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.
Recordings of the workshop are linked to in the schedule.
8:30 - 8:45 - Welcome and Opening Remarks
8:45 - 9:20 - John Platt (Google AI): AI for Climate Change: the Context (Keynote talk)
9:20 - 9:45 - Jack Kelly (Open Climate Fix): Why it’s hard to mitigate climate change, and how to do better (Invited talk)
9:45 - 10:10 - Andrew Ng (Stanford): Tackling climate change challenges with AI through collaboration (Invited talk)
10:10 - 10:20 - Volodymyr Kuleshov: Towards a Sustainable Food Supply Chain Powered by Artificial Intelligence (Spotlight talk)
10:20 - 10:30 - Clement Duhart: Deep Learning for Wildlife Conservation and Restoration Efforts (Spotlight talk)
10:30 - 11:00 - Coffee break + Poster Session
11:00 - 12:00 - Chad Frischmann (Project Drawdown): Achieving Drawdown (Keynote talk)
12:00 - 1:30 - Networking lunch (provided) + Poster Session
1:30 - 1:55 - Yoshua Bengio (Mila): Personalized Visualization of the Impact of Climate Change (Invited talk)
1:55 - 2:30 - Claire Monteleoni (CU Boulder): Advances in Climate Informatics: Machine Learning for the Study of Climate Change (Invited talk)
2:30 - 2:40 - Duncan Watson-Parris: Detecting anthropogenic cloud perturbations with deep learning (Spotlight talk)
2:40 - 2:50 - Chaopeng Shen: Evaluating aleatoric and epistemic uncertainties of time series deep learning models for soil moisture predictions (Spotlight talk)
2:50 - 3:00 - Mohammad Mahdi Kamani: Targeted Meta-Learning for Critical Incident Detection in Weather Data (Spotlight talk)
3:00 - 3:30 - Coffee break + Poster Session
3:30 - 3:45 - Karthik Mukkavilli (Mila): Geoscience data and models for the Climate Change AI community (Invited talk)
3:45 - 4:20 - Sims Witherspoon (DeepMind): ML vs. Climate Change, Applications in Energy at DeepMind (Invited talk)
4:20 - 4:30 - Lynn Kaack: Truck Traffic Monitoring with Satellite Images (Spotlight talk)
4:30 - 4:50 - Neel Guha: Machine Learning for AC Optimal Power Flow (Spotlight talk)
4:40 - 4:50 - Christian Clough, Gopal Erinjippurath: Planetary Scale Monitoring of Urban Growth in High Flood Risk Areas (Spotlight talk)
4:50 - 5:15 - “Ideas” mini-spotlights
5:15 - 6:00 - Yoshua Bengio, Andrew Ng, Raia Hadsell, John Platt, Claire Monteleoni: Panel discussion
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 (ETH Zürich)
Manvitha Ponnapati (MIT)
Works are submitted to one of three tracks: Research, Deployed, or Ideas.
Sandeep Manjanna (McGill University); Herke van Hoof (University of Amsterdam); Gregory Dudek (McGill University)
Jussi Gillberg (Aalto University); Pekka Marttinen (Aalto University); Hiroshi Mamitsuka (Kyoto University); Samuel Kaski (Aalto University)
Han Zou (UC Berkeley); Hari Prasanna Das (UC Berkeley ); Jianfei Yang (Nanyang Technological University); Yuxun Zhou (UC Berkeley); Costas Spanos (UC Berkeley)
Sookyung Kim (Lawrence Livermore National Laboratory); Sunghyun Park (Korea University); Sunghyo Chung (Korea University); Yunsung Lee (Korea University); Hyojin Kim (LLNL); Joonseok Lee (Google Research); Jaegul Choo (Korea University); Mr Prabhat (Lawrence Berkeley National Laboratory)
Soukayna Mouatadid (University of Toronto); Pierre Gentine (Columbia University); Wei Yu (University of Toronto); Steve Easterbrook (University of Toronto)
Sajad Haghanifar (University of Pittsburgh); Bolong Cheng (SigOpt); Mike Mccourt (SigOpt); Paul Leu (University of Pittsburgh)
Dimitrios Giannakis (Courant Institute of Mathematical Sciences, New York University); Joanna Slawinska (University of Wisconsin-Milwaukee); Abbas Ourmazd (University of Wisconsin-Milwaukee)
Di Wu (McGill); Tracy Cui (Google NYC); Doina Precup (McGill University); Benoit Boulet (McGill)
Neel Guha (Carnegie Mellon University); Zhecheng Wang (Stanford University); Arun Majumdar (Stanford University)
Mohammad Mahdi Kamani (The Pennsylvania State University); Sadegh Farhang (Pennsylvania State University); Mehrdad Mahdavi (Penn State); James Z Wang (The Pennsylvania State University)
Karthik Kashinath (Lawrence Berkeley National Laboratory); Mayur Mudigonda (UC Berkeley); Kevin Yang (UC Berkeley); Jiayi Chen (UC Berkeley); Annette Greiner (Lawrence Berkeley National Laboratory); Mr Prabhat (Lawrence Berkeley National Laboratory)
Paulo Orenstein (Stanford); Jessica Hwang (Stanford); Judah Cohen (AER); Karl Pfeiffer (AER); Lester Mackey (Microsoft Research New England)
Kalai Ramea (PARC)
Niccolo Dalmasso (Carnegie Mellon University); Robin Dunn (Carnegie Mellon University); Benjamin LeRoy (Carnegie Mellon University); Chad Schafer (Carnegie Mellon University)
Zhuangfang Yi (Development Seed); Drew Bollinger (Development Seed); Devis Peressutti (Sinergise)
Tom G Beucler (Columbia University & UCI); Stephan Rasp (Ludwig-Maximilian University of Munich); Michael Pritchard (UCI); Pierre Gentine (Columbia University)
Telmo Felgueira (IST)
Gege Wen (Stanford University)
Lynn Kaack (ETH Zurich); George H Chen (Carnegie Mellon University); Granger Morgan (Carnegie Mellon University)
Chaopeng Shen (Pennsylvania State University)
Duncan Watson-Parris (University of Oxford); Sam Sutherland (University of Oxford); Matthew Christensen (University of Oxford); Anthony Caterini (University of Oxford); Dino Sejdinovic (University of Oxford); Philip Stier (University of Oxford)
Ashesh K Chattopadhyay (Rice University); Pedram Hassanzadeh (Rice University); Devika Subramanian (Rice University); Krishna Palem (Rice University); Charles Jiang (Rice University); Adam Subel (Rice University)
Shubhankar V Deshpande (Carnegie Mellon University), Brian D Bue (NASA JPL/Caltech), David R Thompson (NASA JPL/Caltech), Vijay Natraj (NASA JPL/Caltech), Mario Parente (UMass Amherst)
Christian F Clough (Planet); Ramesh Nair (Planet); Gopal Erinjippurath (Planet); Matt George (Planet); Jesus Martinez Manso (Planet)
Jinfan Xu (Zhejiang University); Renhai Zhong (Zhejiang University); Jialu Xu (Zhejiang University); Haifeng Li (Central South University); Jingfeng Huang (Zhejiang University); Tao Lin (Zhejiang University)
Prabal Acharyya (Petuum Inc); Sean D Rosario (Petuum Inc); Roey Flor (Petuum Inc); Ritvik Joshi (Petuum Inc); Dian Li (Petuum Inc); Roberto Linares (Petuum Inc); Hongbao Zhang (Petuum Inc)
Volodymyr Kuleshov (Stanford University)
Johan Mathe (Frog Labs)
Tianle Yuan (NASA)
Abraham Stanway (Amperon Holdings, Inc); Ydo Wexler (Amperon)
Clement Duhart (MIT Media Lab)
Jonathan Binas (Mila, Montreal); Leonie Luginbuehl (Department of Plant Sciences, University of Cambridge); Yoshua Bengio (Mila)
Christian A Schroeder (University of Oxford); Thomas Hornigold (University of Oxford)
Sasha Luccioni (Mila); Hector Palacios (Element AI)
Kris Sankaran (Montreal Institute for Learning Algorithms); Zouheir Malki (Polytechnique Montréal); Loubna Benabou (UQAR); Hicham Bouzekri (MASEN)
David Dao (ETH); Ce Zhang (ETH); Nick Beglinger (Cleantech21); Catherine Cang (UC Berkeley); Reuven Gonzales (OasisLabs); Ming-Da Liu Zhang (ETHZ); Nick Pawlowski (Imperial College London); Clement Fung (University of British Columbia)
Kate Duffy (Northeastern University); Auroop Ganguly (Northeastern University)
Hillary S Scannell (University of Washington); Chris Fraley (Tableau Software); Nathan Mannheimer (Tableau Software); Sarah Battersby (Tableau Software); LuAnne Thompson (University of Washington)
Kevin McCloskey (Google)
Nikola Milojevic-Dupont (Mercator Research Institute on Global Commons and Climate Change (MCC)); Felix Creutzig (Mercator Research Institute on Global Commons and Climate Change (MCC))
Ashley Pilipiszyn (Stanford University)
Mahdi Jamei (Invenia Labs); Letif Mones (Invenia Labs); Alex Robson (Invenia Labs); Lyndon White (Invenia Labs); James Requeima (Invenia Labs); Cozmin Ududec (Invenia Labs)
Aneesh Rangnekar (Rochester Institute of Technology); Matthew J Hoffman (Rochester Institute of Technology)
Yang Song (Oak Ridge National Lab); Dali Wang (Oak Ridge National Lab); Melanie Mayes (Oak Ridge National Lab)
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 https://icml.cc/ while tickets last (a number of spots will be reserved for accepted 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 firstname.lastname@example.org 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).
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 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).
Q: How can I keep up to date on this kind of stuff?
A: Sign up to our mailing list! https://www.climatechange.ai/Mailing_list.html
Q: What is the date of the workshop / when will we know?
A: Friday, June 14 was recently confirmed as the date.
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 email@example.com 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.
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.