NeurIPS 2021 Workshop
Tackling Climate Change with Machine Learning

About

Many in the ML community wish to take action on climate change, but are unsure of the pathways through which they can have the most impact. This workshop highlights work that demonstrates that, while no silver bullet, ML can be an invaluable tool 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 theoretical advances to deployment of new technology. Many of these actions represent high-impact opportunities for real-world change, and are simultaneously interesting academic research problems.

This workshop is part of a series (ICML 2021, NeurIPS 2020, ICLR 2020, NeurIPS 2019, and ICML 2019). For this iteration of the workshop, the keynote talks and panel discussions will be particularly focused on ML as an enabling technology for empowering decision-makers in tackling climate change, though submitted works may be on any topic of relevance at the intersection of climate change and machine learning.

About NeurIPS

This workshop is part of the conference on Neural Information Processing Systems (NeurIPS), one of the premier conferences on machine learning. To attend the workshop, register for the Workshops component of NeurIPS at https://neurips.cc/Register/view-registration (registration opens Sep 14). The workshop is open to the public; it is not necessary to submit to the workshop in order to attend.

About the Workshop

Schedule

Coming soon!

Accepted Works

Works were submitted to one of two tracks: Papers or Proposals. Note that camera-ready submissions are not yet available for all accepted works, but will be updated before the workshop.

Papers

Title Authors
(1) Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning Siddha Ganju (Nvidia Corporation); Sayak Paul (Carted)
(2) Short-term Solar Irradiance Prediction from Sky Images Hoang Chuong Nguyen (Australia National University); Miaomiao Liu (The Australian National University)
(3) Towards Representation Learning for Atmospheric Dynamics Sebastian Hoffmann (University of Magdeburg); Christian Lessig (Otto-von-Guericke-Universitat Magdeburg)
(4) Addressing Deep Learning Model Uncertainty in Long-Range Climate Forecasting with Late Fusion Ken C. L. Wong (IBM Research – Almaden Research Center); Hongzhi Wang (IBM Almaden Research Center); Etienne E Vos (IBM); Bianca Zadrozny (IBM Research); Campbell D Watson (IBM Reserch); Tanveer Syeda-Mahmood (IBM Research)
(5) Predicting Discharge in Catchment Outlet Using Deep Learning: Case Study of the Ansongo-Niamey Basin Peniel J. Y. Adounkpe (WASCAL); Eric Alamou (Université d'Abomey-Calavi); Belko Diallo (WASCAL); Abdou Ali (AGRHYMET Regional Centre)
(6) Accurate and Timely Forecasts of Geologic Carbon Storage using Machine Learning Methods Dan Lu (Oak Ridge National Laboratory); Scott Painter (Oak Ridge National Laboratory); Nicholas Azzolina (University of North Dakota); Matthew Burton-Kelly (University of North Dakota)
(7) Towards debiasing climate simulations using unsuperviserd image-to-image translation networks James Fulton (University of Edinburgh); Ben Clarke (Oxford University)
(8) Using Non-Linear Causal Models to StudyAerosol-Cloud Interactions in the Southeast Pacific Andrew Jesson (University of Oxford); Peter Manshausen (University of Oxford); Alyson Douglas (University of Oxford); Duncan Watson-Parris (University of Oxford); Yarin Gal (University of Oxford); Philip Stier (University of Oxford)
(9) Memory to Map: Improving Radar Flood Maps With Temporal Context and Semantic Segmentation Veda Sunkara (Cloud to Street); Nicholas Leach (Cloud to Street); Siddha Ganju (Nvidia)
(10) Hurricane Forecasting: A Novel Multimodal Machine Learning Framework Léonard Boussioux (MIT, CentraleSupélec); Cynthia Zeng (MIT); Dimitris Bertsimas (MIT); Théo J Guenais (Harvard University)
(11) Improved Drought Forecasting Using Surrogate Quantile And Shape (SQUASH) Loss Devyani Lambhate Lambhate (Indian Institute of Science); Smit Marvaniya (IBM Research India); Jitendra Singh (IBM Research - India); David Gold (IBM)
(12) Global ocean wind speed estimation with CyGNSSnet Caroline Arnold (German Climate Computing Center); Milad Asgarimehr (German Research Centre for Geosciences)
(13) Predicting Critical Biogeochemistry of the Southern Ocean for Climate Monitoring Ellen Park (MIT); Jae Deok Kim (MIT-WHOI); Nadege Aoki (MIT); Yumeng Cao (MIT); Yamin Arefeen (Massachusetts Institute of Technology); Matthew Beveridge (Massachusetts Institute of Technology); David P Nicholson (Woods Hole Oceanographic Institution); Iddo Drori (MIT)
(14) On the Generalization of Agricultural Drought Classification from Climate Data Julia S Gottfriedsen (DLR); Max Berrendorf (Ludwig-Maximilians-Universität München); Pierre Gentine (Columbia University); Markus Reichstein (Max Planck Institute for Biogeochemistry, Jena; Michael Stifel Center Jena for Data-Driven and Simulation Science, Jena); Katja Weigel (niversity of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany); Birgit Hassler (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany); Veronika Eyring (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany; University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany)
(15) Mapping Post-Climate Change Biogeographical Regions with Deep Latent Variable Models Christopher Krapu (Oak Ridge National Lab - Oak Ridge, TN)
(16) Rotation Equivariant Deforestation Segmentation and Driver Classification Joshua Mitton (University of Glasgow); Roderick Murray-Smith (University of Glasgow)
(17) WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data Rupa Kurinchi-Vendhan (Caltech); Björn Lütjens (MIT); Ritwik Gupta (University of California, Berkeley); Lucien D Werner (California Institute of Technology); Dava Newman (MIT); Steven Low (California Institute of Technology)
(18) Meta-Learned Bayesian Optimization for Calibrating Building Simulation Models with Multi-Source Data Sicheng Zhan (NUS); Gordon Wichern (Mitsubishi Electric Research Laboratories (MERL)); Christopher Laughman (Mitsubishi Electric Research Laboratories); Ankush Chakrabarty (Mitsubishi Electric Research Labs)
(19) MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather Sylwester Klocek (Microsoft Corporation); Haiyu Dong (Microsoft); Matthew Dixon (Microsoft Corporation); Panashe Kanengoni (Microsoft Corporation); Najeeb Kazmi (Microsoft); Pete Luferenko (Microsoft Corporation); Zhongjian Lv (Microsoft Corporation); Shikhar Sharma (); Jonathan Weyn (Microsoft); Siqi Xiang (Microsoft Corporation)
(20) SunCast: Solar Irradiance Nowcasting from Geosynchronous Satellite Data Dhileeban Kumaresan (UC Berkeley); Richard Wang (UC Berkeley); Ernesto A Martinez (UC Berkeley); Richard Cziva (UC Berkeley); Alberto Todeschini (UC Berkeley); Colorado J Reed (University of California, Berkeley); Puya Vahabi (UC Berkeley)
(21) Synthetic Imagery Aided Geographic Domain Adaptation for Rare Energy Infrastructure Detection in Remotely Sensed Imagery Wei Hu (Duke University); Tyler Feldman (Duke University); Eddy Lin (Duke University); Jose Luis Moscoso (Duke); Yanchen J Ou (Duke University); Natalie Tarn (Duke University); Baoyan Ye (Duke University); Wendy Zhang (Duke University); Jordan Malof (Duke University); Kyle Bradbury (Duke University)
(22) Being the Fire: A CNN-Based Reinforcement Learning Method to Learn How Fires Behave Beyond the Limits of Physics-Based Empirical Models William L Ross (Stanford)
(23) Subseasonal Solar Power Forecasting via Deep Sequence Learning Saumya Sinha (University of Colorado, Boulder); Bri-Mathias S Hodge (University of Colorado Boulder); Claire Monteleoni (University of Colorado Boulder)
(24) A Transfer Learning-Based Surrogate Model for Geological Carbon Storage with Multi-Fidelity Training Data Su Jiang (Stanford University)
(25) National Cropland Classification with Agriculture Census Information and EO Datasets Junshi Xia (RIKEN); Naoto Yokoya (The University of Tokyo); Bruno Adriano (RIKEN Center for Advanced Intelligence Project (AIP))
(26) FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection Anshuman Dewangan (University of California, San Diego); Mai Nguyen (University of California, San Diego); Garrison Cottrell (UC San Diego)
(27) Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery Joëlle Hanna (University of St. Gallen); Michael Mommert (University of St. Gallen); Linus M. Scheibenreif (University of St. Gallen); Damian Borth (University of St. Gallen)
(28) ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models Salva Rühling Cachay (Technical University of Darmstadt); Venkatesh Ramesh (MILA); Jason N. S. Cole (Environment and Climate Change Canada); Howard Barker (Environment and Climate Change Canada); David Rolnick (McGill University, Mila)
(29) A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction Joshua Fan (Cornell University); Junwen Bai (Cornell University); Zhiyun Li (Cornell University); Ariel Ortiz-Bobea (Cornell); Carla P Gomes (Cornell University)
(30) Learned Benchmarks for Subseasonal Forecasting Soukayna Mouatadid (University of Toronto); Paulo Orenstein (IMPA); Genevieve E Flaspohler (MIT); Miruna Oprescu (Microsoft Research); Judah Cohen (AER); Franklyn Wang (Harvard University); Sean Knight (MIT); Maria Geogdzhayeva (MIT); Sam Levang (Salient Predictions Inc.); Ernest Fraenkel (MIT); Lester Mackey (Microsoft Research)
(31) Emissions-aware electricity network expansion planning via implicit differentiation Anthony Degleris (Stanford University); Lucas Fuentes (Stanford); Abbas El Gamal (Stanford University); Ram Rajagopal (Stanford University)
(32) Amortized inference of Gaussian process hyperparameters for improved concrete strength trajectory prediction Kristen Severson (Microsoft Research); Olivia Pfeiffer (MIT); Jie Chen (IBM Research); Kai Gong (MIT); Jeremy Gregory (Massachusetts Institute of Technology); Richard Goodwin (IBM Research); Elsa Olivetti (Massachusetts Institute of Technology)
(33) A data integration pipeline towards reliable monitoring of phytoplankton and early detection of harmful algal blooms Bruna Guterres (Universidade Federal do Rio Grande - FURG); Sara khalid (University of Oxford); Marcelo Pias (Federal University of Rio Grande); Silvia Botelho (Federal University of Rio Grande)
(34) Identifying Distributional Differences in Convective Evolution Prior to Rapid Intensification in Tropical Cyclones Irwin H McNeely (Carnegie Mellon University); Galen Vincent (Carnegie Mellon University); Rafael Izbicki (UFSCar); Kimberly Wood (Mississippi State University); Ann B. Lee (Carnegie Mellon University)
(35) Predicting Atlantic Multidecadal Variability Glenn Liu (Massachusetts Institute of Technology); Peidong Wang (MIT); Matthew Beveridge (Massachusetts Institute of Technology); Young-Oh Kwon (Woods Hole Oceanographic Institution); Iddo Drori (MIT)
(36) Identifying the atmospheric drivers of drought and heat using a smoothed deep learning approach Magdalena Mittermeier (Ludwig-Maximilians-Universität München); Maximilian Weigert (Ludwig-Maximilians-Universität München); David Ruegamer (LMU Munich)
(37) Learning to identify cracks on wind turbine blade surfaces using drone-based inspection images Akshay B Iyer (SkySpecs, Inc.); Linh V Nguyen (SkySpecs Inc); Shweta Khushu (SkySpecs Inc.)
(38) Evaluating Pretraining Methods for Deep Learning on Geophysical Imaging Datasets James Chen (Kirby School)
(39) An Automated System for Detecting Visual Damages of Wind Turbine Blades Linh V Nguyen (SkySpecs Inc); Shweta Khushu (SkySpecs Inc.); Akshay B Iyer (SkySpecs, Inc.)
(40) Power System Dynamic Simulation Using Fourier Neural Operators Wenqi Cui (University of Washington); Weiwei Yang (Microsoft Research); Baosen Zhang (University of Washington)
(41) HyperionSolarNet: Solar Panel Detection from Aerial Images Poonam Parhar (UCBerkeley); Ryan Sawasaki (UCBerkeley); Alberto Todeschini (UC Berkeley); Colorado Reed (UC Berkeley); Hossein Vahabi (University California Berkeley); Nathan Nusaputra (UC Berkeley); Felipe Vergara (UC Berkeley)
(42) Prediction of Household-level Heat-Consumption using PSO enhanced SVR Model Satyaki Chatterjee (Pattern Recognition Lab, FAU Erlangen-Nuremberg); Siming Bayer (Pattern Recognition Lab, Friedrich-Alexander University); Andreas K Maier (Pattern Recognition Lab, FAU Erlangen-Nuremberg)
(43) EcoLight: Reward Shaping in Deep Reinforcement Learning for Ergonomic Traffic Signal Control Pedram Agand (Simon Fraser University); Alexey Iskrov (Breeze Labs Inc.); Mo Chen (Simon Fraser University)
(44) Data Driven Study of Estuary Hypoxia Monwer Hussain (University of New-Brunswick); Guillaume Durand (National Research Council Canada); Michael Coffin (Department of Fisheries and Oceans Canada); Julio J Valdés (National Research Council Canada); Luke Poirier (Department of Fisheries and Oceans Canada)
(45) Safe Learning for Voltage Control Wenqi Cui (University of Washington); Jiayi Li (University of Washington); Baosen Zhang (University of Washington)
(46) NoFADE: Analyzing Diminishing Returns on CO2 Investment Andre Fu (University of Toronto); Justin B Tran (University of Toronto); Andy Xie (University of Toronto); Jonathan T Spraggett (University of Toronto); Elisa Ding (University of Toronto); Chang-Won Lee (University of Toronto); Kanav Singla (University of Toronto); Mahdi S. Hosseini (University of New Brunswick); Konstantinos N Plataniotis (UofT)
(47) High-resolution rainfall-runoff modeling using graph neural network Zhongrun Xiang (University of Iowa); Ibrahim Demir (The University of Iowa)
(48) Machine Learning for Snow Stratigraphy Classification Julia Kaltenborn (McGill University)
(49) DEM Super-Resolution with EfficientNetV2 Bekir Z Demiray (University of Iowa); Muhammed A Sit (The University of Iowa); Ibrahim Demir (The University of Iowa)
(50) Learning to Dissipate Traffic Jams with Piecewise Constant Control Mayuri Sridhar (MIT); Cathy Wu ()
(51) Multi-objective Reinforcement Learning Controller for Multi-Generator Industrial Wave Energy Converter Soumyendu Sarkar (Hewlett Packard Enterprise); Vineet Gundecha (Hewlett Packard Enterpise); Alexander Shmakov (UC Irvine); Sahand Ghorbanpour (Hewlett Packard Enterprise); Ashwin Ramesh Babu (Hewlett Packard Enterprise Labs); Paolo Faraboschi (HPE); mathieu Cocho (Carnegie Clean Energy); Alexandre Pichard (Carnegie Clean Energy); Jonathan Fievez (Carnegie Clean Energy)
(52) Resolving Super Fine-Resolution SIF via Coarsely-Supervised U-Net Regression Joshua Fan (Cornell University); Di Chen (Cornell University); Jiaming Wen (Cornell University); Ying Sun (Cornell University); Carla P Gomes (Cornell University)
(53) PreDisM: Pre-Disaster Modelling With CNN Ensembles for At-Risk Communities Vishal Anand (Columbia University); Yuki Miura (Columbia University)
(54) A hybrid convolutional neural network/active contour approach to segmenting dead trees in aerial imagery Jacquelyn Shelton (Hong Kong Polytechnic University); Przemyslaw Polewski (TomTom Location Technology Germany GmbH); Drew J Lipman (MITRE corp.); Marco Heurich (Bavarian Forest National Park); Wei Yao (The Hong Kong Polytechnic University)
(55) Semi-Supervised Classification and Segmentation on High Resolution Aerial Images Sahil S Khose (Manipal Institute of Technology); Abhiraj Tiwari (Manipal Institute of Technology); Ankita Ghosh (Manipal Institute of Technology)
(56) Reducing the barriers of acquiring species-level ground-truth on passive acoustic monitoring data using neural network assisted intelligent sampling techniques Jacob G Ayers (UC San Diego); Ryan Kastner (University of California San Diego)
(57) Two-phase training mitigates class imbalance for camera trap image classification with CNNs Ruben Cartuyvels (KULeuven)
(58) Capturing Electricity Market Dynamics in the Optimal Trading of Strategic Agents using Neural Network Constrained Optimization Mihály Dolányi (KU Leuven); Kenneth Bruninx (KU Leuven); Jean-François Toubeau (Faculté Polytechnique (FPMs), Université de Mons (UMONS)); Erik Delaue (KU Leuven)
(59) Leverage Machine Learning to Predict the Autoconversion Rates from Satellite Data Maria C Novitasari (University College London); Johannes Quaas (University of Leipzig); Miguel Rodrigues (University College London)
(60) Towards Automatic Transformer-based Cloud Classification and Segmentation Roshan Roy (Birla Institute of Technology and Science, Pilani); Ahan M R (BITS Pilani); Vaibhav Soni (MANIT Bhopal); Ashish Chittora (BITS Pilani)
(61) Scalable coastal inundation mapping using machine learning Ophelie Meuriot (IBM Research Europe); Anne Jones (IBM Research)

Proposals

Title Authors
(62) Machine Learning in Automating Carbon Sequestration Site Assessment Jay Chen (Shell); Ligang Lu (Shell); Mohamed Sidahmed (Shell); Taixu Bai (Shell); Ilyana Folmar (Shell); Puneet Seth (Shell); Manoj Sarfare (Shell); Duane Mikulencak (Shell); Ihab Akil (Shell)
(63) A Risk Model for Predicting Powerline-induced Wildfires in Distribution System Mengqi Yao (University of California Berkeley)
(64) Detecting Abandoned Oil Wells Using Machine Learning and Semantic Segmentation Michelle Lin (McGill University); David Rolnick (McGill University, Mila)
(65) Machine learning-enabled model-data integration for predicting subsurface water storage Dan Lu (Oak Ridge National Laboratory); Eric Pierce (Oak Ridge National Laboratory); Shih-Chieh Kao (Oak Ridge National Laboratory); David Womble (Oak Ridge National Laboratory); LI LI (Pennsylvania State University); Daniella Rempe (The University of Texas at Austin)
(66) Hybrid physics-based and data-driven modeling with calibrated uncertainty for lithium-ion battery degradation diagnosis and prognosis Jing Lin (Institute for Infocomm Research); Yu Zhang (I2R); Edwin Khoo (Institute for Infocomm Research)
(67) On the use of Deep Generative Models for "Perfect'" Prognosis Climate Downscaling Jose González-Abad (Institute of Physics of Cantabria); Jorge Baño-Medina (Institute of Physics of Cantabria); Ignacio Heredia (Instituto de Física de Cantabria)
(68) A Deep Learning application towards transparent communication for Payment for Forest Environmental Services (PES) Lan HOANG (IBM Research); Thuy Thu Phan (Center for International Forestry Research (CIFOR))
(69) A NLP-based Analysis of Alignment of Organizations' Climate-Related Risk Disclosures with Material Risks and Metrics Elham Kheradmand (University of Montreal); Didier Serre (Clearsum); Manuel Morales (University of Montreal); Cedric B Robert (Clearsum)
(70) Unsupervised Machine Learning framework for sensor placement optimization: analyzing methane leaks Shirui Wang (University of Houston); Sara Malvar (Microsoft); Leonardo Nunes (Microsoft); Kim Whitehall (Microsoft); Yagna Oruganti (Microsoft); Yazeed Alaudah (Microsoft); Anirudh Badam (Microsoft)
(71) Multi-agent reinforcement learning for renewable integration in the electric power grid Vincent Mai (Mila, Université de Montréal); Tianyu Zhang (Mila, Université de Montréal); Antoine Lesage-Landry (Polytechnique Montréal & GERAD)
(72) Machine Learning Speeding Up the Development of Portfolio of New Crop Varieties to Adapt to and Mitigate Climate Change Abdallah Bari (OperAI Canada - Operational AI); Hassan Ouabbou (INRA); Abderrazek Jilal (INRA); Frederick Stoddard (University of Helsinki); Mikko Sillanpää (University of Oulu); Hamid Khazaei (World Vegetable Center)
(73) Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark Alexandre Lacoste (ServiceNow); Evan D Sherwin (Stanford University, Energy and Resources Engineering); Hannah R Kerner (University of Maryland); Hamed Alemohammad (Radiant Earth Foundation); Björn Lütjens (MIT); Jeremy A Irvin (Stanford); David Dao (ETH Zurich); Alex Chang (Service Now); Mehmet Gunturkun (Element Ai); Alexandre Drouin (ServiceNow); Pau Rodriguez (Element AI); David Vazquez (ServiceNow)
(74) Optimization of Agricultural Management for SoilCarbon Sequestration based on Deep ReinforcementLearning and Large-Scale Simulations Jing Wu (University of Illinois Urbana-Champaign); Pan Zhao (University of Illinois Urbana-Champaign); Ran Tao (University of Illinois Urbana-Champaign); Naira Hovakimyan (UIUC); Guillermo Marcillo (University of Illinois at Urbana-Champaign); Nicolas Martin (University of Illinois at Urbana-Champaign); Carla Ferreira (Royal Institute of Technology); Zahra Kalantari (Royal Institute of Technology); Jennifer Hobbs (IntelinAir Inc.)
(75) Leveraging machine learning for identify hydrological extreme events under global climate change Ying-Jung C Deweese (Georgia Insititute of Technology)
(76) Predicting Cascading Failures in Power Systems using Graph Convolutional Networks Tabia Ahmad (University of Strathclyde); Yongli Zhu (Texas A&M Universersity); Panagiotis Papadopoulos (University of Strathclyde)
(77) DeepQuake: Artificial Intelligence for Earthquake Forecasting Using Fine-Grained Climate Data Yash Narayan (The Nueva School)

Organizers

Maria João Sousa (IST, ULisboa)
Hari Prasanna Das (UC Berkeley)
Simone Fobi (Columbia University)
Ján Drgoňa (PNNL)
Tegan Maharaj (Mila, UToronto)
Yoshua Bengio (Mila, UdeM)

Call for Submissions

We invite submissions of short papers using machine learning to address problems in climate mitigation, adaptation, or modeling, including but not limited to the following topics:

All machine learning techniques are welcome, from kernel methods to deep learning. Each submission should make clear why the application has (or could have) a pathway to positive impacts regarding climate change. We highly encourage submissions which make their data publicly available. Accepted submissions will be invited to give poster presentations, of which some will be selected for spotlight talks.

The workshop does not publish proceedings, and submissions are non-archival. Submission to this workshop does not preclude future publication. Previously published work may be submitted under certain circumstances (see the FAQ).

All papers and proposals submissions must be through the submission 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. Authors are required to use the workshop style template (based on the NeurIPS style files), available for LaTeX and docx format.

All tutorials submissions must be through this application form.

Please see the Tips for Submissions and FAQ, and contact climatechangeai.neurips2021@gmail.com with questions.

Submission Tracks

There are three tracks for submissions: (i) Papers, (ii) Proposals, (iii) Tutorials. Submissions are limited to 4 pages for the Papers track, and 3 pages for the Proposals track, in PDF format (see examples from ICML 2021, NeurIPS 2020, ICLR 2020, NeurIPS 2019, and ICML 2019). References do not count towards this total. Supplementary appendices are allowed but will be read at the discretion of the reviewers. All submissions must explain why the proposed work has (or could have) positive impacts regarding climate change.

PAPERS Track

Work that is in progress, published, and/or deployed.

Submissions for the Papers track should describe projects relevant to climate change that involve machine learning. These may include (but are not limited to) academic research; deployed results from startups, industry, public institutions, etc.; and climate-relevant datasets.

Submissions should provide experimental or theoretical validation of the method presented, as well as specifying what gap the method fills. Authors should clearly illustrate a pathway to climate impact, i.e., identify the way in which this work fits into broader efforts to address climate change. Algorithms need not be novel from a machine learning perspective if they are applied in a novel setting. Details of methodology need not be revealed if they are proprietary, though transparency is highly encouraged.

Submissions creating novel datasets are welcomed. Datasets should be 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.

PROPOSALS Track

Early-stage work and detailed descriptions of ideas for future work.

Submissions for the Proposals track should describe detailed ideas for how machine learning can be used to solve climate-relevant problems. While less constrained than the Papers track, Proposals will be subject to a very high standard of review. Ideas should be justified as extensively as possible, including motivation for why the problem being solved is important in tackling climate change, discussion of why current methods are inadequate, explanation of the proposed method, and discussion of the pathway to climate impact. Preliminary results are optional.

TUTORIALS Track

Interactive notebooks for insightful step-by-step walkthroughs.

Submissions for the Tutorials track should introduce or demonstrate the use of ML methods and tools such as libraries, packages, services, datasets, or frameworks to address a problem related to climate change. Tutorial proposals (250 words, due Aug 23) should take the form of an abstract and should include a clear and concise description of users’ expected learning outcomes from the tutorial. Accepted submissions (to be notified by Aug 30) will be given 6 weeks for the initial tutorial development (“midterm” deadline Oct 11), after which tutorial creators will collaborate with the CCAI Tutorials Team, who will review the tutorials periodically and provide iterative feedback, while the creators continue to develop and improve their work over the course of another 6 weeks. Final tutorial submissions (due Nov 22) should be in the form of executable notebooks (e.g. Jupyter, Colab) in a hosted code environment.

Tips for Submissions

Addressing Impact

Tackling climate change requires translating ideas into action. The guidelines below will help you clearly present the importance of your work to a broad audience, hopefully including relevant decision-makers in industry, government, nonprofits, and other areas.

Mentorship Program

We are hosting a mentorship program to facilitate exchange between potential workshop submitters and experts working in topic areas relevant to the workshop. The goal of this program is to foster cross-disciplinary collaborations and ultimately increase the quality and potential impact of submitted work.

Expectations:

Mentors are expected to guide mentees during the CCAI mentorship program as they prepare submissions for this workshop.

Examples of mentor-mentee interactions may include:

Mentees are expected to initiate contact with their assigned mentor and put in the work and effort necessary to prepare a Paper or Proposal submission by Sep 18.

We suggest that after the mentor-mentee matching is made, a first (physical or digital) meeting should take place within the first week (Aug 19-25) to discuss the Paper or Proposal and set expectations for the mentorship period. Subsequent interactions can take place either through meetings or via email discussions, following the expectations set during the initial meeting, culminating in a final version of a Paper or Proposal submitted via the CMT portal by Sep 18.

Mentors and mentees must abide by the following Code of Conduct: https://www.climatechange.ai/code_of_conduct.

Application

Applications are due by Aug 16.

Frequently Asked Questions

Mentorship Program FAQ

Q: Are mentors allowed to be authors on the paper for which they provided mentorship?
A: Yes, mentors can be co-authors but not reviewers.

Q: What happens if the mentor/mentee does not fulfill their duties, or if major issues come up?
A: Please email us at climatechangeai.neurips2021@gmail.com and we will do our best to help resolve the situation. Potential breaches of the Code of Conduct will be responded to promptly as detailed therein.

Q: What happens if I apply to be a mentee but do not get paired with a mentor?
A: While we will do our best, we cannot guarantee pairings for everyone. Even if you do not get paired with a mentor, we encourage you to submit a Paper or Proposal to the workshop, and our reviewers will provide you with guidance and feedback on how to improve it.

Q: What happens if my submission does not get accepted to the workshop?
A: While the mentorship program is meant to give early-career researchers and students the opportunity to improve the quality of their work, sometimes submissions will need further polishing and elaboration before being ready for presentation at a CCAI workshop. If this is the case, we invite you to take into account the comments made by the reviewers and to resubmit again to a subsequent CCAI workshop.

Q: I cannot guarantee that I can commit at least 4 hours to the program over the time period. Should I still apply as a mentor?
A: No. While the 4 hour time commitment is a suggestion, we do believe that it is necessary to ensure that all mentees receive the help and guidance they need.

Q: I do not have a background in machine learning; can I still apply to be a mentor/mentee?
A: Yes! We welcome applications from domains that are complementary to machine learning to solve the problems that we are targeting.

Q: What happens if my mentor/mentee wants to continue meeting after the workshop?
A: We welcome and encourage continued interactions after the official mentorship period. That said, neither the mentor nor the mentee should feel obligated to maintain contact.

Submission FAQ

Q: How can I keep up to date on this kind of stuff?
A: Sign up for our mailing list!

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.

Q: It’s hard for me to fit my submission on 3 or 4 pages. What should I do?
A: Feel free to include appendices with additional material (these should be part of the same PDF file as the main submission). Do not, however, put essential material in an appendix, as it will be read at the discretion of the reviewers.

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: Do submissions for the Proposals 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.

Q: Can I submit previously published work to this workshop?
A: Yes, though under limited circumstances. In particular, work that has previously been published at non-machine learning venues may be eligible for submission; however, work that has been published in conferences on machine learning or related fields is likely not eligible. If your work was previously accepted to a Climate Change AI workshop, this work should have changed or matured substantively to be eligible for resubmission. Please contact climatechangeai.neurips2021@gmail.com with any questions.

Q: Can I submit work to this workshop if I am also submitting to another NeurIPS 2021 workshop?
A: Yes. We cannot, however, guarantee that you will not be expected to present the material at a time that conflicts with the other workshop.