The workshop will be held remotely due to risks and travel restrictions associated with COVID-19.
Many in the ML community wish to take action on climate change, yet feel their skills are inapplicable. This workshop aims to show that in fact the opposite is true: while no silver bullet, ML can be an invaluable tool both 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 designing smart electrical grids to tracking deforestation in satellite imagery. Many of these actions represent high-impact opportunities for real-world change, as well as being interesting problems for ML research.
The International Conference on Learning Representations (ICLR) is one of the premier conferences on machine learning, and includes a wide audience of researchers and practitioners in academia, industry, and related fields. It is possible to attend the workshop without either presenting at or attending the main ICLR conference. Those interested should register for ICLR at https://iclr.cc/Register/view-registration.
Stefano Ermon (Stanford University)
Cira wa Maina (Dedan Kimathi University of Technology)
Georgina Campbell Flatter (ClimaCell.org)
Dan Morris (Microsoft AI for Earth)
John Platt (Google)
Dan Kammen (UC Berkeley)
Paula Hidalgo-Sanchis (UN Global Pulse)
Jessica Thorn (University of York)
Nana Ama Browne Klutse (University of Ghana)
Sarvapali Ramchurn (University of Southampton)
Coming soon! Please check back here for updates.
Priya Donti (CMU)
David Rolnick (UPenn)
Lynn Kaack (ETH Zürich)
Sasha Luccioni (Mila)
Kris Sankaran (Mila)
Sharon Zhou (Stanford)
Moustapha Cisse (Google Research)
Carla Gomes (Cornell)
Andrew Ng (Stanford)
Yoshua Bengio (Mila)
Adrian Albert (terrafuse, inc.)
Alberto Chapchap (Visia Investments)
Alexandre Lacoste (Element AI)
Alireza Rezvanifar (University of Victoria)
Alpan Raval (Wadhwani Institute for Artificial Intelligence)
Andrew Ross (Harvard)
Aneesh Rangnekar (Rochester Institute of Technology)
Anthony Ortiz (University of Texas at El Paso)
Armi Tiihonen (MIT)
Ashish Kapoor (Microsoft)
Ashley Pilipiszyn (Stanford)
Bill Cai (MIT)
Björn Lütjens (MIT)
Brian Hutchinson (Western Washington University)
Caleb Robinson (Georgia Institute of Technology)
Christian Schroeder de Witt (University of Oxford)
Clement DUHART (MIT Media Lab)
Daniel Vallero (Duke)
Dara Farrell (Graduate of University of Washington)
David Dao (ETH Zurich)
Deval Pandya (Shell Global Solutions)
Eun-Kyeong Kim (University of Zurich)
Evan Sherwin (Stanford)
Fabrizio Falasca (Georgia Institute of Technology)
FELIPE OVIEDO (MIT)
Frederik Diehl (fortiss)
Frederik Kratzert (Johannes Kepler University)
Garima Jain (ClimateAi)
Gautier Cosne (Mila)
Gavin Portwood (Los Alamos National Lab)
Genevieve Flaspohler (MIT)
George Chen (Carnegie Mellon)
Greg Schivley (Carbon Impact Consulting)
Hari Prasanna Das (UC Berkeley)
Ioannis C. Konstantakopoulos (UC Berkeley)
Jan Drgona (Pacific Northwest National Laboratory)
Jigar Doshi (Wadhwani AI)
Jingfan Wang (Stanford)
Johannes Rausch (ETH Zurich)
John Platt (Google)
Joris Guerin (ENSAM)
Joyjit Chatterjee (University of Hull)
Kara Lamb (Cooperative Institute for Research in the Environmental Sciences)
Kelly Kochanski (University of Colorado Boulder)
Kevin McCloskey (Google)
Kira Goldner (Columbia University)
Konstantin Klemmer (University of Warwick)
Lauren Kuntz (Gaiascope)
Lexie Yang (Oak Ridge National Laboratoy)
Lucas Liebenwein (MIT)
Lucas Kruitwagen (University of Oxford)
Mark Barna (IQVIA)
Max Evans (ClimateAi)
Michael Howland (Stanford)
Miguel Molina-Solana (Imperial College London)
Muge Komurcu (MIT)
Nathan Williams (Rochester Institute of Technology)
Niccolo Dalmasso (Carnegie Mellon)
Nicholas Jones (World Bank)
Nikola Milojevic-Dupont (MCC Berlin)
Olya (Olga) Irzak (Frost Methane Labs)
Peetak Mitra (UMass Amherst)
Rajesh Sankaran (Argonne National Lab)
Ruben Glatt (LLNL)
Sam Skillman (Descartes Labs)
Saumya Sinha (University of Colorado, Boulder)
Sepehr Pashang (University of Waterloo)
Shamindra Shrotriya (Carnegie Mellon)
Slava Jankin (Hertie School of Governance)
Sookyung Kim (Lawrence Livermore National Laboratory)
Sophie Giffard-Roisin (University of Colorado Boulder)
Tegan Maharaj (Mila, Polytechnic Montreal)
Valentina Zantedeschi (Jean Monnet University)
Victor Schmidt (Mila)
Victor Kristof (EPFL)
Victoria Preston (MIT)
Vikram Voleti (Mila, University of Montreal)
Yanbing Wang (Vanderbilt University)
Yimeng Min (Mila)
Yonadav Shavit (Harvard University)
Yue Hu (Vanderbilt University)
Zhecheng Wang (Stanford)
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) 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 record 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 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. We encourage, but do not require, use of the ICLR style template (please do not uncomment the \iclrfinalcopy macro as it will deanonymize your submission).
We will be awarding $30K in cloud computing credits, sponsored by Microsoft AI for Earth, as prizes for top submissions. Winners will be announced at the workshop.
Please see the Tips for Submissions and FAQ, and contact email@example.com with questions.
There are two tracks for submissions. Submissions are limited to 4 pages for the Papers track and 3 pages for the Proposals track (in PDF format), and must be anonymized. References do not count towards the page 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.
For examples of previous submissions, see our ICML 2019 and NeurIPS 2019 workshops. We strongly encourage authors to consider applying for our mentorship program, for which applications are due Jan 14 (more information here).
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. 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.
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. No results need to be demonstrated, but 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, and explanation of the proposed method.
We are excited to announce limited travel grants for the workshop. Travel grant applications can be submitted at https://forms.gle/VZZSk7Fj6xcmQkR3A, and are due March 3.
We also encourage workshop participants to apply for ICLR 2020 travel grants or other grants for which they may be eligible. If you are aware of additional funding opportunities that may be relevant to workshop attendees, please contact the workshop organizers so we can make this information available.
We are piloting a mentorship program to pair authors of submissions with mentors having complementary expertise. 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 three weeks of the CCAI mentorship program (Jan 15 - Feb 4) 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 Feb 4.
We suggest that after the mentor-mentee matching is made, a first (physical or digital) meeting should take place within the first week (Jan 15 - Jan 22) 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 4.
Applications are due by Jan 14.
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.
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 doesn’t get accepted to the workshop?
A: While the mentorship program is meant to give young-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! https://www.climatechange.ai/mailing_list.html
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: Do I need to use LaTeX or the ICLR style files?
A: No, although we encourage it.
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: What do I do if I need an earlier decision for visa reasons?
A: Contact us at email@example.com and explain your situation and the date by which you require a decision and we will do our best to be accommodating.
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! However, if your work was previously accepted to a Climate Change AI workshop, this work must have changed or matured substantively to be eligible for resubmission. Please contact firstname.lastname@example.org with any questions.
Q: Can I submit work to this workshop if I am also submitting to another ICLR 2020 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.