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Tackling Climate Change with Machine Learning
Maria João Sousa · Hari Prasanna Das · Sally Simone Fobi · Jan Drgona · Tegan Maharaj · Yoshua Bengio

Tue Dec 14 06:15 AM -- 05:00 PM (PST) @ None
Event URL: https://www.climatechange.ai/events/neurips2021.html »

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). The scope of the workshop includes climate-relevant applications of machine learning to the power sector, buildings and transportation infrastructure, agriculture and land use, extreme event prediction, disaster response, climate policy, and climate finance. The goals of the workshop are: (1) to showcase high-impact applications of ML to climate change mitigation, adaptation, and climate science, (2) to showcase novel and interesting problem settings and challenges for ML techniques, (3) to encourage fruitful collaboration between the ML community and a diverse set of researchers and practitioners from climate change-related fields, and (4) to promote dialogue with decision-makers in the private and public sectors to ensure that the work presented leads to responsible and meaningful deployment.

Author Information

Maria João Sousa (IDMEC, Instituto Superior Técnico, Universidade de Lisboa)
Hari Prasanna Das (University of California Berkeley)
Sally Simone Fobi (Columbia University)
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.

Tegan Maharaj (MILA)
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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