Many individuals in the machine learning community express a desire to contribute to addressing climate change but are uncertain about the most impactful actions.
The workshop aims to showcase research indicating that while machine learning is not a cure-all, it can serve as a valuable tool in reducing greenhouse gas emissions and aiding society in adapting to the impacts of climate change.
This workshop is a part of the International Conference on Learning Representations (ICLR), which is recognized as one of the leading conferences in the field of machine learning. The workshop will provide insights into projects that utilize, analyze, or evaluate machine learning methods in the context of climate change mitigation and adaptation, encompassing both research and practical implementation.
Speakers
Keynotes:
- Prof. Emily Shuckburgh: Director of Cambridge Zero at the University of Cambridge
Panel 1: Pathways to Industry Deployment: Enabling and Sustaining Maturity of ML Applications for Climate Change
- Dr. Amen Ra Mashariki: Director of Data Strategies at Bezos Earth Fund.
- Prof. Aidan O’Sullivan: Associate Professor at University College London, Co-founder and CTO of Carbon Re.
- Kate Kallot: Founder and CEO at Amini.
Panel 2: Shaping the ML Innovation Landscape with a Climate Lens
- Emily Campbell-Ratcliffe: Head of AI Assurance at the Centre for Data Ethics and Innovation, Department for Science, Innovation and Technology of the UK
- Dr. Michal Nachmany: CEO and Founder of Climate Policy Radar and Visiting Policy Fellow at Graham Research Institute on Climate Change & the Environment
- Dr. Olof Mogren: Senior Researcher Scientist at RISE Research Institutes of Sweden
- Dr. Sasha Luccioni: AI Researcher and Climate Lead, HuggingFace
