Tackling Climate Change with Machine Learning

Hari Prasanna Das · Katarzyna Tokarska · Maria João Sousa · Meareg Hailemariam · David Rolnick · Xiaoxiang Zhu · Yoshua Bengio


The focus of this workshop is on the use of machine learning to help in addressing climate change, encompassing mitigation efforts (reducing the severity of climate change), adaptation measures (preparing for unavoidable consequences), and climate science (our understanding of the climate and future climate predictions). Topics within the scope of this workshop include 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 demonstrate that the associated ML methods are interesting in their own right, (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, ensuring that the works presented in this workshop have impact on the thoughtful deployment of ML in climate solutions. Building on our previous workshops in this series, this workshop will have a particular focus on ML for the assessment and implementation of objectives set under the Paris Agreement, though submitted works may be on any topic at the intersection of ML and climate change.

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Timezone: America/Los_Angeles »