NeurIPS 2022 Climate Change and Machine Learning: Call for submissions and mentorship program

[Maria João Sousa]

Tackling Climate Change with Machine Learning
Workshop at Neural Information Processing Systems (NeurIPS 2022)
Virtual: December 9th, 2022

Workshop website: https://www.climatechange.ai/events/neurips2022.html
Mentee/Mentor application deadline: August 18th 2022
Tutorials proposal submission deadline: August 18th 2022
Papers/Proposals submission deadline: September 18th 2022
Contact: climatechange...@gmail.com

We invite submissions of short papers which use machine learning to address climate change. All machine learning techniques are welcome (random forests, kernel methods, deep learning, physics-informed learning methods, etc.). Each submission should clearly illustrate why the application has (or could have) a pathway to impact regarding climate change. We highly encourage submissions that make their data and code publicly available. 

Submissions are non-archival, and do not preclude future publication.

Additional details on submissions:
Potential topics for submissions include but are not limited to the following areas of climate change mitigation, adaptation, and climate science: 

• Agriculture and food


• Behavioral and social science

• Buildings

• Carbon capture and sequestration

• Cities and urban planning

• Climate finance and economics

• Climate justice

• Climate science and climate modeling

• Disaster management and relief

• Earth observations and monitoring

• Earth science

• Ecosystems and biodiversity

• Extreme weather

• Forestry and other land use

• Health

• Heavy industry and manufacturing

• Local and indigenous knowledge systems

• Materials science and discovery

• Oceans and marine systems

• Power and energy systems

• Public policy

• Societal adaptation and resilience

• Supply chains

• Transportation

Accepted submissions will be invited to give virtual poster presentations, of which some will be selected for spotlight talks.

Submissions are limited to 4 pages for the Papers Track (work that is in progress, published, and/or deployed), and 3 pages for the Proposals Track (detailed description of an idea for future work, or early-stage results), with additional pages permitted for references and appendices. Tutorials track proposals are to be submitted in the form of an abstract with a detailed description of the proposed coding tutorial.  All submissions *must* explain why the work has (or could have) positive impacts regarding climate change, and why the machine learning methods involved are well-motivated, including appropriate baselines or comparisons. Negative results, for example finding that machine learning methods are out-performed by alternative approaches, are welcomed.

Please see the workshop website for further details on the workshop.

Organizers:
Peetak Mitra (Xerox PARC)
Maria João Sousa (IST, ULisboa)
Mark Roth (Climate, LLC)
Ján Drgoňa (PNNL)
Emma Strubell (Carnegie Mellon University)
Yoshua Bengio (Mila, UdeM)