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ICML workshop “Climate Change: How Can AI Help?

*** CALL FOR SUBMISSIONS: ICML workshop “Climate Change: How Can AI Help?” ***

We invite submission of extended abstracts applying machine learning to the problems of climate change. There will be three tracks (Deployed, Research, and Ideas).

Date: June 14 or 15, 2019
Location: Long Beach, California, USA
Submission deadline: April 30, 11:59 PM Pacific Time
Notification: May 15 (early notification possible upon request)
Submission website:

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.

Many in the machine learning community want to address climate change but 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, there is significant potential impact 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 and industry. 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).

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:

- Power generation and grids
- Transportation
- Smart buildings and cities
- Industrial optimization
- Carbon capture and sequestration
- Agriculture, forestry and other land use
- Climate modeling
- Extreme weather events
- Disaster management and relief
- Societal adaptation
- Ecosystems and natural resources
- Data presentation and management
- Climate finance

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. 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).

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.

* 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 are inadequate, and details of how tools from machine learning are proposed to fill the gap.

David Rolnick (UPenn)
Alexandre Lacoste (Element AI)
Tegan Maharaj (MILA)
Jennifer Chayes (Microsoft)
Yoshua Bengio (MILA)