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.

Time (UTC) Event
Opening Remarks
Solomon Assefa: Addressing Enterprise Decarbonization and Climate Resiliency Goals with Advances in AI, Cloud, and Quantum Computing (Invited Talk)
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Speaker: Solomon Assefa, IBM Research

Title: Addressing Enterprise Decarbonization and Climate Resiliency Goals with Advances in AI, Cloud, and Quantum Computing

Abstract: Climate change mitigation and adaptation requires a global response across industries, and the call for action is being amplified by regulatory, investor, and consumer pressure. Over 20% of the world’s largest companies have set long term net-zero and climate resiliency targets. For example, energy and utility companies are planning to make a low-carbon transition and develop climate resilient infrastructure and integrate renewables. Companies spanning various industries want to manage physical asset risk, managing emission hotspots across their supply chain, and demonstrate regulatory compliance.

Furthermore, the financial sector is looking to identify, quantify, monitor risks in their investment portfolio and drive capital management. Hence, enterprises across various industries will need innovative technologies to help them reduce their emissions while building operational resiliency to the impacts of climate change.

This talk will explore how recent advances in AI, quantum computing, and cloud platforms could be applied towards addressing the aforementioned industry use cases, thereby enabling climate change mitigation and adaptation.

Bio: Dr. Solomon Assefa is a Vice President at IBM Research. He heads research strategy, execution, commercialization, and partnerships for Impact Science, which includes Future of Climate, Future of Work, and Future of Health. Under his leadership, researchers from IBM’s global labs are applying and advancing disruptive technologies that will transform industries and benefit society. These technologies include AI, cloud, quantum computing, and accelerated discovery. He is also directing the worldwide effort focused on developing core technologies and partnerships for climate change mitigation and adaptation. He directs scientists from IBM’s global labs who are innovating in areas such as sustainable hybrid cloud, AI-enabled material discovery for carbon capture, carbon footprint optimization, AI-driven climate risk and impact modelling to enable resiliency.

Dr. Assefa is also responsible for IBM’s research labs in Kenya and South Africa, and heads IBM’s research strategy and partnership across Middle East and Africa. He is responsible for fostering local innovation ecosystems by forming new models for partnership with government, industry, academia, non-profits, and start-up companies. The labs are localizing these technologies and developing scalable (lab to market) solutions for emerging markets in areas such as financial services, telcos, healthcare, supply chain, energy and utilities, agriculture, and public sector.

As a Research Scientist, Dr. Assefa has worked on IBM’s nanophotonics technology with responsibilities spanning research, development, and technology transfer. He has also worked on field switched and spin torque MRAM technologies.

Dr. Assefa has co-authored over 150 publications in peer-reviewed journals and conference proceedings, has over 50 patents, and has appeared as a guest speaker at numerous conferences worldwide. He received a B.S. in Physics (2001), a B.S. in EECS (2001), a M.S. in EECS (2001) and a Ph.D. (2004) all from the Massachusetts Institute of Technology (MIT).

Spotlight Presentations
(11) Wenting Li, "Physics-Informed Graph Neural Networks for Robust Fault Location in Power Grids"
(75) Vili Hätönen, "From Talk to Action with Accountability: Monitoring the Public Discussion of Policy Makers with Deep Neural Networks and Topic Modelling"
(51) Chris Fletcher, "Toward efficient calibration of higher-resolution Earth System Models"
Panel Discussion: Designing Projects and Finding Collaborators in Climate Change and ML
Details: (click to expand) Panelists:
Poster Session 1
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Posters presented in this slot are listed below.

Papers track:

Proposals track:

Shakir Mohamed: Harnessing Machine Learning to Achieve Net Zero (Invited Talk)
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Speaker: Shakir Mohamed, DeepMind

Title: Harnessing Machine Learning to Achieve Net Zero

Abstract: Achieving net zero as rapidly as possible is an existential grand challenge where machine learning researchers can make unique contributions. These contributions are technical in nature, but they are also in areas of policy, responsible innovation, and social change. I'd like to explore a view on these topics using work from the Royal Society report on digital technology for the planet, decolonial AI, and machine learning for environmental science. Our discussion will interweave the role of data in achieving net zero, the growing interest in digital twins, making better predictions of environmental variables, and deeper considerations of inequality and harms that arise from the coupling of climate change and technology. With this I hope to leave space for our later community discussion on methods and priorities for how we as machine learning researchers can make our contributions to achieving net zero emissions.

Bio: Dr Shakir Mohamed works on technical and sociotechnical questions in machine learning research, aspiring to make contributions to machine learning principles, applied problems in healthcare and environment, and ethics and diversity. Shakir is a research scientist and lead at DeepMind in London, an Associate Fellow at the Leverhulme Centre for the Future of Intelligence, and a Honorary Professor of University College London. Shakir is also a founder and trustee of the Deep Learning Indaba, a grassroots organisation aiming to build pan-African capacity and leadership in AI. Shakir was the General Chair for the 2021 International conference on Learning Representations, and a member of the Royal Society's Diversity Committee.

Spotlight Presentations
(28) Wenqi Cui, "Reinforcement Learning for Optimal Frequency Control: A Lyapunov Approach"
(76) Prakamya Mishra, "NeuralNERE: Neural Named Entity Relationship Extraction for End-to-End Climate Change Knowledge Graph Construction"
(42) Soukayna Mouatadid, "An Accurate and Scalable Subseasonal Forecasting Toolkit for the United States"
(44) Daniel Salles Civitarese, "Extreme Precipitation Seasonal Forecast Using a Transformer Neural Network"
(63) Jeff Wen, "Wildfire Smoke Plume Segmentation Using Geostationary Satellite Imagery"
Panel Discussion: Monitoring and Mitigation of Emissions in Line with Paris Agreement Targets
Details: (click to expand) Panelists:
Poster Session 2
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Posters presented in this slot are listed below.

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Kate Marvel: Using Machine Learning to Understand Present and Future Climate Changes: an Invitation (Invited Talk)
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Speaker: Kate Marvel, NASA, Columbia University

Title: Using machine learning to understand present and future climate changes: an invitation

Abstract: In this talk, I'll briefly discuss the standard methods we use to understand the role of external influences like greenhouse gases and aerosols on climate trends and events. Using the ongoing megadrought in the North American Southwest as an example, I'll show how we can disentangle the relative roles of these external "forcings" and natural climate variability. I'll discuss some areas in which machine learning might be able to help scientists learn about climate modeling and observations. I hope to start a conversation about the most appropriate role for these techniques.

Bio: Dr. Marvel uses climate models, observations, paleoclimate reconstructions, and basic theory to study climate change. Her work has identified human influences on present-day cloud cover, rainfall patterns, and drought risk. She is also interested in future climate changes, particularly climate feedback processes and the planet's sensitivity to increased carbon dioxide. Dr. Marvel teaches in Columbia's MA in Climate and Society Program and writes the "Hot Planet" column for Scientific American. Named one of "15 Women Who Will Save the World" by Time Magazine, she has been profiled by the New York Times and Rolling Stone, and her 2017 TED talk has been viewed over one million times. Before becoming a climate scientist, she received a PhD in theoretical particle physics from Cambridge University, where she was a Gates scholar.

Spotlight Presentations
(81) Manju Murugesu, "Enhancing Laboratory-scale Flow Imaging of Fractured Geological Media with Deep Learning Super Resolution"
(83) Mark Roth, "On the Role of Spatial Clustering Algorithms in Building Species Distribution Models from Community Science Data"
(13) Maike Sonnewald, "Revealing the impact of global warming on climate modes using transparent machine learning and a suite of climate models"
(79) Gyri Reiersen, "Tackling the Overestimation of Forest Carbon with Deep Learning and Aerial Imagery"
(53) Akansha Singh, "Designing Bounded min-knapsack Bandits algorithm for Sustainable Demand Response"
Draguna Vrabie: Differentiable Predictive Control (Invited Talk)
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Speaker: Draguna Vrabie, PNNL

Title: Differentiable Predictive Control

Abstract: Current control technology practice in energy industries implements rule-based controls based on engineering insights. Deep learning control technology can cost-effectively displace current control technology and contribute to climate change mitigation and adaptation. First, I introduce our recent work on modeling dynamic systems with deep learning representations that embed domain knowledge. Next, I will introduce differentiable predictive control (DPC), a data-driven approach that uses physics-informed deep learning representations to synthesize predictive control policies. DPC presents an approximate data-driven solution approach to the explicit Model Predictive Control. Unlike imitation learning approaches, DPC provides neural control policies for time-varying references while satisfying state and input constraints. I illustrate the methodology with application examples of energy-efficient buildings. I conclude with a broader set of control-related problems that machine learning can solve.

Bio: Draguna Vrabie is a chief data scientist in the Data Sciences and Machine Intelligence Group at Pacific Northwest National Laboratory. She serves as Team Lead for the Autonomous Learning and Reasoning Team. At the intersection of control system theory and machine learning, her work is on the design of adaptive decision and control systems. Her current focus is on deep learning methodologies and algorithms for high-performance cyber-physical systems. Before joining PNNL in 2015, she was a senior scientist at United Technologies Research Center, East Hartford, Connecticut. Vrabie holds a doctorate in electrical engineering from the University of Texas at Arlington and an ME and BE in automatic control and computer engineering from Gheorghe Asachi Technical University, Iaşi, Romania.

Closing remarks and awards
Poster Session 3
Details: (click to expand)

Posters presented in this slot are listed below.

Papers track:

Proposals track:

Gather.town networking

Accepted Works

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.


Title Authors
(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)


Title Authors
(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)


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DeepMind logo


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:

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 climatechangeai.icml2021@gmail.com with questions.

Submission Tracks

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

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.


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 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.

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.icml2021@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: 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 climatechangeai.icml2021@gmail.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.