ICLR 2024 Workshop: Tackling Climate Change with Machine Learning
- The Papers/Proposals submission deadline has been extended to Feb 6, 2024 11:59pm AoE.
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.
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.
Building on our past workshops on this topic, this edition focuses on two aspects related to fostering the maturity of ML applications for tackling climate change. First, the workshop will shed light on work that employs, analyzes, or critiques ML methods and their use for climate change mitigation and adaptation, including both research and deployment. In addition, we will discuss the key investment mechanics and policy frameworks needed to advance these applications toward just and equitable large-scale deployment and achieve positive societal-scale impact.
This workshop is part of the International Conference on Learning Representations (ICLR), one of the premier conferences on machine learning. For information on how to attend the ICLR conference, please see here https://iclr.cc/Conferences/2024.
About the Workshop
- Date: May 11, 2024
- Location: Vienna, Austria and virtual
- Mentorship program application deadline:
Jan 3, 2024Jan 6, 2024
- Tutorials proposal deadline: Jan 10, 2024
- Tutorials submission website: https://cmt3.research.microsoft.com/CCAIICLR2024
- Papers/Proposals submission deadline:
Feb 3, 2024Feb 6, 2024 11:59pm AoE
- Papers/Proposals submission website: https://cmt3.research.microsoft.com/CCAIICLR2024
- Notification of accepted papers/proposals: Mar 3, 2024
- Contact: email@example.com
Call for Submissions
We invite submissions of short papers using machine learning to address problems in climate mitigation, adaptation, or science, including but not limited to the following topics:
- Agriculture and food
- Behavioural and social science
- 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
- 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
All machine learning techniques are welcome, from kernel methods to deep learning. Each submission should make clear why the application has (or could have) a pathway to positive impacts regarding climate change. We highly encourage submissions which make their data and code publicly available. Accepted submissions will be invited to give poster presentations, of which some will be selected for spotlight talks.
The workshop does not publish proceedings, and submissions are non-archival. Submission to this workshop does not preclude future publication. Previously published work may be submitted under certain circumstances (see the FAQ).
All papers and proposals submissions must be through the submission website. Submissions will be reviewed double-blind; do your best to anonymize your submission, and do not include identifying information for authors in the PDF. Authors are required to use the workshop style template (based on the ICLR style files), available for LaTeX.
All tutorial submissions must be through the submission website.
Please see the Tips for Submissions and FAQ, and contact firstname.lastname@example.org with questions.
There are three tracks for submissions: (i) Papers, (ii) Proposals and (iii) Tutorials. Submissions are limited to 4 pages for the Papers track, and 3 pages for the Proposals track, in PDF format (see examples from previous workshops here). References do not count towards this total. Supplementary appendices are allowed but will be read at the discretion of the reviewers. All submissions must explain why the proposed work has (or could have) positive impacts regarding climate change.
Work that is in progress, published, and/or deployed.
Submissions for the Papers track should describe projects relevant to climate change that involve machine learning. These may include (but are not limited to) academic research; deployed results from startups, industry, public institutions, etc.; and climate-relevant datasets.
Submissions should provide experimental or theoretical validation of the method presented, as well as specifying what gap the method fills. Authors should clearly illustrate a pathway to climate impact, i.e., identify the way in which this work fits into broader efforts to address climate change. Algorithms need not be novel from a machine learning perspective if they are applied in a novel setting. Details of methodology need not be revealed if they are proprietary, though transparency is highly encouraged.
Submissions creating novel datasets are welcome. Datasets should be properly documented with regards to their provenance and contents and designed to permit machine learning research (e.g. formatted with clear benchmarks for evaluation).
Early-stage work and detailed descriptions of ideas for future work.
Submissions for the Proposals track should describe detailed ideas for how machine learning can be used to solve climate-relevant problems. While less constrained than the Papers track, Proposals will be subject to a very high standard of review. Ideas should be justified as extensively as possible, including motivation for why the problem being solved is important in tackling climate change, discussion of why current methods are inadequate, explanation of the proposed method, and discussion of the pathway to climate impact. Preliminary results are optional.
Interactive notebooks for insightful step-by-step walkthroughs
Submissions for the Tutorials track should demonstrate the use of machine learning methods and tools (e.g. libraries, packages, services, datasets, or frameworks) to address climate-relevant challenges.
Tutorial proposals will be due January 10, 2024, and should take the form of an abstract that includes a clear and concise description of what users can expect to learn. Proposal submissions will be reviewed based on their potential impact and usability by the climate and AI research community. Submissions will also be assessed based on the originality of the problem space. Please review our list of CCAI tutorials to minimize substantial overlap with existing tutorials.
For accepted proposals, the first iteration of the tutorial will be due February 18, 2024 and should be in the form of executable notebooks that follows CCAI’s Tutorial Template. Authors must submit their notebook in a self-contained runnable environment (e.g. Colab, Binder) to allow users to easily access and run the tutorial. We expect tutorials to be 75-90% complete by this first deadline in order for our reviewers to provide adequate feedback and select tutorials for spotlight presentations. Tutorials accepted during this round will be invited to make final submissions by April 7, 2024.
Notebook submissions will be assessed based on clarity, accessibility, and code quality. We ask that authors emphasize real-world impacts of their ML models by answering questions such as Who will be using the models/outputs and how will they be used? What decisions will be made based on these models? How will this impact existing systems/the environment/affected communities on the ground? For more information please review our tutorial development guidelines.
Tips for Submissions
- For examples of typical formatting and content, see submissions from our previous workshops.
- Be explicit: Describe how your proposed approach addresses climate change, demonstrating an understanding of the application area.
- Frame your work: The specific problem and/or data proposed should be contextualized in terms of prior work.
- Address the impact: Describe the practical implications of your method in addressing the problem you identify, as well as any relevant societal impacts or potential side-effects. We recommend reading our further guidelines on this aspect here.
- Explain the ML: Readers may not be familiar with the exact techniques you are using or may desire further detail.
- Justify the ML: Describe why the ML method involved is needed, and why it is a good match for the problem.
- Avoid jargon: Jargon is sometimes unavoidable but should be minimized. Ideal submissions will be accessible both to an ML audience and to experts in other relevant fields, without the need for field-specific knowledge. Feel free to direct readers to accessible overviews or review articles for background, where it is impossible to include context directly.
Tackling climate change requires translating ideas into action. The guidelines below will help you clearly present the importance of your work to a broad audience, hopefully including relevant decision-makers in industry, government, nonprofits, and other areas.
- Illustrate the link: Many types of work, from highly theoretical to deeply applied, can have clear pathways to climate impact. Some links may be direct, such as improving solar forecasting to increase utilization within existing electric grids. Others may take several steps to explain, such as improving computer vision techniques for classifying clouds, which could help climate scientists seeking to understand fundamental climate dynamics.
- Consider your target audience: Try to convey with relative specificity why and to whom solving the problem at hand will be useful. If studying extreme weather prediction, consider how you would communicate your key findings to a government disaster response agency. If analyzing a supply chain optimization pilot program, what are the main takeaways for industries who might adopt this technology? To ensure your work will be impactful, where possible we recommend co-developing projects with relevant stakeholders or reaching out to them early in the process for feedback. We encourage you to use this opportunity to do so!
- Outline key metrics: Quantitative or qualitative assessments of how well your results (or for proposals, anticipated results) compare to existing methods are encouraged. Try to give a sense of the importance of your problem and your findings. We encourage you to convey why the particular metrics you choose are relevant from a climate change perspective. For instance, if you are evaluating your machine learning model on the basis of accuracy, how does improved accuracy on a machine learning model translate to climate impact, and why is accuracy the best metric to use in this context?
- Be clear and concise: The discussion of impact does not need to be lengthy, just clear.
- Convey the big picture: Ultimately, the goal of Climate Change AI is to “empower work that meaningfully addresses the climate crisis.” Try to make sure that from the beginning, you contextualize your method and its impacts in terms of this objective.
If you have further questions on how to participate in the workshop, you can ask those directly via the interactive Q&A session hosted on our community platform. You can also contact us via email at email@example.com. For recordings of informational webinars of previous editions of our workshop, please see here.
- 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
Shiva Madadkhani (Technical University of Munich)
Arthur Ouaknine (McGill, Mila)
Rasika Bhalerao (Northeastern University)
Millie Chapman (National Center for Ecological Analysis and Synthesis)
Jesse Dunietz (Climate Change AI)
Olivia Mendivil Ramos (Climate Change AI)
David Rolnick (McGill, Mila)
Yoshua Bengio (Mila, UdeM)
Tutorials Track Organizers
Isabelle Tingzon (The World Bank/GFDRR)
Nadia Ahmed (UC Irvine)
Shafat Rahman (University of Southern California)
We are hosting a mentorship program to facilitate exchange between potential workshop submitters and experts working in topic areas relevant to the workshop. The goal of this program is to foster cross-disciplinary collaborations and ultimately increase the quality and potential impact of submitted work.
Mentors are expected to guide mentees during the CCAI mentorship program as they prepare submissions for this workshop.
Examples of mentor-mentee interactions may include:
- In-depth discussion of relevant related work in the area of the Paper or Proposal, to ensure submissions are well-framed and contextualized in terms of prior work.
- Iterating on the core idea of a Proposal to ensure that the climate change application is well-posed and the ML techniques used are well-suited.
- Giving feedback on the writing or presentation of a Paper or Proposal to bring it to the right level of maturity for submission.
Mentees are expected to initiate contact with their assigned mentor and put in the work and effort necessary to prepare a Paper or Proposal submission by Feb 3.
We suggest that after the mentor-mentee matching is made, a first (physical or digital) meeting should take place within the first week (Jan 10-14) to discuss the Paper or Proposal and set expectations for the mentorship period. Subsequent interactions can take place either through meetings or via email discussions, following the expectations set during the initial meeting, culminating in a final version of a Paper or Proposal submitted via the CMT portal by Feb 3.
Mentors and mentees must abide by the Climate Change AI Code of Conduct: https://www.climatechange.ai/code_of_conduct.
Applications are due by
Jan 3 Jan 6.
- Application to be a mentee: https://forms.gle/CqNGadZDbdVrfnab8
- Application to be a mentor: https://forms.gle/atd3VGWzgCy8Z6wz8
Frequently Asked Questions
Mentorship Program FAQ
Q: Are mentors allowed to be authors on the paper for which they provided mentorship?
A: Yes, mentors can be co-authors but not reviewers.
Q: What happens if the mentor/mentee does not fulfill their duties, or if major issues come up?
A: Please email us at firstname.lastname@example.org and we will do our best to help resolve the situation. Potential breaches of the Code of Conduct will be responded to promptly as detailed therein.
Q: What happens if I apply to be a mentee but do not get paired with a mentor?
A: While we will do our best, we cannot guarantee pairings for everyone. Even if you do not get paired with a mentor, we encourage you to submit a Paper or Proposal to the workshop, and our reviewers will provide you with guidance and feedback on how to improve it.
Q: What happens if my submission does not get accepted to the workshop?
A: While the mentorship program is meant to give early-career researchers and students the opportunity to improve the quality of their work, sometimes submissions will need further polishing and elaboration before being ready for presentation at a CCAI workshop. If this is the case, we invite you to take into account the comments made by the reviewers and to resubmit again to a subsequent CCAI workshop.
Q: I cannot guarantee that I can commit at least 4 hours to the program over the time period. Should I still apply as a mentor?
A: No. While the 4 hour time commitment is a suggestion, we do believe that it is necessary to ensure that all mentees receive the help and guidance they need.
Q: I do not have a background in machine learning; can I still apply to be a mentor/mentee?
A: Yes! We welcome applications from domains that are complementary to machine learning to solve the problems that we are targeting.
Q: What happens if my mentor/mentee wants to continue meeting after the workshop?
A: We welcome and encourage continued interactions after the official mentorship period. That said, neither the mentor nor the mentee should feel obligated to maintain contact.
Q: How can I keep up to date on this kind of stuff?
A: Sign up for our mailing list!
Q: I’m not in machine learning. Can I still submit?
A: Yes, absolutely! We welcome submissions from many fields. Do bear in mind, however, that the majority of attendees of the workshop will have a machine learning background; therefore, other fields should be introduced sufficiently to provide context for the work.
Q: What if my submission is accepted but I can’t attend the workshop?
A: You may ask someone else to present your work in your stead.
Q: It’s hard for me to fit my submission on 3 or 4 pages. What should I do?
A: Feel free to include appendices with additional material (these should be part of the same PDF file as the main submission). Do not, however, put essential material in an appendix, as it will be read at the discretion of the reviewers.
Q: Can I send submissions directly by email?
A: No, please use the CMT website to make submissions.
Q: The submission website is asking for my name. Is this a problem for anonymization?
A: You should fill out your name and other info when asked on the submission website; CMT will keep your submission anonymous to reviewers.
Q: Do submissions for the Proposals track need to have experimental validation?
A: No, although some initial experiments or citation of published results would strengthen your submission.
Q: The submission website never sent me a confirmation email. Is this a problem?
A: No, the CMT system does not send automatic confirmation emails after a submission, though the submission should show up on the CMT page once submitted. If in any doubt regarding the submission process, please contact the organizers. Also please avoid making multiple submissions of the same article to CMT.
Q: Can I submit previously published work to this workshop?
A: Yes, though under limited circumstances. In particular, work that has previously been published at non-machine learning venues may be eligible for submission; however, work that has been published in conferences on machine learning or related fields is likely not eligible. If your work was previously accepted to a Climate Change AI workshop, this work should have changed or matured substantively to be eligible for resubmission. Please contact email@example.com with any questions.
Q: Can I submit work to this workshop if I am also submitting to another ICLR 2024 workshop?
A: Yes. We cannot, however, guarantee that you will not be expected to present the material at a time that conflicts with the other workshop.