ICML 2021 Workshop
Tackling Climate Change with Machine Learning
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 was held as part of the International Conference on Machine Learning (ICML), one of the premier conferences on machine learning, which draws a wide audience of researchers and practitioners in academia, industry, and related fields.
Schedule Full Recording
The main workshop took place digitally on July 23rd, 2020, featuring 89 posters, 13 spotlight presentations, along with invited speakers and panels.
Works were submitted to one of two tracks: Papers or Proposals.
Click the links below for information about each submission, including slides, videos, and papers.
|(1) Examining the nexus of environmental policy, climate physics, and maritime shipping with deep learning models and space-borne data||Tianle Yuan (University of Maryland, NASA); Hua Song (NASA, SSAI); Chenxi Wang (University of Maryland, NASA); Kerry Meyer (NASA); Siobhan Light (University of Maryland); Sophia von Hippel (University of Arizona); Steven Platnick (NASA); Lazaros Oreopoulos (NASA); Robert Wood (University of Washington); Hans Mohrmann (University of Washington)|
|(2) A human-labeled Landsat-8 contrails dataset||Kevin McCloskey (Google); Scott Geraedts (Google); Brendan Jackman (Google); Vincent R. Meijer (Laboratory for Aviation and the Environment, Massachusetts Institute of Technology); Erica Brand (Google); Dave Fork (Google); John C. Platt (Google); Carl Elkin (Google); Christopher Van Arsdale (Google)|
|(3) Urban Tree Species Classification Using Aerial Imagery||Emily Waters (Anglia Ruskin University); Mahdi Maktabdar Oghaz (Anglia Ruskin University); Lakshmi Babu Saheer (Anglia Ruskin University)|
|(4) Estimation of Corporate Greenhouse Gas Emissions via Machine Learning||You Han (Bloomberg L.P.); Achintya Gopal (Bloomberg LP); Liwen Ouyang (Bloomberg L.P.); Aaron Key (Bloomberg LP)|
|(5) ANP-BBO: Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins||Ankush Chakrabarty (Mitsubishi Electric Research Labs); Gordon Wichern (Mitsubishi Electric Research Laboratories (MERL)); Christopher Laughman (Mitsubishi Electric Research Laboratories (MERL))|
|(6) Seasonal Sea Ice Presence Forecasting of Hudson Bay using Seq2Seq Learning||Nazanin Asadi (University of Waterloo); K Andrea Scott (University of Waterloo); Philippe Lamontagne (National Research Council Canada)|
|(7) Semantic Segmentation on Unbalanced Remote Sensing Classes for Active Fire||Xikun Hu (KTH Royal Institute of Technology); Alberto Costa Nogueira Junior (IBM Research Brazil); Tian Jin (College of Electronic Science, National University of Defense Technology)|
|(8) Improving Image-Based Characterization of Porous Media with Deep Generative Models||Timothy Anderson (Stanford University); Kelly Guan (Stanford University); Bolivia Vega (Stanford University); Laura Froute (Stanford University); Anthony Kovscek (Stanford University)|
|(9) Forest Terrain Identification using Semantic Segmentation on UAV Images||Muhammad Umar (Anglia Ruskin University); Lakshmi Babu Saheer (Anglia Ruskin University); Javad Zarrin (Anglia Ruskin University)|
|(10) Climate-based ensemble machine learning model to forecast Dengue epidemics||Rochelle Schneider (European Space Agency); Alessandro Sebastianelli (European Space Agency); Dario Spiller (Italian Space Agency); James Wheeler (European Space Agency); Raquel Carmo (European Space Agency); Artur Nowakowski (Warsaw University of Technology); Manuel Garcia-Herranz (UNICEF); Dohyung Kim (UNICEF); Hanoch Barlevi (UNICEF LACRO); Zoraya El Raiss Cordero (UNICEF LACRO); Silvia Liberata Ullo (University of Sannio); Pierre-Philippe Mathieu (European Space Agency); Rachel Lowe (London School of Hygiene & Tropical Medicine)|
|(11) Physics-Informed Graph Neural Networks for Robust Fault Location in Power Grids Best Paper: ML Innovation||Wenting Li (Los Alamos National Laboratory); Deepjyoti Deka (Los Alamos National Laboratory)|
|(12) Prediction of Boreal Peatland Fires in Canada using Spatio-Temporal Methods||Shreya Bali (Carnegie Mellon University); Sydney Zheng (Carnegie Mellon University); Akshina Gupta (Carnegie Mellon University); Yue Wu (None); Blair Chen (Carnegie Mellon University); Anirban Chowdhury (Carnegie Mellon University); Justin Khim (Carnegie Mellon University)|
|(13) Revealing the impact of global warming on climate modes using transparent machine learning and a suite of climate models||Maike Sonnewald (Princeton University); Redouane Lguensat (LSCE-IPSL); Aparna Radhakrishnan (Geophysical Fluid Dynamics Laboratory); Zoubero Sayibou (Bronx Community College); Venkatramani Balaji (Princeton University); Andrew Wittenberg (NOAA)|
|(14) Challenges in Applying Audio Classification Models to Datasets Containing Crucial Biodiversity Information||Jacob G Ayers (UC San Diego); Yaman Jandali (University of California, San Diego); Yoo-Jin Hwang (Harvey Mudd College); Erika Joun (University of California, San Diego); Gabriel Steinberg (Binghampton University); Mathias Tobler (San Diego Zoo Wildlife Alliance); Ian Ingram (San Diego Zoo Wildlife Alliance); Ryan Kastner (University of California San Diego); Curt Schurgers (University of California San Diego)|
|(15) Learning Optimal Power Flow with Infeasibility Awareness||Gang Huang (Zhejiang Lab); Longfei Liao (Zhejiang Lab); Lechao Cheng (Zhejiang Lab); Wei Hua (Zhejiang Lab)|
|(16) Reconstructing Aerosol Vertical Profiles with Aggregate Output Learning||Sofija Stefanovic (University of Oxford); Shahine Bouabid (University of Oxford); Philip Stier (University of Oxford); Athanasios Nenes (EPFL); Dino Sejdinovic (University of Oxford)|
|(17) Self-Attentive Ensemble Transformer: Representing Ensemble Interactions in Neural Networks for Earth System Models||Tobias S Finn (Universität Hamburg)|
|(18) DeepOPF-NGT: A Fast Unsupervised Learning Approach for Solving AC-OPF Problems without Ground Truth||Wanjun Huang (City University of Hong Kong); Minghua Chen (City University of Hong Kong)|
|(19) Commercial Vehicle Traffic Detection from Satellite Imagery with Deep Learning||Moritz Blattner (University of St. Gallen); Michael Mommert (University of St. Gallen); Damian Borth (University of St. Gallen)|
|(20) Probabilistic Short-Term Low-Voltage Load Forecasting using Bernstein-Polynomial Normalizing Flows||Marcel Arpogaus (Konstanz University of Applied Sciences); Marcus Voß (Technische Universität Berlin (DAI-Labor)); Beate Sick (ZHAW and University of Zurich); Mark Nigge-Uricher (Bosch.IO GmbH); Oliver Dürr (Konstanz University of Applied Sciences)|
|(21) Guided A* Search for Scheduling Power Generation Under Uncertainty||Patrick de Mars (UCL); Aidan O'Sullivan (UCL)|
|(22) DroughtED: A dataset and methodology for drought forecasting spanning multiple climate zones||Christoph D Minixhofer (The University of Edinburgh); Mark Swan (The University of Edinburgh); Calum McMeekin (The University of Edinburgh); Pavlos Andreadis (The University of Edinburgh)|
|(23) Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space||Linus M. Scheibenreif (University of St. Gallen); Michael Mommert (University of St. Gallen); Damian Borth (University of St. Gallen)|
|(24) Emulating Aerosol Microphysics with a Machine Learning||Paula Harder (Fraunhofer ITWM); Duncan Watson-Parris (University of Oxford); Dominik Strassel (Fraunhofer ITWM); Nicolas Gauger (TU Kaiserslautern); Philip Stier (University of Oxford); Janis Keuper (hs-offenburg)|
|(25) Automated Identification of Climate Risk Disclosures in Annual Corporate Reports||David Friederich (University of Bern); Lynn Kaack (ETH Zurich); Sasha Luccioni (Mila); Bjarne Steffen (ETH Zurich)|
|(26) Forecasting Black Sigatoka Infection Risks with Latent Neural ODEs||Yuchen Wang (University of Toronto); Matthieu Chan Chee (University of Toronto); Ziyad Edher (University of Toronto); Minh Duc Hoang (University of Toronto); Shion Fujimori (University of Toronto); Jesse Bettencourt (University of Toronto)|
|(27) A Reinforcement Learning Approach to Home Energy Management for Modulating Heat Pumps and Photovoltaic Systems||Lissy Langer (TU Berlin)|
|(28) Reinforcement Learning for Optimal Frequency Control: A Lyapunov Approach||Wenqi Cui (University of Washington); Baosen Zhang (University of Washington)|
|(29) Modeling Bird Migration by Disaggregating Population Level Observations||Miguel Fuentes (University of Massachusetts, Amherst); Benjamin Van Doren (Cornell University); Daniel Sheldon (University of Massachusetts, Amherst)|
|(30) Power Grid Cascading Failure Mitigation by Reinforcement Learning||Yongli Zhu (Texas A&M University)|
|(31) Decadal Forecasts with ResDMD: a residual DMD neural network||EDUARDO ROCHA RODRIGUES (IBM Research); Campbell Watson (IBM Reserch); Bianca Zadrozny (IBM Research); David Gold (IBM Global Business Services)|
|(32) TweetDrought: A Deep-Learning Drought Impacts Recognizer based on Twitter Data||Beichen Zhang (University of Nebraska-Lincoln); Frank Schilder (Thomson Reuters); Kelly Smith (National Drought Mitigation Center); Michael Hayes (University of Nebraska-Lincoln); Sherri Harms (University of Nebraska-Kearney); Tsegaye Tadesse (University of Nebraska-Lincoln)|
|(33) Graph Neural Networks for Learning Real-Time Prices in Electricity Market||Shaohui Liu (University of Texas at Austin); Chengyang Wu (University of Texas at Austin); Hao Zhu (University of Texas at Austin)|
|(34) Learning Granger Causal Feature Representations||Gherardo Varando (Universitat de València); Miguel-Ángel Fernández-Torres (Universitat de València); Gustau Camps-Valls (Universitat de València)|
|(35) DeepPolicyTracker: Tracking Changes In Environmental Policy In The Brazilian Federal Official Gazette With Deep Learning||Flávio N Cação (University of Sao Paulo); Anna Helena Reali Costa (Universidade de São Paulo); Natalie Unterstell (Política por Inteiro); Liuca Yonaha (Política por Inteiro); Taciana Stec (Política por Inteiro); Fábio Ishisaki (Política por Inteiro)|
|(36) Fast-Slow Streamflow Model Using Mass-Conserving LSTM||Miguel Paredes Quinones (IBM Research); Maciel Zortea (IBM Research); Leonardo Martins (IBM Research)|
|(37) Attention For Damage Assessment||Tashnim Chowdhury (University of Maryland Baltimore County); Maryam Rahnemoonfar (University of Maryland Baltimore County)|
|(38) Online LSTM Framework for Hurricane Trajectory Prediction||Ding Wang (Michigan State University); Pang-Ning Tan (MSU)|
|(39) Controlling Weather Field Synthesis Using Variational Autoencoders||Dario Augusto Borges Oliveira (IBM Research); Jorge Luis Guevara Diaz (IBM Research); Bianca Zadrozny (IBM Research); Campbell Watson (IBM Reserch)|
|(40) ForestViT: A Vision Transformer Network for Convolution-free Multi-label Image Classification in Deforestation Analysis||Maria Kaselimi (National Technical University of Athens); Athanasios Voulodimos (University of West Attica); Ioannis Daskalopoulos (University of West Attica); Nikolaos Doulamis (National Technical University of Athens); Anastasios Doulamis (Technical University of Crete)|
|(41) Reducing Carbon in the Design of Large Infrastructure Scheme with Evolutionary Algorithms||Matt Blythe (Continuum Industries)|
|(42) An Accurate and Scalable Subseasonal Forecasting Toolkit for the United States||Soukayna Mouatadid (University of Toronto); Paulo Orenstein (IMPA); Genevieve E Flaspohler (MIT); Miruna Oprescu (Microsoft Research); Judah Cohen (AER); Franklyn Wang (Harvard); Sean Knight (MIT); Ernest Fraenkel (MIT); Lester Mackey (Microsoft Research)|
|(43) Self-supervised Contrastive Learning for Irrigation Detection in Satellite Imagery||Chitra Agastya (UC Berkeley, IBM); Sirak Ghebremusse (UC Berkeley); Ian Anderson (UC Berkeley); Colorado Reed (UC Berkeley); Hossein Vahabi (University California Berkeley); Alberto Todeschini (UC Berkeley)|
|(44) Extreme Precipitation Seasonal Forecast Using a Transformer Neural Network||Daniel Salles Civitarese (IBM Research, Brazil); Daniela Szwarcman (IBM Research); Bianca Zadrozny (IBM Research); Campbell Watson (IBM Reserch)|
|(45) BERT Classification of Paris Agreement Climate Action Plans||Tom Corringham (Scripps Institution of Oceanography); Daniel Spokoyny (Carnegie Mellon University); Eric Xiao (University of California San Diego); Christopher Cha (University of California San Diego); Colin Lemarchand (University of California San Diego); Mandeep Syal (University of California San Diego); Ethan Olson (University of California San Diego); Alexander Gershunov (Scripps Institution of Oceanography)|
|(46) Quantification of Carbon Sequestration in Urban Forests||Levente Klein (IBM Research); Wang Zhou (IBM Research); Conrad M Albrecht (IBM Research)|
|(47) A comparative study of stochastic and deep generative models for multisite precipitation synthesis||Jorge Luis Guevara Diaz (IBM Research); Dario Augusto Borges Oliveira (IBM Research); Bianca Zadrozny (IBM Research); Campbell Watson (IBM Reserch)|
|(48) Self-Supervised Learning on Multispectral Satellite Data for Near-Term Solar Forecasting||Akansha Singh Bansal (University of Massachusetts Amherst); Trapit Bansal (University of Massachusetts Amherst); David Irwin (University of Massachusetts Amherst)|
|(49) Refining Ice Layer Tracking through Wavelet combined Neural Networks||Debvrat Varshney (University of Maryland Baltimore County); Masoud Yari (College of Engineering and Information Technology, University of Maryland Balitimore County); Tashnim Chowdhury (University of Maryland Baltimore County); Maryam Rahnemoonfar (University of Maryland Baltimore County)|
|(50) Forecasting Sea Ice Concentrations using Attention-based Ensemble LSTM||Sahara Ali (University of Maryland, Baltimore County); Yiyi Huang (University of Maryland, Baltimore County); Xin Huang (University of Maryland, Baltimore County); Jianwu Wang (University of Maryland, Baltimore County)|
|(51) Toward efficient calibration of higher-resolution Earth System Models Best Paper: Pathway to Impact||Christopher Fletcher (University of Waterloo); William McNally (University of Waterloo); John Virgin (University of Waterloo)|
|(52) Visual Question Answering: A Deep Interactive Framework for Post-Disaster Management and Damage Assessment||Argho Sarkar (University of Maryland, Baltimore County); Maryam Rahnemoonfar (University of Maryland Baltimore County)|
|(53) Designing Bounded min-knapsack Bandits algorithm for Sustainable Demand Response||Akansha Singh (Indian Institute of Technology, Ropar); Meghana Reddy (Indian Institute of Technology, Ropar); Zoltan Nagy (University of Texas); Sujit P. Gujar (Machine Learning Laboratory, International Institute of Information Technology, Hyderabad); Shweta Jain (Indian Institute of Technology Ropar)|
|(54) Sky Image Prediction Using Generative Adversarial Networks for Solar Forecasting||Yuhao Nie (Stanford University); Andea Scott (Stanford University); Eric Zelikman (Stanford University); Adam Brandt (Stanford University)|
|(55) EVGen: Adversarial Networks for Learning Electric Vehicle Charging Loads and Hidden Representations||Robert Buechler (Stanford University); Emmanuel O Balogun (Stanford University); Arun Majumdar (Stanford University); Ram Rajagopal (Stanford University)|
|(56) Reconstruction of Long-Term Historical Electricity Demand Data||Reshmi Ghosh (Carnegie Mellon University); Michael Craig (University of Michigan); H.Scott Matthews (Carnegie Mellon University); Laure Berti-Equille (IRD)|
|(57) A Set-Theoretic Approach to Safe Reinforcement Learning in Power Systems||Daniel Tabas (University of Washington); Baosen Zhang (University of Washington)|
|(58) A study of battery SoC scheduling using machine learning with renewable sources||Daisuke Kawamoto (Sony Computer Science Laboratories, Inc.); Gopinath Rajendiran (CSIR Central Scientific Instruments Organisation, Chennai)|
|(59) Multivariate climate downscaling with latent neural processes||Anna Vaughan (Univeristy of Cambridge); Nic Lane (University of Cambridge); Michael Herzog (University of Cambridge)|
|(60) FIRE-ML: A Remotely-sensed Daily Wildfire Forecasting Dataset for the Contiguous United States||Casey A Graff (UC Irvine)|
|(61) IowaRain: A Statewide Rain Event Dataset Based on Weather Radars and Quantitative Precipitation Estimation||Muhammed A Sit (The University of Iowa); Bongchul Seo (IIHR—Hydroscience & Engineering, The University of Iowa); Ibrahim Demir (The University of Iowa)|
|(62) Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks||Muhammed A Sit (The University of Iowa); Bekir Demiray (The University of Iowa); Ibrahim Demir (The University of Iowa)|
|(63) Wildfire Smoke Plume Segmentation Using Geostationary Satellite Imagery||Jeffrey L Wen (Stanford University); Marshall Burke (Stanford University)|
|(64) Deep Spatial Temporal Forecasting of Electrical Vehicle Charging Demand||Frederik B Hüttel (Technical University of Denmark (DTU)); Filipe Rodrigues (Technical University of Denmark (DTU)); Inon Peled (Technical University of Denmark (DTU)); Francisco Pereira (DTU)|
|(65) Powering Effective Climate Communication with a Climate Knowledge Base||Kameron B. Rodrigues (Stanford University); Shweta Khushu (SkySpecs Inc); Mukut Mukherjee (ClimateMind); Andrew Banister (Climate Mind); Anthony Hevia (ClimateMind); Sampath Duddu (ClimateMind); Nikita Bhutani (Megagon Labs)|
|(66) Solar PV Maps for Estimation and Forecasting of Distributed Solar Generation||Julian de Hoog (The University of Melbourne); Maneesha Perera (The University of Melbourne); Kasun Bandara (The University of Melbourne); Damith Senanayake (The University of Melbourne); Saman Halgamuge (University of Melbourne)|
|(67) An Iterative Approach to Finding Global Solutions of AC Optimal Power Flow Problems||Ling Zhang (University of Washington); Baosen Zhang (University of Washington)|
|(68) Deep learning applied to sea surface semantic segmentation: Filtering sunglint from aerial imagery||Teodor Vrecica (UCSD); Quentin Paletta (University of Cambridge); Luc Lenain (UCSD)|
|(69) Technical support project and analysis of the dissemination of carbon dioxide and methane from Lake Kivu in nature and its impact on biodiversity in the Great Lakes region since 2012||Bulonze Chibaderhe (FEMAC Asbl)|
|(70) Virtual Screening for Perovskites Discovery||Andrea Karlova (UCL); Cameron C.L. Underwood (University of Surrey); Ravi Silva (University of Surrey)|
|(71) Leveraging Machine Learning for Equitable Transition of Energy Systems||Enea Dodi (UMass Amherst); Anupama A Sitaraman (University of Massachusetts Amherst); Mohammad Hajiesmaili (UMass Amherst); Prashant Shenoy (University of Massachusetts Amherst)|
|(72) Long-term Burned Area Reconstruction through Deep Learning||Seppe Lampe (Vrije Universiteit Brussel); Bertrand Le Saux (European Space Agency (ESA)); Inne Vanderkelen (Vrije Universiteit Brussel); Wim Thiery (Vrije Universiteit Brussel)|
|(73) Preserving the integrity of the Canadian northern ecosystems through insights provided by reinforcement learning-based Arctic fox movement models||Catherine Villeneuve (Université Laval); Frédéric Dulude-De Broin (Université Laval); Pierre Legagneux (Université Laval); Dominique Berteaux (Université du Québec à Rimouski); Audrey Durand (Université Laval)|
|(74) Street2Sat: A Machine Learning Pipeline for Generating Ground-truth Geo-referenced Labeled Datasets from Street-Level Images||Madhava Paliyam (University of Maryland); Catherine L Nakalembe (University of Maryland); Kevin Liu (University of Maryland); Richard Nyiawung (University of Guelph); Hannah R Kerner (University of Maryland)|
|(75) From Talk to Action with Accountability: Monitoring the Public Discussion of Policy Makers with Deep Neural Networks and Topic Modelling||Vili Hätönen (Emblica); Fiona Melzer (University of Edinburgh)|
|(76) NeuralNERE: Neural Named Entity Relationship Extraction for End-to-End Climate Change Knowledge Graph Construction||Prakamya Mishra (Independent Researcher); Rohan Mittal (Independent Researcher)|
|(77) A multi-task learning approach to enhance sustainable biomolecule production in engineered microorganisms||Erin Wilson (University of Washington); Mary Lidstrom (University of Washington); David Beck (University of Washington)|
|(78) MethaNet - an AI-driven approach to quantifying methane point-source emission from high-resolution 2-D plume imagery||Siraput Jongaramrungruang (Caltech)|
|(79) Tackling the Overestimation of Forest Carbon with Deep Learning and Aerial Imagery||Gyri Reiersen (TUM); David Dao (ETH Zurich); Björn Lütjens (MIT); Konstantin Klemmer (University of Warwick); Xiaoxiang Zhu (Technical University of Munich,Germany); Ce Zhang (ETH)|
|(80) Learning Why: Data-Driven Causal Evaluations of Climate Models||Jeffrey J Nichol (University of New Mexico); Matthew Peterson (Sandia National Laboratories); George M Fricke (UNM); Kara Peterson (Sandia National Laboratories)|
|(81) Enhancing Laboratory-scale Flow Imaging of Fractured Geological Media with Deep Learning Super Resolution||Manju Pharkavi Murugesu (Stanford University); Timothy Anderson (Stanford University); Niccolo Dal Santo (MathWorks, Inc.); Vignesh Krishnan (The MathWorks Ltd); Anthony Kovscek (Stanford University)|
|(82) Forecasting emissions through Kaya identity using Neural ODEs||Pierre Browne (Imperial College London)|
|(83) On the Role of Spatial Clustering Algorithms in Building Species Distribution Models from Community Science Data Best Paper: Proposals||Mark Roth (Oregon State University); Tyler Hallman (Swiss Ornithological Institute); W. Douglas Robinson (Oregon State University); Rebecca Hutchinson (Oregon State University)|
|(84) Machine Learning for Climate Change: Guiding Discovery of Sorbent Materials for Direct Air Capture of CO2||Diana L Ortiz-Montalvo (NIST); Aaron Gilad Kusne (NIST); Austin McDannald (NIST); Daniel Siderius (NIST); Kamal Choudhary (NIST); Taner Yildirim (NIST)|
|(85) Reducing greenhouse gas emissions by optimizing room temperature set-points||Yuan Cai (MIT); Subhro Das (MIT-IBM Watson AI Lab, IBM Research); Leslie Norford (Massachusetts Institute of Technology); Jeremy Gregory (Massachusetts Institute of Technology); Julia Wang (Massachusetts Institute of Technology); Kevin J Kircher (MIT); Jasmina Burek (Massachusetts Institute of Technology)|
|(86) Deep learning network to project future Arctic ocean waves||Merce Casas Prat (Environment and Climate Change Canada); Lluis Castrejon (Mila, Université de Montréal, Facebook AI Research); Shady Moahmmed (University of Ottawa)|
|(87) Deep Learning for Spatiotemporal Anomaly Forecasting: A Case Study of Marine Heatwaves||Ding Ning (University of Canterbury); Varvara Vetrova (University of Canterbury); Karin Bryan (University of Waikato); Sébastien Delaux (Meteorological Service of New Zealand)|
|(88) Leveraging Domain Adaptation for Low-Resource Geospatial Machine Learning||John M Lynch (NC State University); Sam Wookey (Masterful AI)|
|(89) APPLYING TRANSFORMER TO IMPUTATION OF MULTI-VARIATE ENERGY TIME SERIES DATA||Hasan Ümitcan Yilmaz (Karlsruhe Institute of Technology); Max Kleinebrahm (Karlsruhe Institut für Technologie); Christopher Bülte (Karlsruhe Institute of Technology); Juan Gómez-Romero (Universidad de Granada)|
Hari Prasanna Das (UC Berkeley)
Katarzyna (Kasia) Tokarska (ETH Zurich)
Maria João Sousa (IST, ULisboa)
Meareg Hailemariam (DAUST)
David Rolnick (Mila, McGill)
Xiaoxiang Zhu (TU Munich)
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:
- Buildings and cities
- Behavioral and social science
- Carbon capture and sequestration
- Climate modeling
- Climate finance and economics
- Climate justice
- Climate policy
- Disaster prediction, management, and relief
- Earth science and monitoring
- Ecosystems and natural systems
- Forestry and other land use
- Heavy industry and manufacturing
- Power and energy systems
- Societal adaptation
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 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. We encourage, but do not require, use of the ICML style template.
Please see the Tips for Submissions and FAQ, and contact email@example.com with questions.
There are two tracks for submissions. Submissions are limited to 4 pages for the Papers track, and 3 pages for the Proposals track, in PDF format (see examples from 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.
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.
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.
Tips for Submissions
- For examples of typical formatting and content, see submissions from our previous workshops at NeurIPS 2020, ICLR 2020, NeurIPS 2019, and ICML 2019).
- Be explicit: Describe how your proposed approach addresses climate change, demonstrating an understanding of the application area.
- Frame your work: The specific problem and/or data proposed should be contextualized in terms of prior work.
- Address the impact: Describe the practical implications of your method in addressing the problem you identify, as well as any relevant societal impacts or potential side-effects. We recommend reading our further guidelines on this aspect here.
- Explain the ML: Readers may not be familiar with the exact techniques you are using or may desire further detail.
- Justify the ML: Describe why the ML method involved is needed, and why it is a good match for the problem.
- Avoid jargon: Jargon is sometimes unavoidable but should be minimized. Ideal submissions will be accessible both to an ML audience and to experts in other relevant fields, without the need for field-specific knowledge. Feel free to direct readers to accessible overviews or review articles for background, where it is impossible to include context directly.
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.
- Illustrate the link: Many types of work, from highly theoretical to deeply applied, can have clear pathways to climate impact. Some links may be direct, such as improving solar forecasting to increase utilization within existing electric grids. Others may take several steps to explain, such as improving computer vision techniques for classifying clouds, which could help climate scientists seeking to understand fundamental climate dynamics.
- Consider your target audience: Try to convey with relative specificity why and to whom solving the problem at hand will be useful. If studying extreme weather prediction, consider how you would communicate your key findings to a government disaster response agency. If analyzing a supply chain optimization pilot program, what are the main takeaways for industries who might adopt this technology? To ensure your work will be impactful, where possible we recommend co-developing projects with relevant stakeholders or reaching out to them early in the process for feedback. We encourage you to use this opportunity to do so!
- Outline key metrics: Quantitative or qualitative assessments of how well your results (or for proposals, anticipated results) compare to existing methods are encouraged. Try to give a sense of the importance of your problem and your findings. We encourage you to convey why the particular metrics you choose are relevant from a climate change perspective. For instance, if you are evaluating your machine learning model on the basis of accuracy, how does improved accuracy on a machine learning model translate to climate impact, and why is accuracy the best metric to use in this context?
- Be clear and concise: The discussion of impact does not need to be lengthy, just clear.
- Convey the big picture: Ultimately, the goal of Climate Change AI is to “empower work that meaningfully addresses the climate crisis.” Try to make sure that from the beginning, you contextualize your method and its impacts in terms of this objective.
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.
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:
- In-depth discussion of relevant related work in the area of the Paper or Proposal, to ensure submissions are well-framed and contextualized in terms of prior work.
- Iterating on the core idea of a Proposal to ensure that the climate change application is well-posed and the ML techniques used are well-suited.
- Giving feedback on the writing or presentation of a Paper or Proposal to bring it to the right level of maturity for submission.
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 May 31.
We suggest that after the mentor-mentee matching is made, a first (physical or digital) meeting should take place within the first week (May 1-8) 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 May 31.
Mentors and mentees must abide by the following Code of Conduct: https://www.climatechange.ai/code_of_conduct.
Applications are due by April 28.
- Application to be a mentee: https://cmt3.research.microsoft.com/CCAIMICML2021
- Application to be a mentor: https://forms.gle/efYnYF7zKrHqLuJe7
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 firstname.lastname@example.org 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.
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: Do I need to use LaTeX or the ICML style files?
A: No, although we encourage it.
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 email@example.com with any questions.
Q: Can I submit work to this workshop if I am also submitting to another ICML 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.