NeurIPS 2022 Workshop
Tackling Climate Change with Machine Learning

About

Many in the ML community wish to take action on climate change, but are unsure of the pathways through which they can have the most impact. This workshop highlights work that demonstrates that, while no silver bullet, ML 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 theoretical advances to deployment of new technology. Many of these actions represent high-impact opportunities for real-world change, and are simultaneously interesting academic research problems.

This workshop is part of a series (NeurIPS 2021, ICML 2021, NeurIPS 2020, ICLR 2020, NeurIPS 2019, and ICML 2019). For this iteration of the workshop, the keynote talks and panel discussions will be particularly focused on exploring the theme of climate change-informed metrics for AI, focusing both on (a) the domain-specific metrics by which AI systems should be evaluated when used as a tool for climate action, and (b) the climate change-related implications of using AI more broadly.

About NeurIPS

This workshop is part of the Neural Information Processing Systems (NeurIPS), one of the premier conferences on machine learning. To attend this virtual workshop, the “Virtual Only” registration would suffice, and grants you full virtual access to the entire 2 weeks of the conference (including virtual workshops). Please register for the “Virtual Only” component of NeurIPS at https://neurips.cc/Register/view-registration (registration already open). The workshop is open to the public; it is not necessary to submit to the workshop in order to attend.

About the Workshop

EDS Partnership

CCAI has partnered with the journal for Environmental Data Science (EDS) to release a special issue with submissions focused on climate-relevant applications of machine learning. The application, which is currently accepting submissions, is open to anyone and closes January 30th. Please refer to the webpage EDS has dedicated to this special issue for details on submitting your application. Reach out to climatechangeai.neurips2022+EDS@gmail.com with any questions!

Schedule

Time (UTC) Time (Local) Event
Opening Remarks
Gustau Camps-Valls: "Physics-aware Machine learning for Earth observation"
Details: (click to expand)

Gustau Camps-Valls (born 1972 in València) is a Physicist and Full Professor in Electrical Engineering in the Universitat de València, Spain, where lectures on machine learning, remote sensing and signal processing. He is the Head of the Image and Signal Processing (ISP) group, an interdisciplinary group of 40 researchers working at the intersection of AI for Earth and Climate sciences.

Prof. Camps-Valls has published over 250+ peer-reviewed international journal papers, 350+ international conference papers, 25 book chapters, and 5 international books on remote sensing, image processing and machine learning. He has an h-index of 78 with 29000+ citations in Google Scholar. He was listed as a Highly Cited Researcher in 2011, 2020 and 2021; currently has 13 «Highly Cited Papers» and 1 «Hot Paper», Thomson Reuters ScienceWatch identified his activities as a Fast Moving Front research (2011) and the most-cited paper in the area of Engineering in 2011, received the Google Classic paper award (2019), and Stanford Metrics includes him in the top 2% most cited researchers of 2017-2020. He publishes in both technical and scientific journals, from IEEE and PLOS One to Nature, Nature Communications, Science Advances, and PNAS.

He has been Program Committee member of international conferences (IEEE, SPIE, EGU, AGU), and Technical Program Chair at IEEE IGARSS 2018 (2400+ attendees) and general at AISTATS 2022. He served in technical committees of the IEEE GRSS & IEEE SPS, as Associate Editor of 5 top IEEE journals, and in the prestigious IEEE Distinguished Lecturer program of the GRSS (2017-2019) to promote «AI in Earth sciences» globally. He has given 100+ talks, keynote speaker in 10+ conferences, and (co)advised 10+ PhD theses.

He coordinated/participated in 60+ research projects, involving industry and academia at national and European levels. He assisted the aerospace industry in Advisory Boards; Fellow Consultant of the ESA PhiLab (2019) and member of the EUMETSAT MTG-IRS Science Team. He is compromised with open source/access in Science, and is habitual panel evaluator for H2020 (ERC, FET), NSF, China and Swiss Science Foundations.

He coordinates the 'Machine Learning for Earth and Climate Sciences' research program of ELLIS, the top network of excellence on AI in Europe. He was elevated to IEEE Fellow member (2018) in two Societies (Geosciences and Signal Processing) and to ELLIS Fellow (2019). Prof. Camps-Valls is the only researcher receiving two European Research Council (ERC) grants in two different areas, an ERC Consolidator (2015, Computer Science) and ERC Synergy (2019, Physical Sciences) grants to advance AI for Earth and Climate Sciences. In 2021 he became a Member of the ESSC panel part of the European Science Foundation (ESF), and in 2022 was elevated to Fellow of the European Academy of Sciences (EurASc), Fellow of the Academia Europeae (AE), and Fellow of Asia-Pacific Artificial Intelligence Association (AAIA).

Break
Panel 1: "Domain-specific metrics for evaluation and integration of AI"
Details: (click to expand) Panelists:
  • Veronica Adetola, Pacific Northwest National Lab
  • David Dao, ETH Zürich
  • Antoine Marot, Réseau de Transport d'Electricité
Spotlight Presentations
Break
Poster Session 1
Inês M. Azevedo: "Mitigating climate and air pollutions from the electricity and transportation sectors in the United States"
Details: (click to expand) Inês M.L. Azevedo is Associate Professor in the Department of Energy Resources Engineering at Stanford University. She also serves as Senior Fellow for the Woods Institute for the Environment at Stanford University and Fellow for the Precourt Institute for Energy (PIE) at Stanford University. She is the co-director of the Bits&Watts Initiative from PIE at Stanford University. Prof. Azevedo’s research interests focus on how to transition to a sustainable, low carbon, affordable, and equitable energy system. She is interested in sustainability and energy issues where a systems approach is needed, by combining engineering and technology analysis with economic and decision science approaches. Her current interest is to address energy issues with particular focus on distributional effects and equity. She has published 100+ peer-reviewed journal papers. She has participated as an author and committee member in several National Research Council reports from the U.S. National Academy of Sciences. She was one of the Lead Authors for IPCC AR6 report on Climate Mitigation for the Energy chapter, and she is now also participating as Lead Author for the upcoming U.S. National Climate Assessment chapter on climate change mitigation. Prof. Azevedo is also contributing as a chapter author to the upcoming U.S. National Climate Assessment report. Prof. Azevedo has received the World Economic Forum’s “Young Scientists under 40” award in 2014, and the C3E Women in Clean Energy Research Award in 2017.
Break
Panel 2: "Assessing AI’s impacts on greenhouse gas emissions and climate change adaptation"
Details: (click to expand) Panelists:
  • Sasha Luccioni, Hugging Face
  • George Kamiya, International Energy Agency
  • Melissa Omino, Strathmore University
Break
Rose Yu: "Accelerating climate model simulation with physics-guided deep learning"
Details: (click to expand) Dr. Rose Yu is an assistant professor at the University of California San Diego, Department of Computer Science and Engineering. She earned her Ph.D. in Computer Sciences at USC in 2017. She was subsequently a Postdoctoral Fellow at Caltech. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data analysis, with applications to sustainability, health, and physical sciences. Among her awards, she has won NSF CAREER Award, Faculty Research Award from JP Morgan, Facebook, Google, Amazon, and Adobe, Several Best Paper Awards, Best Dissertation Award at USC, and was nominated as one of the MIT Rising Stars in EECS.
Poster Session 2
Tutorials Intro
Spotlight Presentations
Break
Spotlight Presentations
Closing Remarks
Poster Session 3
Networking

Informational Webinar

We recently conducted two informational webinars answering questions about the mentorship program and how to prepare a successful submission for the workshop. The video recording of the Aug 16 webinar can be found here.

Organizers

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

Call for Submissions

We invite submissions of short papers using machine learning to address problems in climate mitigation, adaptation, or modeling, including but not limited to the following topics:

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 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 NeurIPS style files), available for LaTeX and docx format.

All tutorials submissions must be through the submission website.

Please see the Tips for Submissions and FAQ, and contact climatechangeai.neurips2022@gmail.com with questions.

Submission Tracks

There are three tracks for submissions: (i) Papers, (ii) Proposals, (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 NeurIPS 2021, ICML 2021, NeurIPS 2020, ICLR 2020, NeurIPS 2019, and ICML 2019). 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.

PAPERS Track

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 welcomed. Datasets should be designed to permit machine learning research (e.g. formatted with clear benchmarks for evaluation). In this case, baseline experimental results on the dataset are preferred, but not required.

PROPOSALS Track

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.

TUTORIALS Track

Interactive notebooks for insightful step-by-step walkthroughs

Submissions for the Tutorials track should introduce or demonstrate the use of ML methods and tools such as libraries, packages, services, datasets, or frameworks to address a problem related to climate change. Tutorial proposals (due Aug 18) should take the form of an abstract and should include a clear and concise description of users’ expected learning outcomes from the tutorial. Midterm tutorial submissions (due Sep 18) and Final tutorial submissions (due Nov 3) should be in the form of executable notebooks (e.g. Jupyter, Colab). Submissions will be reviewed based on their potential impact and overall usability by the climate and AI research community.

Tips for Submissions

Addressing Impact

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.

Mentorship Program

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.

Expectations:

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:

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 Sep 18.

We suggest that after the mentor-mentee matching is made, a first (physical or digital) meeting should take place within the first week (Aug 18-25) 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 Sep 18.

Mentors and mentees must abide by the following Code of Conduct: https://www.climatechange.ai/code_of_conduct.

Application

Applications are due by Aug 18.

Sponsors

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 climatechangeai.neurips2022@gmail.com 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.

Submission FAQ

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 climatechangeai.neurips2022@gmail.com with any questions.

Q: Can I submit work to this workshop if I am also submitting to another NeurIPS 2022 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.