ICML 2019 Workshop
Climate Change: How Can AI Help?

About Speakers Schedule Organizers Accepted Works Call for Submissions FAQ

Announcements


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.

About the workshop

Speakers

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, Sims Witherspoon, John Platt, Claire Monteleoni: Panel discussion

Organizers

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)

ElementAI logo


Works are submitted to one of three tracks: Research, Deployed, or Ideas.

Research Track

Title Authors
(1) Policy Search with Non-uniform State Representations for Environmental Sampling Sandeep Manjanna (McGill University); Herke van Hoof (University of Amsterdam); Gregory Dudek (McGill University)
(2) Modelling GxE with historical weather information improves genomic prediction in new environments Jussi Gillberg (Aalto University); Pekka Marttinen (Aalto University); Hiroshi Mamitsuka (Kyoto University); Samuel Kaski (Aalto University)
(3) Machine Learning empowered Occupancy Sensing for Smart Buildings Han Zou (UC Berkeley); Hari Prasanna Das (UC Berkeley ); Jianfei Yang (Nanyang Technological University); Yuxun Zhou (UC Berkeley); Costas Spanos (UC Berkeley)
(4) Focus and track: pixel-wise spatio-temporal hurricane tracking 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)
(5) Recovering the parameters underlying the Lorenz-96 chaotic dynamics Soukayna Mouatadid (University of Toronto); Pierre Gentine (Columbia University); Wei Yu (University of Toronto); Steve Easterbrook (University of Toronto)
(6) Using Bayesian Optimization to Improve Solar Panel Performance by Developing Antireflective, Superomniphobic Glass Sajad Haghanifar (University of Pittsburgh); Bolong Cheng (SigOpt); Mike Mccourt (SigOpt); Paul Leu (University of Pittsburgh)
(7) A quantum mechanical approach for data assimilation in climate dynamics Dimitrios Giannakis (Courant Institute of Mathematical Sciences, New York University); Joanna Slawinska (University of Wisconsin-Milwaukee); Abbas Ourmazd (University of Wisconsin-Milwaukee)
(8) Data-driven Chance Constrained Programming based Electric Vehicle Penetration Analysis Di Wu (McGill); Tracy Cui (Google NYC); Doina Precup (McGill University); Benoit Boulet (McGill)
(9) Machine Learning for AC Optimal Power Flow Honorable Mention Neel Guha (Carnegie Mellon University); Zhecheng Wang (Stanford University); Arun Majumdar (Stanford University)
(10) Targeted Meta-Learning for Critical Incident Detection in Weather Data Mohammad Mahdi Kamani (The Pennsylvania State University); Sadegh Farhang (Pennsylvania State University); Mehrdad Mahdavi (Penn State); James Z Wang (The Pennsylvania State University)
(11) ClimateNet: Bringing the power of Deep Learning to weather and climate sciences via open datasets and architectures 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)
(12) Improving Subseasonal Forecasting in the Western U.S. with Machine Learning Paulo Orenstein (Stanford); Jessica Hwang (Stanford); Judah Cohen (AER); Karl Pfeiffer (AER); Lester Mackey (Microsoft Research New England)
(13) Unsupervised Temporal Clustering to Monitor the Performance of Alternative Fueling Infrastructure Kalai Ramea (PARC)
(14) A Flexible Pipeline for Prediction of Tropical Cyclone Paths Niccolo Dalmasso (Carnegie Mellon University); Robin Dunn (Carnegie Mellon University); Benjamin LeRoy (Carnegie Mellon University); Chad Schafer (Carnegie Mellon University)
(15) Mapping land use and land cover changes faster and at scale with deep learning on the cloud Zhuangfang Yi (Development Seed); Drew Bollinger (Development Seed); Devis Peressutti (Sinergise)
(16) Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling Tom G Beucler (Columbia University & UCI); Stephan Rasp (Ludwig-Maximilian University of Munich); Michael Pritchard (UCI); Pierre Gentine (Columbia University)
(17) The Impact of Feature Causality on Normal Behaviour Models for SCADA-based Wind Turbine Fault Detection Telmo Felgueira (IST)
(18) Predicting CO2 Plume Migration using Deep Neural Networks Gege Wen (Stanford University)
(19) Truck Traffic Monitoring with Satellite Images Lynn Kaack (ETH Zurich); George H Chen (Carnegie Mellon University); Granger Morgan (Carnegie Mellon University)
(20) Evaluating aleatoric and epistemic uncertainties of time series deep learning models for soil moisture predictions Chaopeng Shen (Pennsylvania State University)
(21) Detecting anthropogenic cloud perturbations with deep learning Best Paper Award 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)
(22) Data-driven surrogate models for climate modeling: application of echo state networks, RNN-LSTM and ANN to the multi-scale Lorenz system as a test case 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)
(23) Learning Radiative Transfer Models for Climate Change Applications in Imaging Spectroscopy 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)
(24) Planetary Scale Monitoring of Urban Growth in High Flood Risk Areas Christian F Clough (Planet); Ramesh Nair (Planet); Gopal Erinjippurath (Planet); Matt George (Planet); Jesus Martinez Manso (Planet)
(25) Efficient Multi-temporal and In-season Crop Mapping with Landsat Analysis Ready Data via Long Short-term Memory Networks 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)

Deployed Track

Title Authors
(26) Autopilot of Cement Plants for Reduction of Fuel Consumption and Emissions 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)
(27) Towards a Sustainable Food Supply Chain Powered by Artificial Intelligence Honorable Mention Volodymyr Kuleshov (Stanford University)
(28) PVNet: A LRCN Architecture for Spatio-Temporal Photovoltaic Power Forecasting from Numerical Weather Prediction Johan Mathe (Frog Labs)
(29) Finding Ship-tracks Using Satellite Data to Enable Studies of Climate and Trade Related Issues Tianle Yuan (NASA)
(30) Using Smart Meter Data to Forecast Grid Scale Electricity Demand Abraham Stanway (Amperon Holdings, Inc); Ydo Wexler (Amperon)
(31) Deep Learning for Wildlife Conservation and Restoration Efforts Clement Duhart (MIT Media Lab)

Ideas Track

Title Authors
(32) Reinforcement Learning for Sustainable Agriculture Jonathan Binas (Mila, Montreal); Leonie Luginbuehl (Department of Plant Sciences, University of Cambridge); Yoshua Bengio (Mila)
(33) Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem Honorable Mention Christian A Schroeder (University of Oxford); Thomas Hornigold (University of Oxford)
(34) Using Natural Language Processing to Analyze Financial Climate Disclosures Sasha Luccioni (Mila); Hector Palacios (Element AI)
(35) Machine Learning-based Maintenance for Renewable Energy: The Case of Power Plants in Morocco Kris Sankaran (Montreal Institute for Learning Algorithms); Zouheir Malki (Polytechnique Montréal); Loubna Benabou (UQAR); Hicham Bouzekri (MASEN)
(36) GainForest: Scaling Climate Finance for Forest Conservation using Interpretable Machine Learning on Satellite Imagery 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)
(37) Machine Intelligence for Floods and the Built Environment Under Climate Change Kate Duffy (Northeastern University); Auroop Ganguly (Northeastern University)
(38) Predicting Marine Heatwaves using Global Climate Models with Cluster Based Long Short-Term Memory Hillary S Scannell (University of Washington); Chris Fraley (Tableau Software); Nathan Mannheimer (Tableau Software); Sarah Battersby (Tableau Software); LuAnne Thompson (University of Washington)
(39) ML-driven search for zero-emissions ammonia production materials Kevin McCloskey (Google)
(40) Low-carbon urban planning with machine learning 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))
(41) The Grid Resilience & Intelligence Platform (GRIP) Ashley Pilipiszyn (Stanford University)
(42) Meta-Optimization of Optimal Power Flow Mahdi Jamei (Invenia Labs); Letif Mones (Invenia Labs); Alex Robson (Invenia Labs); Lyndon White (Invenia Labs); James Requeima (Invenia Labs); Cozmin Ududec (Invenia Labs)
(43) Learning representations to predict landslide occurrences and detect illegal mining across multiple domains Aneesh Rangnekar (Rochester Institute of Technology); Matthew J Hoffman (Rochester Institute of Technology)
(44) Harness the Power of Artificial intelligence and -Omics to Identify Soil Microbial Functions in Climate Change Projection Yang Song (Oak Ridge National Lab); Dali Wang (Oak Ridge National Lab); Melanie Mayes (Oak Ridge National Lab)

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 https://icml.cc/ 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:

Accepted submissions will be invited to give poster presentations at the workshop, of which some will be selected for spotlight talks. Please contact climatechangeai.icml2019@gmail.com 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:

DEPLOYED track

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.

RESEARCH track

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).

Frequently Asked Questions

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: 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 climatechangeai.icml2019@gmail.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.