Tackling Climate Change with Machine Learning

Many in the ML community wish to take action on climate change, but are unsure how to have the most impact. This workshop will highlight work that demonstrates that, while ML is no silver bullet, it can be an invaluable tool in reducing greenhouse gas emissions and in helping society adapt to the effects of climate change.

Climate change is a complex problem for which action takes many forms, from advancing theory to deploying new technology. Many of these actions represent high-impact opportunities for real-world change, and simultaneously pose interesting academic research problems.

The theme of this workshop, “Roots to Routes: A Dialogue on Different Machine Learning Methods for Climate Impact,” invites submissions that explore the strengths of diverse machine learning approaches in climate-related contexts. We particularly encourage work that demonstrates the effectiveness of classical ML methods under real-world constraints, such as limited data availability, privacy concerns, or restricted computational resources. At the same time, we welcome contributions that showcase how scaling up data and computing resources combined with modern tools and techniques can unlock new possibilities for tackling global-scale climate prediction challenges.

This workshop is part of a series that aims to bring together those applying ML to climate change challenges and facilitate cross-pollination between ML researchers and experts in climate-relevant fields.

The main workshop will take place on December 6 or 7, 2025 (exact date TBD).