NeurIPS 2020 Workshop
Tackling Climate Change with Machine Learning
This workshop is focused on impactful uses of machine learning in reducing and responding to climate change, and is intended to be a venue for discourse between experts in machine learning and other fields. Recordings are available at the links below:
- Main workshop (Dec 11)
- Side event: Monitoring the Climate Crisis with AI, Satellites and Drones (Dec 14)
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 will highlight work that demonstrates that, while no silver bullet, ML can be an invaluable tool both in reducing greenhouse gas emissions and in helping society adapt to the effects of climate change. Climate change is a complex problem, for which action takes many forms - from 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 Conference on Neural Information Processing Systems (NeurIPS), one of the premier conferences on machine learning, which draws a wide audience of researchers and practitioners in academia, industry, and related fields.
Schedule and Recordings
The main workshop took place digitally on December 11, and the side event took place on December 14. The schedule is available below, with links to recordings, papers, and slides.
Main Workshop Full Recording
The main workshop day (Dec. 11) featured 94 posters and spotlight pesentations, along with 12 invited speakers and panelists highlighting pathways to impact in the public and private sectors, and poster sessions via Gather.town.
|Welcome and opening remarks|
Rose Mwebaza: The Role of CTCN in Climate Technology Innovation (Invited talk)
Details: (click to expand)
Summary: Dr. Rose Mwebaza provides an overview of the activities within the Climate Technology Centre & Network (CTCN) and how multiple stakeholders can leverage the UNFCCC framework to bridge research and application.
Speaker bio: Dr. Rose Mwebaza is the Director of the Climate Technology Centre & Network (CTCN), the implementation arm of the UN Framework Convention on Climate Change Technology Mechanism. She served previously as Chief Natural Resources Officer at the African Development Bank. Dr. Mwebaza holds a PhD in Environment and Natural Resource Governance from Macquarie University, Sydney, Australia; a Master’s Degree in International Comparative Law from the University of Florida, U.S.A; and a Bachelor of Law Degree (LL.B, Hons.) from Makerere University, Kampala, Uganda. She is a former Carl Duisberg Research fellow at the World Conservation Union (IUCN) and a founding member of the Network for African Women Environmentalists. She has over 20 years of experience on a wide range of climate change, environment and sustainable development issues.
|(7) Michael Steininger, "Deep Learning for Climate Model Output Statistics"|
|(46) Kevin Mayer, "An Enriched Automated PV Registry: Combining Image Recognition and 3D Building Data"|
|(73) Thomas Chen, "Interpretability in Convolutional Neural Networks for Building Damage Classification in Satellite Imagery"|
|(20) Jiayang Wang, "A Machine Learning Approach to Methane Emissions Mitigation in the Oil and Gas Industry"|
|(38) Catherine Tong, "RainBench: Enabling Data-Driven Precipitation Forecasting on a Global Scale"|
Details: (click to expand)
Posters presented in this slot are listed below.
|(62) Gerson Vizcarra Aguilar, "The Peruvian Amazon Forestry Dataset: A Leaf Image Classification Corpus"|
|(58) Aqsa Naeem, "Data-driven modeling of cooling demand in a commercial building"|
|(80) Irwin McNeely, "Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning"|
|(45) Tristan C Ballard, "FireSRnet: Geoscience-driven super-resolution of future fire risk from climate change"|
|(63) Marta Skreta, "Spatiotemporal Features Improve Fine-Grained Butterfly Image Classification"|
Panel: Climate Change and ML - The Role of Public Policy
Details: (click to expand)Join us for a panel discussion on pathways to impact via public policy for work in climate change and machine learning.
Panelists: Dr. Moustapha Cissé (Google AI), Pete Clutton-Brock (Radiance International),Dr. Angel Hsu (Yale, UNC), Dr. Dava Newman (MIT), James Rattling Leaf, Sr. (GEO).
Details: (click to expand)
Posters presented in this slot are listed below.
Zico Kolter: Actually end-to-end: Expanding the scope of ML via differentiable optimization and beyond (Invited talk)
Details: (click to expand)
Summary: Dr. Zico Kolter provides a roadmap towards truly end-to-end learning, with a focus on applications in the electricity sector. He discusses techniques to integrate decision analysis into deep neural networks.
Speaker bio: Dr. Zico Kolter is an Associate Professor in the Computer Science Department at Carnegie Mellon University, and also serves as chief scientist of AI research for the Bosch Center for Artificial Intelligence. His work spans the intersection of machine learning and optimization, with a large focus on developing more robust and rigorous methods in deep learning. In addition, he has worked in a number of application areas, highlighted by work on sustainability and smart energy systems. He is a recipient of the DARPA Young Faculty Award, a Sloan Fellowship, and best paper awards at NeurIPS, ICML (honorable mention), IJCAI, KDD, and PESGM.
Panel: Climate Change and ML in the Private Sector
Details: (click to expand)Join us for a panel discussion on pathways to impact via the private sector for work in climate change and machine learning.
Panelists: Dr. Lea Boche (EPRI), Dr. Tom Boussie (Activate), Andrew Cottam (Restor Foundation), Dr. Aisha Walcott-Bryant (IBM Research)
|(57) Benjamin Akera, "Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya"|
|(82) Lorenzo Tomaselli, "Wildfire Smoke and Air Quality: How Machine Learning Can Guide Forest Management"|
|(51) Hao Sheng, "OGNet: Towards a Global Oil and Gas Infrastructure Database using Deep Learning on Remotely Sensed Imagery"|
|(40) Somya Sharma, "Climate Change Driven Crop Yield Failures"|
|(11) Heather Couture, "Towards Tracking the Emissions of Every Power Plant on the Planet"|
Details: (click to expand)
Posters presented in this slot are listed below.
Jennifer Chayes: ML & Data Science for a Sustainable Future (Invited talk)
Details: (click to expand)
Summary: Dr. Jennifer Chayes highlights ongoing work at UC Berkeley at the intersection of climate change and AI, ML, and data science, from ecology to materials science to economics and beyond.
Speaker bio: Dr. Jennifer Chayes is Associate Provost of the Division of Computing, Data Science, and Society, and Dean of the School of Information. She is Professor of EECS, Mathematics, Statistics, and the School of Information. Before joining Berkeley, she was at Microsoft for over 20 years, where she was Technical Fellow, and founder and managing director of three interdisciplinary labs: Microsoft Research New England, New York City, and Montreal. Dr. Chayes’ research areas include phase transitions in computer science, and structural and dynamical properties of networks including modeling and graph algorithms. She is one of the inventors of the field of graphons, which are widely used for the machine learning of large-scale networks. Her recent work focuses on machine learning, including both theory and applications in cancer immunotherapy, ethical decision making, and climate change. Dr. Chayes has received numerous awards for both leadership and scientific contributions, including the Anita Borg Institute Women of Vision Leadership Award, the John von Neumann Award of the Society for Industrial and Applied Mathematics, and an honorary doctorate from Leiden University. She is a member of the American Academy of Arts and Sciences and the National Academy of Sciences.
Vinod Khosla: AI: Can it be a climate changer? (Invited talk)
Details: (click to expand)
Summary: Vinod Khosla urges the machine learning community to focus on those hard, unsolved, and high-risk technological problems that could make a serious dent in climate change mitigation. He identifies twelve such problems for which entrepreneurial "instigators" will be needed, and explores the role of AI in addressing some of the associated challenges.
Speaker bio: Vinod Khosla is an entrepreneur and venture capitalist. He is the founder of Khosla Ventures, a VC firm focused on energy and technology companies with a particular interest in big problems amenable to technology solutions - such as climate change. Khosla Ventures has invested in technologies across the climate and energy space, ranging from battery technologies to plant-based meats and bioplastics. Prior to investing in and mentoring entrepreneurs, Khosla founded several technology companies, contributing to the evolution of computing technologies with Sun Microsystems. He is particularly passionate about scaling energy sources, social entrepreneurship, and promoting a pragmatic approach to the environment. Khosla holds a bachelor’s degree in electrical engineering from the Indian Institute of Technology (IIT), a master’s degree in biomedical engineering from Carnegie Mellon University and an MBA from Stanford University.
|Closing remarks and awards|
Side Event Full Recording
Our side event on “Monitoring the Climate Crisis with AI, Satellites and Drones” (Dec. 14) featured talks and socials with prominent speakers in industry and academia to discuss how artificial intelligence and remote sensing can be used to monitor global carbon impact - and allow for new levels of trust and accountability between governments, companies, and projects internationally.
|Welcome and opening remarks|
Susan Graham: Dendra Systems: Building the most powerful tools for ecosystem restoration today (Invited talk)
Details: (click to expand)
Summary: Dr. Susan Graham introduces Dendra Systems and how technology at Dendra is deployed to scale reforestation for negative carbon capture.
Speaker bio: Dr Susan Graham is the CEO and co-founder of Dendra Systems, leading a team building the most powerful tools for ecosystem restoration today. Dendra Systems restores the world’s native forests through a combination of data ecology, analytics and aerial seeding. Dendra enables site coordinators to better manage and maintain their land across a range of industries, whether it’s through identifying weeds, erosion, fauna populations and much more. Our high-resolution imagery offers a never-before-seen picture of a land holding, right down to a single blade of grass, and our easy-to-use analytics engine helps diagnose problems and create targeted action plans for best-in-class environmental management. We work with forward-thinking partners who want to make confident decisions for their lands, and believe strongly in enabling the best conditions for ongoing stewardship of our natural world. Dr Graham graduated from the University of Oxford with a PhD in Biomedical Engineering in 2015. For her work as an entrepreneur Dr Graham was named on the Forbes 30 Under 30 list for industry in Europe in 2017.
Panel: AI for Carbon Monitoring - Opportunities and Challenges
Details: (click to expand)
Join us for a panel discussion on the opportunities and challenges of artificial intelligence and remote sensing for monitoring global climate impact.
Panelists: Dr. Susan Graham (Dendra Systems), Prof. Xiaoxiang Zhu (Technical University of Munich), Matthew Grey (ClimateTRACE).
Xiaoxiang Zhu: Earth Observation Data Science Meets Climate Science (Invited talk)
Details: (click to expand)
Summary: Dr. Xiaoxiang Zhu discusses how AI can empower earth observations and provides an overview of large-scale EO projects and open data sources.
Speaker bio: Dr. Xiaoxiang Zhu is the Professor for Signal Processing in Earth Observation at the Technical University of Munich, heads the Department "Earth Observation Data Science" at the German Aerospace Center and since May, she is also the Director of the Munich Future AI Lab "AI4EO". The research of Professor Zhu focuses on signal processing and data science in earth observation. Geo-information derived from Earth observation satellite data is indispensable for many scientific, governmental and planning tasks. Furthermore, Earth observation has arrived in the Big Data era with ESA's Sentinel satellites and NewSpace companies. Professor Zhu develops innovative machine learning methods and big data analytics solutions to extract highly accurate large scale geo-information from big EO data. These show, for example, not only three-dimensional structures of buildings, settlement types, population density, but also their evolutions over time. Her team aims at tackling societal grand challenges, e.g. UN’s SDGs, thus, works on solutions that can scale up for global applications, with a particular focus on the developing world.
Matthew Gray: ECAA and Climate TRACE (Invited talk)
Details: (click to expand)
Summary: Matthew Gray provides an overview of Climate TRACE, a global coalition which uses artificial intelligence, satellite image processing, machine learning, and other remote sensing technologies to monitor worldwide greenhouse gas (GHG) emissions.
Speaker bio: Matthew Gray is director and co-founder at Energy and Clean Air Analytics (ECAA). ECAA is a founding member of Climate TRACE, where we are responsible for tracking emissions from power generation and heavy industry sectors, including iron and steel, cement, chemicals and petrochemicals. The Climate TRACE coalition is building a tool that will use artificial intelligence, satellite image processing, machine learning, and other remote sensing technologies to monitor worldwide greenhouse gas (GHG) emissions. The coalition aims to track human-caused emissions to specific sources in real time—independently and publicly. Matt has over a decade of energy financial experience. Matt started his financial career at Credit Suisse and more recently worked at Jefferies, an American investment bank, where he was the head of European carbon and power research. Matt was also a consultant to the International Energy Agency and a managing director and co-head of the power & utilities team at Carbon Tracker which he started in 2016.
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) Electric Vehicle Range Improvement by Utilizing Deep Learning to Optimize Occupant Thermal Comfort||Alok Warey (General Motors Global Research and Development); Shailendra Kaushik (General Motors Global Research and Development); Bahram Khalighi (General Motors Global Research and Development); Michael Cruse (Siemens Digital Industries Software); Ganesh Venkatesan (Siemens Digital Industries Software)|
|(2) Is Africa leapfrogging to renewables or heading for carbon lock-in? A machine-learning-based approach to predicting success of power-generation projects||Galina Alova (University of Oxford); Philipp Trotter (University of Oxford); Alex Money (University of Oxford)|
|(3) pymgrid: An Open-Source Python Microgrid Simulator for Applied Artificial Intelligence Research||Gonzague Henri (Total); Tanguy Levent (Ecole Polytechnique); Avishai Halev (Total, UC Davis); Reda ALAMI (Total R&D); Philippe Cordier (Total S.A.)|
|(4) Towards Optimal District Heating Temperature Control in China with Deep Reinforcement Learning||Adrien Le Coz (EDF); Tahar Nabil (EDF); Francois Courtot (EDF)|
|(5) Deep Reinforcement Learning in Electricity Generation Investment for the Minimization of Long-Term Carbon Emissions and Electricity Costs||Alexander J. M. Kell (Newcastle University); Pablo Salas (University of Cambridge); Jean-Francois Mercure (University of Exeter); Matthew Forshaw (Newcastle University); A. Stephen McGough (Newcastle University)|
|(6) Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models||Eric Zelikman (Stanford University); Sharon Zhou (Stanford University); Jeremy A Irvin (Stanford); Cooper Raterink (Stanford University); Hao Sheng (Stanford University); Avati Anand (Stanford University); Jack Kelly (Open Climate Fix); Ram Rajagopal (Stanford University); Andrew Ng (Stanford University); David J Gagne (National Center for Atmospheric Research)|
|(7) Deep Learning for Climate Model Output Statistics Best ML Innovation||Michael Steininger (University of Würzburg); Daniel Abel (University of Würzburg); Katrin Ziegler (University of Würzburg); Anna Krause (Universität Würzburg, Department of Computer Science, CHair X Data Science); Heiko Paeth (University of Würzburg); Andreas Hotho (Universitat Wurzburg)|
|Quentin Paletta (University of Cambridge); Joan Lasenby (University of Cambridge)|
|(9) Characterization of Industrial Smoke Plumes from Remote Sensing Data||Michael Mommert (University of St. Gallen); Mario Sigel (Sociovestix Labs Ltd.); Marcel Neuhausler (ISS Technology Innovation Lab); Linus M. Scheibenreif (University of St. Gallen); Damian Borth (University of St. Gallen)|
|(10) Learning the distribution of extreme precipitation from atmospheric general circulation model variables||Philipp Hess (Free University Berlin); Niklas Boers (Free University Berlin)|
|(11) Towards Tracking the Emissions of Every Power Plant on the Planet Best Pathway to Impact||Heather D Couture (Pixel Scientia Labs); Joseph O'Connor (Carbon Tracker); Grace Mitchell (WattTime); Isabella Söldner-Rembold (Carbon Tracker); Durand D'souza (Carbon Tracker); Krishna Karra (WattTime); Keto Zhang (WattTime); Ali Rouzbeh Kargar (WattTime); Thomas Kassel (WattTime); Brian Goldman (Google); Daniel Tyrrell (Google); Wanda Czerwinski (Google); Alok Talekar (Google); Colin McCormick (Georgetown University)|
|(12) Spatio-Temporal Learning for Feature Extraction inTime-Series Images||Gael Kamdem De Teyou (Huawei)|
|(13) Meta-modeling strategy for data-driven forecasting||Dominic J Skinner (MIT); Romit Maulik (Argonne National Laboratory)|
|(14) Short-term prediction of photovoltaic power generation using Gaussian process regression||Yahya Hasan Al Lawati (Queen Mary University of London); Jack Kelly (Open Climate Fix); Dan Stowell (Queen Mary University of London)|
|(15) Formatting the Landscape: Spatial conditional GAN for varying population in satellite imagery||Tomas Langer (Intuition Machines); Natalia Fedorova (Max Planck Institute for Evolutionary Anthropology); Ron Hagensieker (Osir.io)|
|(16) Storing Energy with Organic Molecules: Towards a Metric for Improving Molecular Performance for Redox Flow Batteries||Luis M Mejia Mendoza (University of Toronto); Alan Aspuru-Guzik (Harvard University); Martha Flores Leonar (University of Toronto)|
|(17) Predicting Landsat Reflectance with Deep Generative Fusion||Shahine Bouabid (University of Oxford); Jevgenij Gamper (Cervest Ltd.)|
|(18) Quantitative Assessment of Drought Impacts Using XGBoost based on the Drought Impact Reporter||Beichen Zhang (University of Nebraska-Lincoln); Fatima K Abu Salem (American University of Beirut); Michael Hayes (University of Nebraska-Lincoln); Tsegaye Tadesse (University of Nebraska-Lincoln)|
|(19) Estimating Forest Ground Vegetation Cover From Nadir Photographs Using Deep Convolutional Neural Networks||Pranoy Panda (Indian Institute of Technology, Hyderabad); Martin Barczyk (University of Alberta); Jen Beverly (University of Alberta)|
|(20) A Machine Learning Approach to Methane Emissions Mitigation in the Oil and Gas Industry Overall Best Paper||Jiayang Wang (Harrisburg University); Selvaprabu Nadarajah (University of Illinois at Chicago); Jingfan Wang (Stanford University); Arvind Ravikumar (Harrisburg University)|
|(21) Monitoring the Impact of Wildfires on Tree Species with Deep Learning||Wang Zhou (IBM Research); Levente Klein (IBM Research)|
|(22) ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery||Jeremy A Irvin (Stanford); Hao Sheng (Stanford University); Neel Ramachandran (Stanford University); Sonja Johnson-Yu (Stanford University); Sharon Zhou (Stanford University); Kyle Story (Descartes Labs); Rose Rustowicz (Descartes Labs); Cooper Elsworth (Descartes Labs); Kemen Austin (RTI International); Andrew Ng (Stanford University)|
|(23) Mangrove Ecosystem Detection using Mixed-Resolution Imagery with a Hybrid-Convolutional Neural Network||Dillon Hicks (Engineers for Exploration); Ryan Kastner (University of California San Diego); Curt Schurgers (University of California San Diego); Astrid Hsu (University of California San Diego); Octavio Aburto (University of California San Diego)|
|(24) Context-Aware Urban Energy Efficiency Optimization Using Hybrid Physical Models||Benjamin Choi (Stanford University); Alex Nutkiewicz (Stanford University); Rishee Jain (Stanford University)|
|(25) Deep learning architectures for inference of AC-OPF solutions||Thomas Falconer (University College London); Letif Mones (Invenia Labs)|
|(26) Predicting the Solar Potential of Rooftops using Image Segmentation and Structured Data||Daniel de Barros Soares (nam.R); François ANDRIEUX (nam.R); Bastien HELL (nam.R); Julien LENHARDT (nam.R; ENSTA); JORDI BADOSA (Ecole Polytechnique); Sylvain GAVOILLE (nam.R); Stéphane GAIFFAS (nam.R; LPSM (Université de Paris)); Emmanuel BACRY (nam.R; CEREMADE (Université Paris Dauphine, PSL))|
|(27) Revealing the Oil Majors' Adaptive Capacity to the Energy Transition with Deep Multi-Agent Reinforcement Learning||Dylan Radovic (Imperial College London); Lucas Kruitwagen (University of Oxford); Christian Schroeder de Witt (University of Oxford)|
|(28) Quantifying the presence of air pollutants over a road network in high spatio-temporal resolution||Matteo Bohm (Sapienza University of Rome); Mirco Nanni (ISTI-CNR Pisa, Italy); Luca Pappalardo (ISTI)|
|(29) Annual and in-season mapping of cropland at field scale with sparse labels||Gabriel Tseng (NASA Harvest); Hannah R Kerner (University of Maryland); Catherine L Nakalembe (University of Maryland); Inbal Becker-Reshef (University of Maryland)|
|(30) NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations||Paula Harder (Fraunhofer ITWM); William Jones (University of Oxford); Redouane Lguensat (LSCE-IPSL); Shahine Bouabid (University of Oxford); James Fulton (University of Edinburgh); Dánnell Quesada-Chacón (Technische Universität Dresden); Aris Marcolongo (University of Bern); Sofija Stefanovic (University of Oxford); Yuhan Rao (North Carolina Institute for Climate Studies); Peter Manshausen (University of Oxford); Duncan Watson-Parris (University of Oxford)|
|(31) Analyzing Sustainability Reports Using Natural Language Processing||Sasha Luccioni (Mila); Emi Baylor (McGill); Nicolas Duchene (Universite de Montreal)|
|(32) Automated Identification of Oil Field Features using CNNs||SONU DILEEP (Colorado State University); Daniel Zimmerle (Colorado State University); Ross Beveridge (CSU); Timothy Vaughn (Colorado State University)|
|(33) Using attention to model long-term dependencies in occupancy behavior||Max Kleinebrahm (Karlsruhe Institut für Technologie); Jacopo Torriti (University Reading); Russell McKenna (University of Aberdeen); Armin Ardone (Karlsruhe Institut für Technologie); Wolf Fichtner (Karlsruhe Institute of Technology)|
|(34) Street to Cloud: Improving Flood Maps With Crowdsourcing and Semantic Segmentation||Veda Sunkara (Cloud to Street); Matthew Purri (Rutgers University); Bertrand Le Saux (European Space Agency (ESA)); Jennifer Adams (European Space Agency (ESA))|
|(35) Accurate river level predictions using a Wavenet-like model||Shannon Doyle (UvA); Anastasia Borovykh (Imperial College London)|
|(36) Movement Tracks for the Automatic Detection of Fish Behavior in Videos||Declan GD McIntosh (University Of Victoria); Tunai Porto Marques (University of Victoria); Alexandra Branzan Albu (University of Victoria); Rodney Rountree (University of Victoria); Fabio De Leo Cabrera (Ocean Networks Canada)|
|(37) Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery||Issam Hadj Laradji (Element AI); Pau Rodriguez (Element AI); Alfredo Kalaitzis (University of Oxford); David Vazquez (Element AI); Ross Young (Element AI); Ed Davey (Global Witness); Alexandre Lacoste (Element AI)|
|(38) RainBench: Enabling Data-Driven Precipitation Forecasting on a Global Scale||Catherine Tong (University of Oxford); Christian A Schroeder de Witt (University of Oxford); Valentina Zantedeschi (GE Global Research); Daniele De Martini (University of Oxford); Alfredo Kalaitzis (University of Oxford); Matthew Chantry (University of Oxford); Duncan Watson-Parris (University of Oxford); Piotr Bilinski (University of Warsaw / University of Oxford)|
|(39) Machine learning for advanced solar cell production: adversarial denoising, sub-pixel alignment and the digital twin||Matthias Demant (Fraunhofer ISE); Leslie Kurumundayil (Fraunhofer ISE); Philipp Kunze (Fraunhofer ISE); Aditya Kovvali (Fraunhofer ISE); Alexandra Woernhoer (Fraunhofer ISE); Stefan Rein (Fraunhofer ISE)|
|(40) Climate Change Driven Crop Yield Failures||Somya Sharma (U. Minnesota); Deepak Ray (University of Minnesota); Snigdhansu Chatterjee (University of Minnesota)|
|(41) Physics-constrained Deep Recurrent Neural Models of Building Thermal Dynamics||Jan Drgona (Pacific Northwest National Laboratory); Aaron R Tuor (Pacific Northwest National Laboratory); Vikas Chandan (PNNL); Draguna Vrabie (PNNL)|
|(42) Narratives and Needs: Analyzing Experiences of Cyclone Amphan Using Twitter Discourse||Ancil S Crayton (Booz Allen Hamilton); Joao Fonseca (NOVA Information Management School); Kanav Mehra (Independent Researcher); Jared Ross (Booz Allen Hamilton); Marcelo Sandoval-Castañeda (New York University Abu Dhabi); Michelle Ng (International Water Management Institute); Rachel von Gnechten (International Water Management Institute)|
|(43) FlowDB: A new large scale river flow, flash flood, and precipitation dataset||Isaac Godfried (CoronaWhy)|
|(44) Can Federated Learning Save The Planet ?||Xinchi Qiu (University of Cambridge); Titouan Parcollet (University of Oxford); Daniel J Beutel (Adap GmbH / University of Cambridge); Taner Topal (Adap GmbH); Akhil Mathur (Nokia Bell Labs); Nicholas Lane (University of Cambridge and Samsung AI)|
|(45) FireSRnet: Geoscience-driven super-resolution of future fire risk from climate change||Tristan C Ballard (Sust Global, Stanford University); Gopal Erinjippurath (Sust Global)|
|(46) An Enriched Automated PV Registry: Combining Image Recognition and 3D Building Data||Benjamin Rausch (Stanford); Kevin Mayer (Stanford); Marie-Louise Arlt (Stanford); Gunther Gust (University of Freiburg); Philipp Staudt (KIT); Christof Weinhardt (Karlsruhe Institute of Technology); Dirk Neumann (Universität Freiburg); Ram Rajagopal (Stanford University)|
|(47) Satellite imagery analysis for Land Use, Land Use Change and Forestry: A pilot study in Kigali, Rwanda||Bright Aboh (African Institute for Mathematical Sciences); Alphonse Mutabazi (UN Environment Program)|
|Christian Requena-Mesa (Computer Vision Group, Friedrich Schiller University Jena; DLR Institute of Data Science, Jena; Max Planck Institute for Biogeochemistry, Jena); Vitus Benson (Max-Planck-Institute for Biogeochemistry); Jakob Runge (Institute of Data Science, German Aerospace Center (DLR)); Joachim Denzler (Computer Vision Group, Friedrich Schiller University Jena, Germany); Markus Reichstein (Max Planck Institute for Biogeochemistry, Jena; Michael Stifel Center Jena for Data-Driven and Simulation Science, Jena)|
|(49) DeepWaste: Applying Deep Learning to Waste Classification for a Sustainable Planet||Yash Narayan (The Nueva School)|
|(50) Machine Learning Climate Model Dynamics: Offline versus Online Performance||Noah D Brenowitz (Vulcan Inc.); Brian Henn (Vulcan, Inc.); Spencer Clark (Vulcan, Inc.); Anna Kwa (Vulcan, Inc.); Jeremy McGibbon (Vulcan, Inc.); W. Andre Perkins (Vulcan, Inc.); Oliver Watt-Meyer (Vulcan, Inc.); Christopher S. Bretherton (Vulcan, Inc.)|
|(51) OGNet: Towards a Global Oil and Gas Infrastructure Database using Deep Learning on Remotely Sensed Imagery||Hao Sheng (Stanford University); Jeremy A Irvin (Stanford); Sasankh Munukutla (Stanford University); Shawn Zhang (Stanford University); Christopher Cross (Stanford University); Zutao Yang (Stanford University); Kyle Story (Descartes Labs); Rose Rustowicz (Descartes Labs); Cooper Elsworth (Descartes Labs); Mark Omara (Environmental Defense Fund); Ritesh Gautam (Environmental Defense Fund); Rob Jackson (Stanford University); Andrew Ng (Stanford University)|
|(52) VConstruct: Filling Gaps in Chl-a Data Using a Variational Autoencoder||Matthew Ehrler (University of Victoria); Neil Ernst (University of Victoria)|
|(53) A Comparison of Data-Driven Models for Predicting Stream Water Temperature||Helen Weierbach (Lawrence Berkeley); Aranildo Lima (Aquatic Informatics); Danielle Christianson (Lawrence Berkeley National Lab); Boris Faybishenko (Lawrence Berkeley National Lab); Val Hendrix (Lawrence Berkeley National Lab); Charuleka Varadharajan (Lawrence Berkeley National Lab)|
|(54) Automated Salmonid Counting in Sonar Data||Peter Kulits (Caltech); Angelina Pan (Caltech); Sara M Beery (Caltech); Erik Young (Trout Unlimited); Pietro Perona (California Institute of Technology); Grant Van Horn (Cornell University)|
|(55) Short-term PV output prediction using convolutional neural network: learning from an imbalanced sky images dataset via sampling and data augmentation||Yuhao Nie (Stanford University); Ahmed S Zamzam (The National Renewable Energy Laboratory); Adam Brandt (Stanford University)|
|(56) OfficeLearn: An OpenAI Gym Environment for Building Level Energy Demand Response||Lucas Spangher (U.C. Berkeley); Akash Gokul (University of California at Berkeley); Utkarsha Agwan (U.C. Berkeley); Joseph Palakapilly (UC Berkeley); Manan Khattar (University of California at Berkeley); Akaash Tawade (University of California at Berkeley); Costas J. Spanos (University of California at Berkeley)|
|(57) Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya||Shimaa Baraka (Mila); Benjamin Akera (Makerere University); Bibek Aryal (The University of Texas at El Paso); Tenzing Sherpa (International Centre for Integrated Mountain Development); Finu Shrestha (International Centre for Integrated Mountain Development); Anthony Ortiz (Microsoft); Kris Sankaran (University of Wisconsin-Madison); Juan M Lavista Ferres (Microsoft); Mir A Matin (International Center for Integrated Mountain Development); Yoshua Bengio (Mila)|
|(58) Data-driven modeling of cooling demand in a commercial building||Aqsa Naeem (Stanford University); Sally Benson (Stanford University); Jacques de Chalendar (Stanford University)|
|(59) Investigating two super-resolution methods for downscaling precipitation: ESRGAN and CAR||Campbell Watson (IBM); Chulin Wang (Northwestern University); Tim Lynar (University of New South Wales); Komminist Weldemariam (IBM Research)|
|(60) Emerging Trends of Sustainability Reporting in the ICT Industry: Insights from Discriminative Topic Mining||Lin Shi (Stanford University); Nhi Truong Vu (Stanford University)|
|(61) Loosely Conditioned Emulation of Global Climate Models With Generative Adversarial Networks||Alex Ayala (Western Washington University); Chris Drazic (Western Washington University); Brian Hutchinson (Western Washington University); Ben Kravitz (Indiana University); Claudia Tebaldi (Joint Global Change Research Institute)|
|(62) The Peruvian Amazon Forestry Dataset: A Leaf Image Classification Corpus||Gerson Waldyr Vizcarra Aguilar (San Pablo Catholic University); Danitza Bermejo (Universidad Nacional del Altiplano); Manasses A. Mauricio (Universidad Católica San Pablo); Ricardo Zarate (Instituto de Investigaciones de la Amazonía Peruana); Erwin Dianderas (Instituto de Investigaciones de la Amazonía Peruana)|
|(63) Spatiotemporal Features Improve Fine-Grained Butterfly Image Classification||Marta Skreta (University of Toronto); Sasha Luccioni (Mila); David Rolnick (McGill University, Mila)|
|(64) High-resolution global irrigation prediction with Sentinel-2 30m data||Will Hawkins (UC Berkeley); Weixin Wu (UC Berkeley); Sonal Thakkar (UC Berkeley); Puya Vahabi (UC Berkeley); Alberto Todeschini (UC Berkeley)|
|(65) Do Occupants in a Building exhibit patterns in Energy Consumption? Analyzing Clusters in Energy Social Games||Hari Prasanna Das (UC Berkeley); Ioannis C. Konstantakopoulos (UC Berkeley); Aummul Baneen Manasawala (UC Berkeley); Tanya Veeravalli (UC Berkeley); Huihan Liu (UC Berkeley); Costas J. Spanos (University of California at Berkeley)|
|(66) In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness||Robbie M Jones (Stanford University); Sang Michael Xie (Stanford University); Ananya Kumar (Stanford University); Fereshte Khani (Stanford); Tengyu Ma (Stanford University); Percy Liang (Stanford University)|
|(67) Climate-FEVER: A Dataset for Verification of Real-World Climate Claims||Markus Leippold (University of Zurich); Thomas Diggelmann (ETH Zurich)|
|(68) Understanding global fire regimes using Artificial Intelligence||Cristobal Pais (University of California Berkeley); Jose-Ramon Gonzalez (CTFC); Pelagy Moudio (University of California Berkeley); Jordi Garcia-Gonzalo (CTFC); Marta C. González (Berkeley); Zuo-Jun Shen (University of California, Berkeley)|
|(69) ClimaText: A Dataset for Climate Change Topic Detection||Markus Leippold (University of Zurich); Francesco Saverio Varini (ETH)|
|(70) Towards Data-Driven Physics-Informed Global Precipitation Forecasting from Satellite Imagery||Valentina Zantedeschi (GE Global Research); Daniele De Martini (University of Oxford); Catherine Tong (University of Oxford); Christian A Schroeder de Witt (University of Oxford); Piotr Bilinski (University of Warsaw / University of Oxford); Alfredo Kalaitzis (University of Oxford); Matthew Chantry (University of Oxford); Duncan Watson-Parris (University of Oxford)|
|(71) A Generative Adversarial Gated Recurrent Network for Power Disaggregation & Consumption Awareness||Maria Kaselimi (National Technical University of Athens); Athanasios Voulodimos (University of West Attica); Nikolaos Doulamis (National Technical University of Athens); Anastasios Doulamis (Technical University of Crete); Eftychios Protopapadakis (National Technical University of Athens)|
|(72) Deep Fire Topology: Understanding the role of landscape spatial patterns in wildfire susceptibility||Cristobal Pais (University of California Berkeley); Alejandro Miranda (University of Chile); Jaime Carrasco (University of Chile); Zuo-Jun Shen (University of California, Berkeley)|
|(73) Interpretability in Convolutional Neural Networks for Building Damage Classification in Satellite Imagery||Thomas Y Chen (The Academy for Mathematics, Science, and Engineering)|
|(74) Long-Range Seasonal Forecasting of 2m-Temperature with Machine Learning||Etienne E Vos (IBM); Ashley Gritzman (IBM); Sibusisiwe Makhanya (IBM Research); Thabang Mashinini (IBM); Campbell Watson (IBM)|
|(75) Explaining Complex Energy Systems: A Challenge||Jonas Hülsmann (TU Darmstadt); Florian Steinke (TU Darmstadt)|
|(76) The Human Effect Requires Affect: Addressing Social-Psychological Factors of Climate Change with Machine Learning||Kyle Tilbury (University of Waterloo); Jesse Hoey (University of Waterloo)|
|(77) A Way Toward Low-Carbon Shipping: Improving Port Operations Planning using Machine Learning||Sara El Mekkaoui (EMI Engineering School); Loubna Benabou (UQAR); Abdelaziz Berrado (EMI Engineering School)|
|(78) Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters||Christopher Briggs (Keele University); Zhong Fan (Keele University); Peter Andras (Keele University, School of Computing and Mathematics, Newcastle-under-Lyme, UK)|
|(79) Leveraging Machine learning for Sustainable and Self-sufficient Energy Communities||Anthony Faustine (University College Dublin); Lucas Pereira (ITI, LARSyS, Técnico Lisboa); Loubna Benabou (UQAR); Daniel Ngondya (The University of Dodoma)|
|(80) Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning||Irwin H McNeely (Carnegie Mellon University); Kimberly Wood (Mississippi State University); Niccolo Dalmasso (Carnegie Mellon University); Ann Lee (Carnegie Mellon University)|
|(81) Hyperspectral Remote Sensing of Aquatic Microbes to Support Water Resource Management||Grace E Kim (Booz Allen Hamilton); Evan Poworoznek (NASA GSFC); Susanne Craig (NASA GSFC)|
|(82) Wildfire Smoke and Air Quality: How Machine Learning Can Guide Forest Management||Lorenzo Tomaselli (Carnegie Mellon University); Coty Jen (Carnegie Mellon University); Ann Lee (Carnegie Mellon University)|
|(83) HECT: High-Dimensional Ensemble Consistency Testing for Climate Models||Niccolo Dalmasso (Carnegie Mellon University); Galen Vincent (Carnegie Mellon University); Dorit Hammerling (Colorado School of Mines); Ann Lee (Carnegie Mellon University)|
|(84) Towards DeepSentinel: An extensible corpus of labelled Sentinel-1 and -2 imagery and a proposed general purpose sensor-fusion semantic embedding model||Lucas Kruitwagen (University of Oxford)|
|(85) Monitoring Shorelines via High-Resolution Satellite Imagery and Deep Learning||Venkatesh Ramesh (HyperVerge); Digvijay Singh (HyperVerge)|
|(86) Graph Neural Networks for Improved El Niño Forecasting||Salva Rühling Cachay (Technical University of Darmstadt); Emma Erickson (University of Illinois at Urbana-Champaign); Arthur F C Bucker (University of São Paulo); Ernest J Pokropek (Warsaw University of Techology); Willa Potosnak (Duquesne University); Salomey Osei (African Master's of Machine Intelligence(AMMI-GH)); Björn Lütjens (MIT)|
|(87) Residue Density Segmentation for Monitoring and Optimizing Tillage Practices||Jennifer Hobbs (IntelinAir); Ivan A Dozier (IntelinAir); Naira Hovakimyan (UIUC)|
|(88) Machine Learning Informed Policy for Environmental Justice in Atlanta with Climate Justice Implications||Lelia Hampton (Massachusetts Institute of Technology)|
|(89) A Multi-source, End-to-End Solution for Tracking Climate Change Adaptation in Agriculture||Alejandro Coca-Castro (Kings College London); Aaron Golden (NUI Galway); Louis Reymondin (The Alliance of Bioversity International and the International Center for Tropical Agriculture)|
|(90) Expert-in-the-loop Systems Towards Safety-critical Machine Learning Technology in Wildfire Intelligence||Maria João Sousa (IDMEC, Instituto Superior Técnico, Universidade de Lisboa); Alexandra Moutinho (IDMEC, Instituto Superior Técnico, Universidade de Lisboa); Miguel Almeida (ADAI, University of Coimbra)|
|(91) ACED: Accelerated Computational Electrochemical systems Discovery||Rachel C Kurchin (CMU); Eric Muckley (Citrine Informatics); Lance Kavalsky (CMU); Vinay Hegde (Citrine Informatics); Dhairya Gandhi (Julia Computing); Xiaoyu Sun (CMU); Matthew Johnson (MIT); Alan Edelman (MIT); James Saal (Citrine Informatics); Christopher V Rackauckas (Massachusetts Institute of Technology); Bryce Meredig (Citrine Informatics); Viral Shah (Julia Computing); Venkat Viswanathan (Carnegie Mellon University)|
|(92) Forecasting Marginal Emissions Factors in PJM||Amy H Wang (Western University); Priya L Donti (Carnegie Mellon University)|
|(93) Artificial Intelligence, Machine Learning and Modeling for Understanding the Oceans and Climate Change||Nayat Sánchez Pi (Inria); Luis Martí (Inria); André Abreu (Fountation Tara Océans); Olivier Bernard (Inria); Colomban de Vargas (CNRS); Damien Eveillard (Univ. Nantes); Alejandro Maass (CMM, U. Chile); Pablo Marquet (PUC); Jacques Sainte-Marie (Inria); Julien Salomin (Inria); Marc Schoenauer (INRIA); Michele Sebag (LRI, CNRS, France)|
|(94) Machine Learning towards a Global Parameterisation of Atmospheric New Particle Formation and Growth||Theodoros Christoudias (Cyprus Institute); Mihalis A Nicolaou (Cyprus Institute)|
David Dao* (ETH Zürich)
Evan Sherwin* (Stanford)
Priya Donti (CMU)
Lynn Kaack (ETH Zürich)
Lauren Kuntz (Gaiascope)
Yumna Yusuf (UK Gov)
David Rolnick (McGill)
Catherine Nakalembe (UMD)
Claire Monteleoni (CU Boulder)
Yoshua Bengio (Mila)
* Denotes co-lead organizers
Side Event Co-organizers
Kasia Tokarska (ETH Zürich)
Maria João Sousa (IST, ULisboa)
Isabelle Tingzon (Thinking Machines Data Science)
Abdulrahman Ahmed (Cairo University)
Alberto Chapchap (Visia Investments)
Alexandra Puchko (Western Washington University)
Alexandre Lacoste (Element AI)
Amelia Taylor (University of Malawi, The Polytechnic)
Andrew Ross (Harvard University)
Aneesh Rangnekar (Rochester Institute of Technology)
Anthony Ortiz (Microsoft)
Arijit Patra (University of Oxford)
Armi Tiihonen (Massachusetts Institute of Technology)
Arvind Mohan (Los Alamos National Laboratory)
Ashesh Chattopadhyay (Rice University)
Ashley Pilipiszyn (Stanford University)
Bill Cai (Massachusetts Institute of Technology)
Björn Lütjens (Massachussets Institute of Technology)
Brian Hutchinson (Western Washington University)
Caleb Robinson (Georgia Institute of Technology)
Christian Schroeder de Witt (University of Oxford)
Claire Monteleoni (University of Colorado Boulder)
Clement DUHART (MIT Media Lab)
Conrad Foley (Deep Planet)
Dali Wang (ORNL)
Daniel Kofman (Telecom ParisTech)
Daniel Livescu (Los Alamos National Laboratory )
Dara Farrell (Graduate of University of Washington)
David Rolnick (McGill University, Mila)
Deval Pandya (Shell Global solutions)
di wu (McGill)
Diego Kiedansk (Telecom ParisTech)
Duncan Watson-Parris (University of Oxford)
Fabrizio Falasca (Georgia Institute of Technology)
FELIPE OVIEDO (MIT)
Felix Laumann (Imperial College London)
Femke van Geffen (FU Berlin)
Filip Tolovski (Fraunhofer Heinrich-Hertz-Institut )
Fred Otieno (IBM)
Frederik Gerzer (fortiss)
Frederik Kratzert (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria)
Gavin Portwood (Los Alamos National Lab)
Gege Wen (Stanford University)
Genevieve Flaspohler (MIT)
Hari Prasanna Das (UC Berkeley )
Henning Schwabe (Private)
Hovig Bayandorian ()
Ioannis C. Konstantakopoulos (UC Berkeley)
Isaac Waweru (IBM)
Jan Drgona (Pacific Northwest National Laboratory)
Jefferson Sankara (Lori Systems)
Jeremy Irvin (Stanford)
Jigar Doshi (Wadhwani AI)
Jingfan Wang (Stanford University)
Johan Mathe (Frog Labs)
John Platt (Google)
Jonathan Fürst (NEC Laboratories Europe)
Joris Guerin (ENSAM)
Joyjit Chatterjee (University of Hull)
Julius von Kügelgen (MPI for Intelligent Systems, Tübingen & University of Cambridge)
K. Shankari (UC Berkeley)
Kalai Ramea (PARC)
Kate Duffy (Northeastern University)
Kelly Kochanski (University of Colorado Boulder)
Kevin McCloskey (Google)
Kidane Degefa (Haramaya University)
Konstantin Klemmer (University of Warwick)
Kris Sankaran (University of Wisconsin-Madison)
Lauren Kuntz (Gaiascope)
Lea Boche (EPRI)
Lexie Yang (Oak ridge national laboratoy)
Linda Petrini (Google)
loubna benabbou (EMI Engineering School, Mohammed V University in Rabat.)
Lucas Kruitwagen (University of Oxford)
Lucas Liebenwein (Massachusetts Institute of Technology)
Lucas Spangher (U.C. Berkeley)
Lynn Kaack (ETH Zurich)
Martin Gauch (University of Waterloo)
Mayur Mudigonda (UC Berkeley)
Melrose Roderick (Carnegie Mellon University)
Michael Howland (Stanford University)
Miguel Molina-Solana (Imperial College London)
Mohammad Mahdi Kamani (The Pennsylvania State University)
Neel Guha (Stanford University)
Niccolo Dalmasso (Carnegie Mellon University)
Nikola Milojevic-Dupont (Mercator Research Institute on Global Commons and Climate Change (MCC))
Olivia Mendivil Ramos (Cold Spring Harbor Laboratory)
Pedram Hassanzadeh (Rice University)
Priya Donti (Carnegie Mellon University)
Rajesh Sankaran (Argonne National Lab)
Robin Dunn (Carnegie Mellon University)
Ruben Glatt (LLNL)
Sadegh Farhang (The Pennsylvania State University)
Sam Skillman (Descartes Labs)
Sandip Agarwal (IISER Bhopal)
Sasha Luccioni (Mila)
Saumya Sinha (University of Colorado, Boulder)
Shamindra Shrotriya (Carnegie Mellon University)
Shruti Kulkarni (Indian Institute of Science (IISc))
Simona Santamaria (ETH Zurich)
Sookyung Kim (Lawrence Livermore National Laboratory)
Sophie Giffard-Roisin (University of Colorado Boulder)
Soukayna Mouatadid (University of Toronto)
Stephan Rasp (Technical University of Munich)
Tianle Yuan (NASA)
Valentina Zantedeschi (GE Global Research)
Victor Schmidt (Mila)
Victoria Preston (MIT)
Xin Hou (WeBank)
Ydo Wexler (Amperon)
Yimeng Min (Mila)
Yue Hu (Vanderbilt University)
Yumna Yusuf (City University London)
Zhecheng Wang (Stanford University)
Call for Submissions
Deadline: October 6, Anywhere on Earth
Notification: October 30
Submission website: https://cmt3.research.microsoft.com/CCAINeurIPS2020
We invite submissions of short papers using machine learning to address problems in climate mitigation, adaptation, and science, including but not limited to the following topics:
- Behavioral and social science
- Buildings and cities
- Carbon capture and sequestration
- Climate and earth science
- Climate finance
- Climate justice
- Disaster prediction, management, and relief
- Ecosystems and natural systems
- Forestry and other land use
- Power and energy
- 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 that 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 NeurIPS 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 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 methods 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.
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. No results need to be demonstrated, but 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.
Tips for Submissions
- For examples of typical formatting and content, see submissions from our previous workshops at 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 ramifications 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.
NeurIPS Registration Grants
Although conference fees have been significantly reduced, if cost is still a burden, then please apply for financial assistance here. CCAI will do our best to meet financial assistance needs for the NeurIPS registration fee. The application deadline is November 20th, 2020.
Participants may also apply for registration grants through the main NeurIPS conference. Applications can be submitted here through November 27.
Given the COVID-19 pandemic and the virtual nature of NeurIPS this year, CCAI is offering Data Grants to participants who have limited access to the internet. The application deadline is November 20th, 2020. Please apply here here.
Submission Mentorship Program
Deadline: August 25
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. See also this video explaining the program.
Mentors are expected to guide mentees during the six weeks of the CCAI mentorship program (Aug 28 - Oct 6) 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 Oct 6. In turn, mentors are expected to commit at least 5 hours over the time period of the mentorship program.
We suggest that after the mentor-mentee matching is made, a first (physical or digital) meeting should take place within the first week (Aug 28 - Sep 4) 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 Oct 6.
Applications are due by August 25.
Mentees should apply via the mentorship program CMT site: https://cmt3.research.microsoft.com/CCAIMNeurIPS2020
(After logging in, click the “create new submission” button to start your application.)
In the application, mentees will be asked to answer questions describing their project, their project’s relevance to climate change and machine learning, and the areas on which they are looking for feedback.
- In your abstract (100-150 words), please summarize your project and the kinds of feedback you are looking to receive from a mentor.
- The primary subject area you select in CMT should represent the area on which you are primarily looking to obtain feedback. Secondary subject areas should reflect other areas of relevance to your submission.
(While the mentorship program is primarily targeted at students and early-career researchers, individuals at any level of seniority are welcome and encouraged to apply for mentorship.)
Mentors should apply via the following Google form: https://forms.gle/g2xyFkvh9A5LouoZA
We suggest that mentor applicants have at least 3 years of research or industry experience in climate change and/or machine learning.
Between August 26-28, selected mentors may be asked to provide additional input in order to facilitate the mentor/mentee matching process. As the mentor/mentee matching period is short, we ask that mentors be responsive during this period.
Mentorship Program FAQ
Q: Am I required to participate in the mentorship program if I want to submit to the workshop?
A: No – while encouraged, participation in the mentorship program is strictly optional.
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.
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 students and early-career researchers 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 5 hours to the program over the time period. Should I still apply as a mentor?
A: No. While the 5 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.
View recording here
This workshop aims to attract submissions and participants from a broad range of fields and stakeholder groups across the world to highlight work that uses machine learning to help tackle climate change. Both our main workshop and the supplementary mentorship program aim to facilitate exchange between individuals from many different backgrounds who work at the intersection of climate change and machine learning.
Because this call is so broad, potential submitters likely have many questions. E.g:
- “I am a materials scientist applying machine learning to speed up materials discovery. What aspects of my work should I highlight in my submission?”
- “I am a machine learning expert applying reinforcement learning to supply chain optimization. How do I communicate the climate impact of my work?”
- “I study forestry and have a giant database of images of trees from Canadian forests increasingly strained by rising temperatures. I think machine learning could help answer important scientific questions, but I’m not sure where to start!”
To help answer these and other questions, we held a webinar on Thursday, August 20th, at 9am PDT (12pm EDT, 1pm BRT, 6pm CEST, 9:30pm IST, midnight Beijing time), explaining:
- How to prepare a successful submission for the workshop.
- Our mentorship program (applications due August 25), which will pair applicants from a variety of backgrounds with experienced mentors to help improve their submission.
All poster sessions will take place in our online space on Gather.town. (Registration is required to attend.) All attending the poster session are warmly invited to play Mingle Bingo.
Our poster sessions use the Gather.town platform, an interactive virtual world which attendees can navigate as 8-bit characters. For a full description, see this tutorial, but here are the basics:
- The poster session is set up in a virtual space, much like a physical poster session, with posters placed in designated areas across the space and plenty of room for informal conversation.
- You can use the arrow keys to walk anywhere you like in the space with your customizable avatar.
- You can have a video call with anyone near your avatar. As you approach someone, their video will appear on your screen.
- Each poster is prominently numbered on the map in one of the two poster rooms. These numbers are visible on the minimap (which you can access from the bottom bar) to aid navigation, and correspond to the poster numbers on our website.
- Around each poster is a rectangle which marks a private conversation area, meaning that everyone in that area is part of the same video conversation. There are also private conversation areas around the tables in other rooms.
- At least one author of each poster should be present at at least one poster session. See the schedule above.
- To view a poster, walk up to the corresponding podium (which looks like ) and press the “x” button. This will bring up the poster’s webpage (also above), which includes slides describing the work and in many cases a recorded video or workshop paper.
- There is a “CCAI Corner” in the top room. This is a space where conference organizers, speakers, and members of CCAI are available to talk with attendees in a larger group (we will also be circulating and enjoying the show!).
You can press “g” to walk through other people like a ghost. Click on the minimap in the bottom bar to see the whole space, including poster numbers and explanation of the contents of other rooms. You can see who else is in the session in the left panel (click the “participants” button, which looks like two abstract people). You can click on someone there and select “locate” to trace a path directly to them. There is also a chat functionality that you can access via the left panel (click on the message icon). You can use the chat to direct message other participants, message everyone you can see (“nearby”), or message absolutely everyone in the room (“everyone”).
Part of the fun of coming to conferences is meeting exciting new people!
As an icebreaker, we are introducing Mingle Bingo. To play, download our bingo card, which has a grid of 25 things that might describe different conference attendees (e.g. “Works in agriculture”, “Uses computer vision” , or “From South America”).
When you meet someone who checks a box, fill in their name on the card.
Five boxes in a row (horizontally, vertically, or diagonally) is Bingo.
If you fill in all 25 boxes, you’ve got Super-Bingo.
If you have Bingo or Super-Bingo, you can submit your filled-in card here and we will list you as one of the TCCML bingo winners! (The winners list will display name, institution, and type of bingo only)
You can edit your submission any time, so feel free to submit a card with Bingo and keep going for Super-Bingo.
The bingo card should be fillable in most PDF readers but we will accept cards in any format (e.g. feel free to print it out and send a picture of a handwritten card).
Google account sign-in is required to submit a card using the main form due to the file upload but if you don’t have a Google account, just email your card to us at email@example.com and we will add you to the winner list!
Frequently Asked Questions
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 NeurIPS 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: What do I do if I need an earlier decision for visa reasons?
A: Given the virtual nature of the conference, we do not anticipate this being an issue. That said, feel free to contact us at firstname.lastname@example.org and explain your situation and the date by which you require a decision and we will do our best to be accommodating.
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. (As stated by the NeurIPS workshop guidelines, “Workshops are not a venue for work that has been previously published in other conferences on machine learning or related fields. Work that is presented at the main NeurIPS conference should not appear in a workshop, including as part of an invited talk.”) If your work was previously accepted to a Climate Change AI workshop, this work must 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 NeurIPS 2020 workshop?
A: From our perspective, yes; however, please also check the policies of the other workshop. If you submit to multiple workshops, we cannot guarantee that you will not be expected to present the material at a time that conflicts with the other workshop.
Q: If I submit my work to this workshop, can also I still submit this work to other venues in the future?
A: The workshop does not publish proceedings, and submissions are non-archival. Submission to this workshop does not preclude future publication in other venues from our perspective; however, please also check the policies of the other venue(s) to which you wish to submit.