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Climate Change AI Newsletter

In this edition, a conversation with John Platt on climate change and machine learning, NeurIPS workshop travel grants, interesting readings and more. To recommend content for the next newsletter, please get in touch by email (


John Platt (Google) was featured in the podcast Eye on A.I., where he speaks about machine learning and climate change. Listen to the full episode here.

ICLR voted to offset greenhouse gas emissions related to conference travels
in response to an effort by Alexandre Lacoste and Yoshua Bengio. The increased cost is either covered through sponsors or a small increase of the non-student registration cost. They are now engaging with other AI conferences executives to do the same. For more information contact Alexandre Lacoste (

Related read: This
blog post proposes measures that ACM conferences should take to reduce their emissions impacts, including publicly reporting carbon footprint, offsetting emissions, and putting a price on carbon during conference planning.


Calling all researchers working on areas related to computational sustainability! We invite you to join the annual CompSust Doctoral Consortium 2019, which will be held at Carnegie Mellon University on October 18–20. Funding will be available for eligible graduate students, post-doctoral researchers, and junior researchers. Submissions are due September 4, 11:59 PM Pacific Time. For more information, please see the conference website.

We are excited to announce that limited travel grants are now available for the Climate Change AI workshop at NeurIPS 2019; travel grant applications will be accepted until October 3. The deadline for submitting papers to the workshop is September 11, 11:59 PM Pacific Time. Top submissions will receive $30K in prizes. For more information, see the workshop website.

National Geographic Grants Pr
ogram: AI for Earth Innovation and Artificial Intelligence for Species Discovery are two of the main grant programs of National Geographic. The next deadline for many opportunities is October 9, 2019. Find out more here.

Jobs and Calls for Collaboration

Senior Software Engineer with San Francisco based startup Myst AI. Details here.

Frost Methane is looking for volunteers or collaborators in academia. Their project is about detecting and mitigating concentrated methane emissions from arctic lakes, and they need machine vision expertise for satellite imagery analysis. For more information contact Olya Irzak at

Interesting Readings

Assessing the relative importance of psychological and demographic factors for predicting climate and environmental attitudes
Liam F. Beiser-McGrath, Robert A. Huber
The authors use random forests to explore factors which explain peoples’ attitudes to climate change and other environmental issues. They include a discussion of their methods - there are many opportunities for machine learning analysis of important social science data. 

Opinion: Big data has big potential for applications to climate change adaptation
James D. Ford, Simon E. Tilleard, Lea Berrang-Ford, Malcolm Araos, Robbert Biesbroek, Alexandra C. Lesnikowski, Graham K. MacDonald, Angel Hsu, Chen Chen, Livia Bizikova
The authors make the case that big data can inform climate change adaptation research and decision-making. They outline what is needed to maximize this opportunity, and call for adaptation researchers and data scientists to collaborate.

Machine Learning for the Geosciences: Challenges and Opportunities
Anuj Karpatne, Imme Ebert-Uphoff, Sai Ravela, Hassan Ali Babaie, Vipin Kumar
This article reviews ways machine learning can be usefully applied in the geosciences, with a view both to useful applications and innovative machine learning techniques, including generative modeling for missing data, anomaly detection, quantifying uncertainty in ensemble models, and much more.

Green AI
Roy Schwartz, Jesse Dodge, Noah A. Smith, Oren Etzioni
The article proposes making efficiency a criterion when developing, training, and running models alongside accuracy and related measures. The authors call for “Green AI” research that yields novel results without increasing -- and ideally reducing -- computational cost. 

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