Climate change is widely agreed to be one of the greatest challenges facing humanity. We already observe increased incidence and severity of storms, droughts, fires, and flooding, as well as significant changes to global ecosystems, including the natural resources and agriculture on which humanity depends. The 2018 UN report on climate change estimates that the world has only thirty years to eliminate greenhouse emissions completely if we are to avoid catastrophic consequences.

ICML 2019 Workshop

Many in the ML community wish to take action on climate change, yet feel their skills are inapplicable. This workshop will showcase the many settings in which machine learning can be applied to reducing greenhouse emissions and helping society adapt to the effects of climate change. Climate change is a complex problem requiring simultaneous action from many directions. While machine learning is not a silver bullet, this area promises significant impacts for research and implementation.

About ICML

ICML 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 or attending the main ICML conference. Those interested should register for the Workshops component of ICML at while tickets last (a number of spots will be reserved for accepted submissions).

About the workshop

Call For Submissions

We invite submission of extended abstracts on machine learning applied to problems in climate mitigation, adaptation, or modeling, including but not limited to the following topics:

Accepted submissions will be invited to give poster presentations at the workshop, of which some will be selected for spotlight talks. Please contact with questions, or if visa considerations make earlier notification important.

Dual-submissions are allowed, and the workshop does not record proceedings. All submissions must be through the 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 ICML style template (please do not use the “Accepted” format as it will deanonymize your submission).

Submission tracks

Extended abstracts are limited to 3 pages for the Deployed and Research tracks, and 2 pages for the Ideas track, in PDF format. An additional page may be used for references. 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. There are three tracks for submissions:


Work that is already having an impact

Submissions for the Deployed track are intended for machine learning approaches which are impacting climate-relevant problems through consumers or partner institutions. This could include implementations of academic research that have moved beyond the testing phase, as well as results from startups/industry. Details of methodology need not be revealed if they are proprietary, though transparency is encouraged.


Work that will have an impact when deployed

Submissions for the Research track are intended for machine learning research applied to climate-relevant problems. Submissions should provide experimental or theoretical validation of the method proposed, 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.

Datasets may be submitted to this track that are 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.

IDEAS track

Future work that could have an impact

Submissions for the Ideas track are intended for proposed applications of machine learning to solve climate-relevant problems. While the least constrained, this track will be subject to a very high standard of review. No results need be demonstrated, but ideas should be justified as extensively as possible, including motivation for the problem being solved, an explanation of why current tools or methods are inadequate, and details of how tools from machine learning are proposed to fill the gap (i.e. it is important to justify the use of machine learning in your approach).


David Rolnick (UPenn)
Alexandre Lacoste (ElementAI)
Tegan Maharaj (MILA)
Jennifer Chayes (Microsoft)
Yoshua Bengio (MILA)
Karthik Mukkavilli (MILA)
Narmada Balasooriya (ConscientAI)
Di Wu (MILA)
Priya Donti (CMU)
Lynn Kaack (CMU)
Manvitha Ponnapati (MIT)

Frequently Asked Questions

Q: What is the date of the workshop / when will we know?
A: Unfortunately we do not know yet. This is decided by ICML and has not been announced for any of the workshops. We will update the call for papers as soon as we know.

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, or we can also print a poster for you and put it up during the poster session.

Q: Do I need to use LaTeX or the ICML style files?
A: No, although we encourage it.

Q: What do I do if I need an earlier decision for visa reasons?
A: Contact us at and explain your situation and the date by which you require a decision and we will do our best to be accomodating.

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: I don’t know whether to submit my work in the Deployed or Research track. What’s the difference?
A: Deployed means it’s “really being used” in a real-world setting (i.e. not just that you verify your method on real-world data). If you are still unsure, just pick whichever track you prefer your method be evaluated as.

Q: Do submissions for the Ideas track need to have experimental validation?
A: No, although some initial experiments or citation of published results would strengthen your submission.