climate action, and (b) the climate change-related implications of using AI more broadly.

Author Information

Peetak Mitra (Excarta, Inc)

Computational Physicist at Los Alamos National lab working on advanced machine learning methods for modeling physics problems, including combustion, climate models etc. 4th year PhD student at University of Massachusetts Amherst and co-founder of the ICEnet industry funded consortium that is supported by the likes of NVIDIA, MathWorks, SIEMENS, Cummins, Converge and AVL.

Maria João Sousa (IDMEC, Instituto Superior Técnico, Universidade de Lisboa)
Mark Roth
Jan Drgona (Pacific Northwest National Laboratory)

I am a data scientist in the Physics and Computational Sciences Division (PCSD) at Pacific Northwest National Laboratory, Richland, WA. My current research interests fall in the intersection of model-based optimal control, constrained optimization, and machine learning.

Emma Strubell (Carnegie Mellon University)
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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