NeurIPS 2022 Workshop: Tackling Climate Change with Machine Learning

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About

Many in the ML community wish to take action on climate change, but are unsure of the pathways through which they can have the most impact. This workshop highlights work that demonstrates that, while no silver bullet, ML can be an invaluable tool in reducing greenhouse gas emissions and in helping society adapt to the effects of climate change. Climate change is a complex problem, for which action takes many forms - from theoretical advances to deployment of new technology. Many of these actions represent high-impact opportunities for real-world change, and are simultaneously interesting academic research problems.

This workshop 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. For this iteration of the workshop, the keynote talks and panel discussions were particularly focused on exploring the theme of climate change-informed metrics for AI, focusing both on (a) the domain-specific metrics by which AI systems should be evaluated when used as a tool for climate action, and (b) the climate change-related implications of using AI more broadly.

EDS Partnership

CCAI has partnered with the journal for Environmental Data Science (EDS) to release a special issue with submissions focused on climate-relevant applications of machine learning. The application, which is currently accepting submissions, is open to anyone and closes January 30th. Please refer to the webpage EDS has dedicated to this special issue for details on submitting your application. Reach out to climatechangeai.neurips2022+EDS@gmail.com with any questions!

About the Workshop

The main workshop took place on December 9. The schedule is available below, with links to papers, videos, and slides.

Schedule

Time (UTC)	Event
	Opening Remarks
	Gustau Camps-Valls: Physics-aware Machine learning for Earth observation (Invited talk) Details: (click to expand) Abstract: Most problems in Earth sciences aim to do inferences about the system, where accurate predictions are just a tiny part of the whole problem. Inferences mean understanding variables relations, deriving models that are physically plausible, that are simple parsimonious, and mathematically tractable. Machine learning models alone are excellent approximators, but very often do not respect the most elementary laws of physics, like mass or energy conservation, so consistency and confidence are compromised. I will review the main challenges ahead in the field, and introduce several ways to live in the Physics and machine learning interplay. Physics-aware machine learning models are just a step towards understanding the data-generating process, for which learning causal representations promises great advances. I'll review some recent methodologies to cope with it too. This is a collective long-term AI agenda towards developing and applying algorithms capable of discovering knowledge in the Earth system. Bio: Gustau Camps-Valls (born 1972 in València) is a Physicist and Full Professor in Electrical Engineering in the Universitat de València, Spain, where lectures on machine learning, remote sensing and signal processing. He is the Head of the Image and Signal Processing (ISP) group, an interdisciplinary group of 40 researchers working at the intersection of AI for Earth and Climate sciences. Prof. Camps-Valls has published over 250+ peer-reviewed international journal papers, 350+ international conference papers, 25 book chapters, and 5 international books on remote sensing, image processing and machine learning. He has an h-index of 78 with 29000+ citations in Google Scholar. He was listed as a Highly Cited Researcher in 2011, 2020 and 2021; currently has 13 «Highly Cited Papers» and 1 «Hot Paper», Thomson Reuters ScienceWatch identified his activities as a Fast Moving Front research (2011) and the most-cited paper in the area of Engineering in 2011, received the Google Classic paper award (2019), and Stanford Metrics includes him in the top 2% most cited researchers of 2017-2020. He publishes in both technical and scientific journals, from IEEE and PLOS One to Nature, Nature Communications, Science Advances, and PNAS. He has been Program Committee member of international conferences (IEEE, SPIE, EGU, AGU), and Technical Program Chair at IEEE IGARSS 2018 (2400+ attendees) and general at AISTATS 2022. He served in technical committees of the IEEE GRSS & IEEE SPS, as Associate Editor of 5 top IEEE journals, and in the prestigious IEEE Distinguished Lecturer program of the GRSS (2017-2019) to promote «AI in Earth sciences» globally. He has given 100+ talks, keynote speaker in 10+ conferences, and (co)advised 10+ PhD theses. He coordinated/participated in 60+ research projects, involving industry and academia at national and European levels. He assisted the aerospace industry in Advisory Boards; Fellow Consultant of the ESA PhiLab (2019) and member of the EUMETSAT MTG-IRS Science Team. He is compromised with open source/access in Science, and is habitual panel evaluator for H2020 (ERC, FET), NSF, China and Swiss Science Foundations. He coordinates the 'Machine Learning for Earth and Climate Sciences' research program of ELLIS, the top network of excellence on AI in Europe. He was elevated to IEEE Fellow member (2018) in two Societies (Geosciences and Signal Processing) and to ELLIS Fellow (2019). Prof. Camps-Valls is the only researcher receiving two European Research Council (ERC) grants in two different areas, an ERC Consolidator (2015, Computer Science) and ERC Synergy (2019, Physical Sciences) grants to advance AI for Earth and Climate Sciences. In 2021 he became a Member of the ESSC panel part of the European Science Foundation (ESF), and in 2022 was elevated to Fellow of the European Academy of Sciences (EurASc), Fellow of the Academia Europeae (AE), and Fellow of Asia-Pacific Artificial Intelligence Association (AAIA).
	Break
	Panel Discussion: Domain-specific metrics for evaluation and integration of AI Details: (click to expand) Panelists: Veronica Adetola, Pacific Northwest National Lab David Dao, ETH Zürich Antoine Marot, Réseau de Transport d'Electricité
	Spotlight Presentations
	(87) "Calibration of Large Neural Weather Models"
	(29) "Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation"
	(102) "Deep learning-based bias adjustment of decadal climate predictions"
	(72) "Evaluating Digital Tools for Sustainable Agriculture using Causal Inference"
	(112) "Personalizing Sustainable Agriculture with Causal Machine Learning"
	Break
	Poster Session 1 Details: (click to expand) Posters presented in this slot are listed below. Papers track: (1) Function Approximations for Reinforcement Learning Controller for Wave Energy Converters (2) Image-Based Soil Organic Carbon Estimation from Multispectral Satellite Images with Fourier Neural Operator and Structural Similarity (3) SolarDK: A high-resolution urban solar panel image classification and localization dataset (5) Optimizing toward efficiency for SAR image ship detection (6) AutoML-based Almond Yield Prediction and Projection in California (7) Attention-Based Scattering Network for Satellite Imagery (9) Aboveground carbon biomass estimate with Physics-informed deep network (10) Improving the predictions of ML-corrected climate models with novelty detection (12) Scene-to-Patch Earth Observation: Multiple Instance Learning for Land Cover Classification (13) Deep learning for downscaling tropical cyclone rainfall (14) Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes (15) Identifying latent climate signals using sparse hierarchical Gaussian processes (16) Towards dynamical stability analysis of sustainable power grids using Graph Neural Networks (17) Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation (18) Probabilistic forecasting of regional photovoltaic power production based on satellite-derived cloud motion (19) Robustifying machine-learned algorithms for efficient grid operation (20) Deep Hydrology: Hourly, Gap-Free Flood Maps Through Joint Satellite and Hydrologic Modelling (24) Convolutional Neural Processes for Inpainting Satellite Images: Application to Water Body Segmentation (25) A POMDP Model for Safe Geological Carbon Sequestration (27) Deep Climate Change: A Dataset and Adaptive domain pre-trained Language Models for Climate Change Related Tasks (28) Data-Driven Optimal Solver for Coordinating a Sustainable and Stable Power Grid (29) Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation (30) Explainable Multi-Agent Recommendation System for Energy-Efficient Decision Support in Smart Homes (31) FIRO: A Deep-neural Network for Wildfire Forecast with Interpretable Hidden States (32) Towards a spatially transferable super resolution model for downscaling Antarctic surface melt (33) Forecasting European Ozone Air Pollution With Transformers (34) Stability Constrained Reinforcement Learning for Real-Time Voltage Control (35) Land Use Prediction using Electro-Optical to SAR Few-Shot Transfer Learning (36) Exploring Randomly Wired Neural Networks for Climate Model Emulation (38) SustainGym: A Benchmark Suite of Reinforcement Learning for Sustainability Applications (39) Remote estimation of geologic composition using interferometric synthetic-aperture radar in California’s Central Valley (41) Temperature impacts on hate speech online: evidence from four billion tweets (42) Cross Modal Distillation for Flood Extent Mapping (43) Transformer Neural Networks for Building Load Forecasting (44) Estimating Chicago’s tree cover and canopy height using multi-spectral satellite imagery (45) Reconstruction of Grid Measurements in the Presence of Adversarial Attacks (46) Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble (48) Identifying Compound Climate Drivers of Forest Mortality with β-VAE (49) TCFD-NLP: Assessing alignment of climate disclosures using NLP for the financial markets (50) Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes (51) Hybrid Recurrent Neural Network for Drought Monitoring (52) Deep Learning for Global Wildfire Forecasting (53) Causal Modeling of Soil Processes for Improved Generalization (54) Machine Learning for Activity-Based Road Transportation Emissions Estimation (56) Estimating Corporate Scope 1 Emissions Using Tree-Based Machine Learning Methods (57) Analyzing Micro-Level Rebound Effects of Energy Efficient Technologies (58) Comparing the carbon costs and benefits of low-resource solar nowcasting (59) Climate Policy Tracker: Pipeline for automated analysis of public climate policies (60) Topic correlation networks inferred from open-ended survey responses reveal signatures of ideology behind carbon tax opinion (63) Curriculum Based Reinforcement Learning to Avert Cascading Failures in the Electric Grid (64) Short-range forecasts of global precipitation using deep learning-augmented numerical weather prediction (65) A Multi-Scale Deep Learning Framework for Projecting Weather Extremes (66) A Global Classification Model for Cities using ML (67) EnhancedSD: Downscaling Solar Irradiance from Climate Model Projections (68) Positional Encoder Graph Neural Networks for Geographic Data (70) Towards Global Crop Maps with Transfer Learning (71) Pyrocast: a Machine Learning Pipeline to Forecast Pyrocumulonimbus (PyroCb) Clouds (72) Evaluating Digital Tools for Sustainable Agriculture using Causal Inference (73) Generating physically-consistent high-resolution climate data with hard-constrained neural networks (75) Flood Prediction with Graph Neural Networks (76) Neural Representation of the Stratospheric Ozone Layer (78) Industry-scale CO2 Flow Simulations with Model-Parallel Fourier Neural Operators (79) Adaptive Bias Correction for Improved Subseasonal Forecasting (82) An Interpretable Model of Climate Change Using Correlative Learning (83) Multimodal Wildland Fire Smoke Detection (84) Using uncertainty-aware machine learning models to study aerosol-cloud interactions (85) Accessible Large-Scale Plant Pathology Recognition (86) Dynamic weights enabled Physics-Informed Neural Network for simulating the mobility of Engineered Nano Particles in a contaminated aquifer (88) Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs (89) Generative Modeling of High-resolution Global Precipitation Forecasts (91) Continual VQA for Disaster Response Systems (92) Performance evaluation of deep segmentation models on Landsat-8 imagery Proposals track: (94) Guided Transformer Network for Detecting Methane Emissions in Sentinel-2 Satellite Imagery (95) Identification of medical devices using machine learning on distribution feeder data for informing power outage response (96) Analyzing the global energy discourse with machine learning (98) Towards Low Cost Automated Monitoring of Life Below Water to De-risk Ocean-Based Carbon Dioxide Removal and Clean Power (99) Towards the Automatic Analysis of Ceilometer Backscattering Profiles using Unsupervised Learning (100) Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics (101) An Inversion Algorithm of Ice Thickness and InSAR Data for the State of Friction at the Base of the Greenland Ice Sheet (102) Deep learning-based bias adjustment of decadal climate predictions (103) Surrogate Modeling for Methane Dispersion Simulations Using Fourier Neural Operator (105) Urban Heat Island Detection and Causal Inference Using Convolutional Neural Networks (106) Forecasting Global Drought Severity and Duration Using Deep Learning (107) ForestBench: Equitable Benchmarks for Monitoring, Reporting, and Verification of Nature-Based Solutions with Machine Learning (110) CliMedBERT: A Pre-trained Language Model for Climate and Health-related Text (111) Improving accuracy and convergence of federated learning edge computing methods for generalized DER forecasting applications in power grid Tutorials track: (113) Disaster Risk Monitoring Using Satellite Imagery (114) Machine Learning for Predicting Climate Extremes (115) FourCastNet: A practical introduction to a state-of-the-art deep learning global weather emulator (116) Automating the creation of LULC datasets for semantic segmentation
	Inês M. Azevedo: Mitigating climate and air pollutions from the electricity and transportation sectors in the United States (Invited talk) Details: (click to expand) Abstract: In this talk, I will cover 3 recent pieces. (1) A transition to sustainable, deeply decarbonized, and equitable energy systems is needed in the United States, which will require changes in the way we provide electricity and transportation services. With an increasing interconnected system that encompasses variable energy sources and complex markets, the emissions embedded in electricity generation and consumption are becoming more difficult to estimate. Using flow tracing and consumption-based accounting, we have characterized the health damages from exposure to PM2.5 from electricity imports and find that that 8% of our estimated premature deaths from electricity consumption in the United States are due to electricity imports. There is large geographic heterogeneity, with the most impacts occurring in the Midwest. While the West Coast has much cleaner generation and lower impacts overall, in many West Coast Balancing Areas, more than 50% of the estimated premature mortality associated with electricity consumption is caused by electricity imports, with some groups experiencing larger impacts than others. (2) Vehicle electrification is very likely needed moving forward as we decarbonize the transportation sector We estimate net emissions from vehicle electrification depend on when vehicles are charged, and which types of plants are meeting that electricity demand. We define a new concept, the grid critical emissions factors (CEFs), as the emission intensity of the grid that needs to be achieved when electric vehicles are charging so that electric vehicles achieve lifecycle greenhouse gas emissions parity with some of the most efficient gasoline and hybrid vehicles across the US. We find that the Nissan Leaf and Chevy Bolt battery electric vehicles reduce lifecycle emissions relative to the Toyota Prius and the Honda Accord gasoline hybrids in most of United States. However, in rural counties of the Midwest and the South power grid marginal emissions reductions of up to 208 gCO2/kWh are still needed for these electric vehicles to have lower lifecycle emissions than the gasoline hybrids. With the exception of the Northeast and Florida, the longer-range Tesla Model S battery electric luxury sedan has higher emissions than the hybrids across the U.S., and the emissions intensity of the grid would need to decrease by up to 342 gCO2/kWh in some locations for this vehicle to be at emissions parity with the hybrid vehicles we studied. (3) Electric vehicles will contribute to emissions reductions in the United States, but their charging may challenge electricity grid operations. We present a data-driven, realistic model of charging demand that captures the diverse charging behaviours of future adopters in the US Western Interconnection. We study charging control and infrastructure build-out as critical factors shaping charging load and evaluate grid impact under rapid electric vehicle adoption with a detailed economic dispatch model of 2035 generation. We find that peak net electricity demand increases by up to 25% with forecast adoption and by 50% in a stress test with full electrification. Locally optimized controls and high home charging can strain the grid. Shifting instead to uncontrolled, daytime charging can reduce storage requirements, excess non-fossil fuel generation, ramping and emissions. Our results urge policymakers to reflect generation-level impacts in utility rates and deploy charging infrastructure that promotes a shift from home to daytime charging. Bio: Inês M.L. Azevedo is Associate Professor in the Department of Energy Resources Engineering at Stanford University. She also serves as Senior Fellow for the Woods Institute for the Environment at Stanford University and Fellow for the Precourt Institute for Energy (PIE) at Stanford University. She is the co-director of the Bits&Watts Initiative from PIE at Stanford University. Prof. Azevedo’s research interests focus on how to transition to a sustainable, low carbon, affordable, and equitable energy system. She is interested in sustainability and energy issues where a systems approach is needed, by combining engineering and technology analysis with economic and decision science approaches. Her current interest is to address energy issues with particular focus on distributional effects and equity. She has published 100+ peer-reviewed journal papers. She has participated as an author and committee member in several National Research Council reports from the U.S. National Academy of Sciences. She was one of the Lead Authors for IPCC AR6 report on Climate Mitigation for the Energy chapter, and she is now also participating as Lead Author for the upcoming U.S. National Climate Assessment chapter on climate change mitigation. Prof. Azevedo is also contributing as a chapter author to the upcoming U.S. National Climate Assessment report. Prof. Azevedo has received the World Economic Forum’s “Young Scientists under 40” award in 2014, and the C3E Women in Clean Energy Research Award in 2017.
	Break
	Panel Discussion: Assessing AI’s impacts on greenhouse gas emissions and climate change adaptation Details: (click to expand) Panelists: George Kamiya Sasha Luccioni, Hugging Face Costa Samaras, White House Office of Science and Technology Policy
	Break
	Rose Yu: Physics-Guided Deep Learning for Climate Science (Invited talk) Details: (click to expand) Abstract: While deep learning has shown tremendous success in many scientific domains, it remains a grand challenge to incorporate first principles in a systematic manner into such models. In this talk, I will demonstrate how to incorporate physical principles such as symmetry, conservation, and multi-scale into deep neural networks for forecasting and uncertainty quantification. I will showcase the applications of these models to challenging problems in climate science. Our methods demonstrate significant improvement in physical consistency, sample efficiency, and generalization in complex spatiotemporal dynamics. Bio: Dr. Rose Yu is an assistant professor at the University of California San Diego, Department of Computer Science and Engineering. She earned her Ph.D. in Computer Sciences at USC in 2017. She was subsequently a Postdoctoral Fellow at Caltech. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data analysis, with applications to sustainability, health, and physical sciences. Among her awards, she has won NSF CAREER Award, Faculty Research Award from JP Morgan, Facebook, Google, Amazon, and Adobe, Several Best Paper Awards, Best Dissertation Award at USC, and was nominated as one of the MIT Rising Stars in EECS.
	Poster Session 2 Details: (click to expand) Posters presented in this slot are listed below. Papers track: (1) Function Approximations for Reinforcement Learning Controller for Wave Energy Converters (4) Bayesian inference for aerosol vertical profiles (21) Machine learning emulation of a local-scale UK climate model (23) Learning evapotranspiration dataset corrections from water cycle closure supervision (26) Optimizing Japanese dam reservoir inflow forecast for efficient operation (28) Data-Driven Optimal Solver for Coordinating a Sustainable and Stable Power Grid (32) Towards a spatially transferable super resolution model for downscaling Antarctic surface melt (36) Exploring Randomly Wired Neural Networks for Climate Model Emulation (38) SustainGym: A Benchmark Suite of Reinforcement Learning for Sustainability Applications (40) AutoML for Climate Change: A Call to Action (45) Reconstruction of Grid Measurements in the Presence of Adversarial Attacks (59) Climate Policy Tracker: Pipeline for automated analysis of public climate policies (61) Controllable Generation for Climate Modeling (68) Positional Encoder Graph Neural Networks for Geographic Data (69) Image-based Early Detection System for Wildfires (71) Pyrocast: a Machine Learning Pipeline to Forecast Pyrocumulonimbus (PyroCb) Clouds (74) Transformers for Fast Emulation of Atmospheric Chemistry Box Models (76) Neural Representation of the Stratospheric Ozone Layer (77) DL-Corrector-Remapper: A grid-free bias-correction deep learning methodology for data-driven high-resolution global weather forecasting (81) An Unsupervised Learning Perspective on the Dynamic Contribution to Extreme Precipitation Changes (82) An Interpretable Model of Climate Change Using Correlative Learning (83) Multimodal Wildland Fire Smoke Detection (85) Accessible Large-Scale Plant Pathology Recognition (86) Dynamic weights enabled Physics-Informed Neural Network for simulating the mobility of Engineered Nano Particles in a contaminated aquifer (87) Calibration of Large Neural Weather Models (90) Learning Surrogates for Diverse Emission Models Proposals track: (94) Guided Transformer Network for Detecting Methane Emissions in Sentinel-2 Satellite Imagery (97) Deep-S2SWind: A data-driven approach for improving Sub-seasonal wind predictions (102) Deep learning-based bias adjustment of decadal climate predictions (104) Detecting Floods from Cloudy Scenes: A Fusion Approach Using Sentinel-1 and Sentinel-2 Imagery (108) Estimating Heating Loads in Alaska using Remote Sensing and Machine Learning Methods (109) Interpretable Spatiotemporal Forecasting of Arctic Sea Ice Concentration at Seasonal Lead Times (112) Personalizing Sustainable Agriculture with Causal Machine Learning Tutorials track: (116) Automating the creation of LULC datasets for semantic segmentation
	Spotlight Presentations
	(54) "Machine Learning for Activity-Based Road Transportation Emissions Estimation"
	(55) "Bayesian State-Space SCM for Deforestation Baseline Estimation for Forest Carbon Credit"
	Tutorials
	(113) "Disaster Risk Monitoring Using Satellite Imagery"
	(114) "Machine Learning for Predicting Climate Extremes"
	Break
	Spotlight Presentations
	(79) "Adaptive Bias Correction for Improved Subseasonal Forecasting"
	(77) "DL-Corrector-Remapper: A grid-free bias-correction deep learning methodology for data-driven high-resolution global weather forecasting"
	(65) "A Multi-Scale Deep Learning Framework for Projecting Weather Extremes"
	(31) "FIRO: A Deep-neural Network for Wildfire Forecast with Interpretable Hidden States"
	(42) "Cross Modal Distillation for Flood Extent Mapping"
	Closing Remarks
	Poster Session 3 Details: (click to expand) Posters presented in this slot are listed below. Papers track: (1) Function Approximations for Reinforcement Learning Controller for Wave Energy Converters (4) Bayesian inference for aerosol vertical profiles (11) Levee protected area detection for improved flood risk assessment in global hydrology models (22) Bridging the Microwave Data Gap; Using Bayesian Deep Learning to “See” the Unseen (28) Data-Driven Optimal Solver for Coordinating a Sustainable and Stable Power Grid (36) Exploring Randomly Wired Neural Networks for Climate Model Emulation (37) Closing the Domain Gap -- Blended Synthetic Imagery for Climate Object Detection (45) Reconstruction of Grid Measurements in the Presence of Adversarial Attacks (55) Bayesian State-Space SCM for Deforestation Baseline Estimation for Forest Carbon Credit (62) Learn to Bid: Deep Reinforcement Learning with Transformer for Energy Storage Bidding in Energy and Contingency Reserve Markets (80) Nowformer : A Locally Enhanced Temporal Learner for Precipitation Nowcasting (83) Multimodal Wildland Fire Smoke Detection (86) Dynamic weights enabled Physics-Informed Neural Network for simulating the mobility of Engineered Nano Particles in a contaminated aquifer (93) Synthesis of Realistic Load Data: Adversarial Networks for Learning and Generating Residential Load Patterns Proposals track: (94) Guided Transformer Network for Detecting Methane Emissions in Sentinel-2 Satellite Imagery (95) Identification of medical devices using machine learning on distribution feeder data for informing power outage response (98) Towards Low Cost Automated Monitoring of Life Below Water to De-risk Ocean-Based Carbon Dioxide Removal and Clean Power (108) Estimating Heating Loads in Alaska using Remote Sensing and Machine Learning Methods
	Networking

Accepted Works

Works were submitted to one of three tracks: Papers, Proposals, or Tutorials.

Click the links below for information about each submission, including slides, videos, and papers.

Papers

Title	Authors
(1) Function Approximations for Reinforcement Learning Controller for Wave Energy Converters	Soumyendu Sarkar (Hewlett Packard Enterprise); Vineet Gundecha (Hewlett Packard Enterpise); Alexander Shmakov (UC Irvine); Sahand Ghorbanpour (Hewlett Packard Enterprise); Ashwin Ramesh Babu (Hewlett Packard Enterprise Labs); Alexandre Pichard (Carnegie Clean Energy); Mathieu Cocho (Carnegie Clean Energy)
(2) Image-Based Soil Organic Carbon Estimation from Multispectral Satellite Images with Fourier Neural Operator and Structural Similarity	Ken C. L. Wong (IBM Research – Almaden Research Center); Levente Klein (IBM Research); Ademir Ferreira da Silva (IBM Research); Hongzhi Wang (IBM Almaden Research Center); Jitendra Singh (IBM Research - India); Tanveer Syeda-Mahmood (IBM Research)
(3) SolarDK: A high-resolution urban solar panel image classification and localization dataset	Maxim MK Khomiakov (DTU); Julius Radzikowski (DTU); Carl Schmidt (DTU); Mathias Sørensen (DTU); Mads Andersen (DTU); Michael Andersen (Technical University of Denmark); Jes Frellsen (Technical University of Denmark)
(4) Bayesian inference for aerosol vertical profiles	Shahine Bouabid (University of Oxford); Duncan Watson-Parris (University of Oxford); Dino Sejdinovic (University of Adelaide)
(5) Optimizing toward efficiency for SAR image ship detection	Arthur Van Meerbeeck (KULeuven); Ruben Cartuyvels (KULeuven); Jordy Van Landeghem (KULeuven); Sien Moens (KU Leuven)
(6) AutoML-based Almond Yield Prediction and Projection in California	Shiheng Duan (Lawrence Livermore National Laboratory); Shuaiqi Wu (University of California, Davis); Erwan Monier (University of California, Davis); Paul Ullrich (University of California, Davis)
(7) Attention-Based Scattering Network for Satellite Imagery	Jason Stock (Colorado State University); Charles Anderson (Colorado State University)
(8) Discovering Interpretable Structural Model Errors in Climate Models	Rambod Mojgani (Rice University); Ashesh K Chattopadhyay (Rice University); Pedram Hassanzadeh (Rice University)
(9) Aboveground carbon biomass estimate with Physics-informed deep network	Juan Nathaniel (Columbia University); Levente Klein (IBM Research); Campbell D Watson (IBM Reserch); Gabrielle Nyirjesy (Columbia University); Conrad M Albrecht (IBM Research)
(10) Improving the predictions of ML-corrected climate models with novelty detection	Clayton H Sanford (Columbia); Anna Kwa (Allen Institute for Artificial Intelligence); Oliver Watt-Meyer (Allen Institute for AI); Spencer Clark (Allen Institute for AI); Noah Brenowitz (Allen Institute for AI); Jeremy McGibbon (Allen Institute for AI); Christopher Bretherton (Allen Institute for AI)
(11) Levee protected area detection for improved flood risk assessment in global hydrology models	Masato Ikegawa (Kobe University); Tristan E.M Hascoet (Kobe University); Victor Pellet (Observatoire de Paris); Xudong Zhou (The University of Tokyo); Tetsuya Takiguchi (Kobe University); Dai Yamazaki (The University of Tokyo)
(12) Scene-to-Patch Earth Observation: Multiple Instance Learning for Land Cover Classification	Joseph Early (University of Southampton); Ying-Jung C Deweese (Georgia Insititute of Technology); Christine Evers (University of Southampton); Sarvapali Ramchurn (University of Southampton)
(13) Deep learning for downscaling tropical cyclone rainfall	Emily Vosper (University of Bristol); Lucy Harris (University of Oxford); Andrew McRae (University of Oxford); Laurence Aitchison (University of Bristol); Peter Watson (Bristol); Raul Santos Rodriguez (University of Bristol); Dann Mitchell (University of Bristol)
(14) Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes	So Takao (UCL); Sean Nassimiha (UCL); Peter Dudfield (Open Climate Fix); Jack Kelly (Open Climate Fix); Marc Deisenroth (University College London)
(15) Identifying latent climate signals using sparse hierarchical Gaussian processes	Matt Amos (Lancaster University); Thomas Pinder (Lancaster University); Paul Young (Lancaster University)
(16) Towards dynamical stability analysis of sustainable power grids using Graph Neural Networks	Christian Nauck (PIK); Michael Lindner (PIK); Konstantin Schürholt (University of St. Gallen); Frank Hellmann (PIK)
(17) Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation	Alexis Groshenry (Kayrros); Clément Giron (Kayrros); Alexandre d'Aspremont (CNRS, DI, Ecole Normale Supérieure; Kayrros); Thomas Lauvaux (University of Reims Champagne Ardenne, GSMA, UMR 7331); Thibaud Ehret (Centre Borelli)
(18) Probabilistic forecasting of regional photovoltaic power production based on satellite-derived cloud motion	Alberto Carpentieri (Bern University of Applied Science); Doris Folini (Institute for Atmospheric and Climate Science, ETH Zurich); Martin Wild (Institute for Atmospheric and Climate Science, ETH Zurich); Angela Meyer (Bern University of Applied Science)
(19) Robustifying machine-learned algorithms for efficient grid operation	Nicolas Christianson (California Institute of Technology); Christopher Yeh (California Institute of Technology); Tongxin Li (The Chinese University of Hong Kong (Shenzhen)); Mahdi Torabi Rad (Beyond Limits); Azarang Golmohammadi (Beyond Limits, Inc.); Adam Wierman (California Institute of Technology)
(20) Deep Hydrology: Hourly, Gap-Free Flood Maps Through Joint Satellite and Hydrologic Modelling	Tanya Nair (Cloud To Street); Veda Sunkara (Cloud to Street); Jonathan Frame (Cloud to Street); Philip Popien (Cloud to Street); Subit Chakrabarti (Cloud To Street)
(21) Machine learning emulation of a local-scale UK climate model	Henry Addison (University of Bristol); Elizabeth Kendon (Met Office Hadley Centre); Suman Ravuri (DeepMind); Laurence Aitchison (University of Bristol); Peter Watson (Bristol)
(22) Bridging the Microwave Data Gap; Using Bayesian Deep Learning to “See” the Unseen	Pedro Ortiz (Naval Postgraduate School); Eleanor Casas (Naval Postgraduate School); Marko Orescanin (Naval Postgraduate School); Scott Powell (Naval Postgraduate School)
(23) Learning evapotranspiration dataset corrections from water cycle closure supervision	Tristan E.M Hascoet (Kobe University); Victor Pellet (LERMA); Filipe Aires (LERMA)
(24) Convolutional Neural Processes for Inpainting Satellite Images: Application to Water Body Segmentation	Alexander Pondaven (Imperial College London); Mart Bakler (Imperial College London); Donghu Guo (Imperial College London); Hamzah Hashim (Imperial College London); Martin G Ignatov (Imperial college London); Samir Bhatt (Imperial College London); Seth Flaxman (Oxford); Swapnil Mishra (Imperial College London); Elie Alhajjar (USMA); Harrison Zhu (Imperial College London)
(25) A POMDP Model for Safe Geological Carbon Sequestration	Anthony Corso (Stanford University); Yizheng Wang (Stanford Univerity); Markus Zechner (Stanford University); Jef Caers (Stanford University); Mykel J Kochenderfer (Stanford University)
(26) Optimizing Japanese dam reservoir inflow forecast for efficient operation	Keisuke Yoshimi (Kobe University); Tristan E.M Hascoet (Kobe University); Rousslan F. Julien Dossa (Kobe University); Ryoichi Takashima (Kobe University); Tetsuya Takiguchi (Kobe University); Satoru Oishi (Kobe University)
(27) Deep Climate Change: A Dataset and Adaptive domain pre-trained Language Models for Climate Change Related Tasks	Saeid Vaghefi (University of Zürich); Veruska Muccione (University of Zürich); Christian Huggel (University of Zürich); Hamed Khashehchi (2w2e GmbH); Markus Leippold (University of Zurich)
(28) Data-Driven Optimal Solver for Coordinating a Sustainable and Stable Power Grid	Junfei Wang (York University); Pirathayini Srikantha (York University)
(29) Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation	Raghul Parthipan (University of Cambridge); Damon Wischik (Univeristy of Cambridge)
(30) Explainable Multi-Agent Recommendation System for Energy-Efficient Decision Support in Smart Homes	Alona Zharova (Humboldt University of Berlin); Annika Boer (Humboldt University of Berlin); Julia Knoblauch (Humboldt University of Berlin); Kai Ingo Schewina (Humboldt University of Berlin); Jana Vihs (Humboldt University of Berlin)
(31) FIRO: A Deep-neural Network for Wildfire Forecast with Interpretable Hidden States	Eduardo R Rodrigues (MSR); Campbell D Watson (IBM Reserch); Bianca Zadrozny (IBM Research); Gabrielle Nyirjesy (Columbia University)
(32) Towards a spatially transferable super resolution model for downscaling Antarctic surface melt	Zhongyang Hu (IMAU); Yao Sun (TUM); Peter Kuipers Munneke (IMAU); Stef Lhermitte (TU Delft); Xiaoxiang Zhu (Technical University of Munich,Germany)
(33) Forecasting European Ozone Air Pollution With Transformers	Seb Hickman (University of Cambridge); Paul Griffiths (University of Cambridge); Alex Archibald (University of Cambridge); Peer Nowack (Imperial College London); Elie Alhajjar (USMA)
(34) Stability Constrained Reinforcement Learning for Real-Time Voltage Control	Jie Feng (UCSD); Yuanyuan Shi (University of California San Diego); Guannan Qu (Carnegie Mellon University); Steven Low (California Institute of Technology); Animashree Anandkumar (Caltech); Adam Wierman (California Institute of Technology)
(35) Land Use Prediction using Electro-Optical to SAR Few-Shot Transfer Learning	Marcel Hussing (University of Pennsylvania); Karen Li (University of Pennsylvania); Eric Eaton (University of Pennsylvania)
(36) Exploring Randomly Wired Neural Networks for Climate Model Emulation	William J Yik (Harvey Mudd College); Sam J Silva (The University of Southern California); Andrew Geiss (Pacific Northwest National Laboratory); Duncan Watson-Parris (University of Oxford)
(37) Closing the Domain Gap -- Blended Synthetic Imagery for Climate Object Detection	Caleb Kornfein (Duke University); Frank Willard (Duke University); Caroline Tang (Duke University); Yuxi Long (Duke University); Saksham Jain (Duke University); Jordan Malof (Duke University); Simiao Ren (Duke University); Kyle Bradbury (Duke University)
(38) SustainGym: A Benchmark Suite of Reinforcement Learning for Sustainability Applications	Christopher Yeh (California Institute of Technology); Victor Li (California Institute of Technology); Rajeev Datta (California Institute of Technology); Yisong Yue (Caltech); Adam Wierman (California Institute of Technology)
(39) Remote estimation of geologic composition using interferometric synthetic-aperture radar in California’s Central Valley	Kyongsik Yun (California Institute of Technology); Kyra Adams (California Institute of Technology); John Reager (California Institute of Technology); Zhen Liu (California Institute of Technology); Caitlyn Chavez (California Institute of Technology); Michael Turmon (California Institute of Technology); Thomas Lu (California Institute of Technology)
(40) AutoML for Climate Change: A Call to Action	Renbo Tu (University of Toronto); Nicholas Roberts (University of Wisconsin-Madison); Vishak Prasad C (Indian Institute Of Technology, Bombay); Sibasis Nayak (Indian Institute of Technology, Bombay); Paarth Jain (Indian Institute of Technology Bombay); Frederic Sala (University of Wisconsin-Madison); Ganesh Ramakrishnan (IIT Bombay); Ameet Talwalkar (CMU); Willie Neiswanger (Stanford University); Colin White (Abacus.AI)
(41) Temperature impacts on hate speech online: evidence from four billion tweets	Annika Stechemesser (Potsdam Insitute for Climate Impact Research); Anders Levermann (Potsdam Institute for Climate Impact Research); Leonie Wenz (Potsdam Institute for Climate Impact Research)
(42) Cross Modal Distillation for Flood Extent Mapping	Shubhika Garg (Google); Ben Feinstein (Google); Shahar Timnat (Google); Vishal V Batchu (Google); gideon dror (The Academic College of Tel-Aviv-Yaffo); Adi Gerzi Rosenthal (Google); Varun Gulshan (Google Research)
(43) Transformer Neural Networks for Building Load Forecasting	Matthias Hertel (KIT); Simon Ott (KIT); Oliver Neumann (KIT); Benjamin Schäfer (KIT); Ralf Mikut (Karlsruhe Institute of Technology); Veit Hagenmeyer (Karlsruhe Institute of Technology (KIT))
(44) Estimating Chicago’s tree cover and canopy height using multi-spectral satellite imagery	John Francis (University College London)
(45) Reconstruction of Grid Measurements in the Presence of Adversarial Attacks	Amirmohammad Naeini (York University); Samer El Kababji (Western University); Pirathayini Srikantha (York University)
(46) Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble	Satyaki Chatterjee (Pattern Recognition Lab, FAU Erlangen-Nuremberg); Adithya Ramachandran (Pattern Recognition Lab, Friedrich Alexander University, Erlangen); Thorkil Flensmark Neergaard (Brønderslev Forsyning A/S); Andreas K Maier (Pattern Recognition Lab, FAU Erlangen-Nuremberg); Siming Bayer (Pattern Recognition Lab, Friedrich-Alexander University)
(47) Generalized Ice Detection on Wind Turbine Rotor Blades with Neural Style Transfer	Joyjit Chatterjee (University of Hull); Maria Teresa Alvela Nieto (University of Bremen); Hannes Gelbhardt (University of Bremen); Nina Dethlefs (University of Hull); Jan Ohlendorf (University of Bremen); Klaus-Dieter Thoben (University of Bremen)
(48) Identifying Compound Climate Drivers of Forest Mortality with β-VAE	Mohit Anand (Helmholtz Centre for Environmental Research - UFZ); Lily-belle Sweet (Helmholtz Centre for Environmental Research - UFZ); Gustau Camps-Valls (Universitat de València); Jakob Zscheischler (Helmholtz Centre for Environmental Research - UFZ)
(49) TCFD-NLP: Assessing alignment of climate disclosures using NLP for the financial markets	Rylen Sampson (Manifest Climate); Aysha Cotterill (Manifest Climate); Quoc Tien Au (Manifest Climate)
(50) Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes	Vanessa Boehm (UC Berkeley); Wei Ji Leong (The Ohio State University); Ragini Bal Mahesh (German Aerospace Center DLR); Ioannis Prapas (National Observatory of Athens); Siddha Ganju (Nvidia); Freddie Kalaitzis (University of Oxford); Edoardo Nemni (United Nations Satellite Centre (UNOSAT)); Raul Ramos-Pollan (Universidad de Antioquia)
(51) Hybrid Recurrent Neural Network for Drought Monitoring	Mengxue Zhang (Universitat de València); Miguel-Ángel Fernández-Torres (Universitat de València); Gustau Camps-Valls (Universitat de València)
(52) Deep Learning for Global Wildfire Forecasting	Ioannis Prapas (National Observatory of Athens); Akanksha Ahuja (NOA); Spyros Kondylatos (National Observatory of Athens); Ilektra Karasante (National Observatory of Athens); Lazaro Alonso (Max Planck Institute for Biogeochemistry); Eleanna Panagiotou (Harokopio University of Athens); Charalampos Davalas (Harokopio University of Athens); Dimitrios Michail (Harokopio University of Athens); Nuno Carvalhais (Max Planck Institute for Biogeochemistry); Ioannis Papoutsis (National Observatory of Athens)
(53) Causal Modeling of Soil Processes for Improved Generalization	Somya Sharma (U. Minnesota); Swati Sharma (Microsoft Research); Emre Kiciman (Microsoft Research); Andy Neal (Rothamstead); Ranveer Chandra (Microsoft Research); John Crawford (University of Glasgow); Sara Malvar (Microsoft); Eduardo R Rodrigues (MSR)
(54) Machine Learning for Activity-Based Road Transportation Emissions Estimation	Derek Rollend (JHU); Kevin Foster (JHU); Tomek Kott (JHU); Rohita Mocharla (JHU); Rodrigo Rene Rai Munoz Abujder (Johns Hopkins Applied Physics Laboratory); Neil Fendley (JHU/APL); Chace Ashcraft (JHU/APL); Frank Willard (JHU); Marisa Hughes (JHU)
(55) Bayesian State-Space SCM for Deforestation Baseline Estimation for Forest Carbon Credit Best Paper: Pathway to Impact	Keisuke Takahata (sustainacraft, Inc.); Hiroshi Suetsugu (sustainacraft, Inc.); Keiichi Fukaya (National Institute for Environmental Studies); Shinichiro Shirota (Hitotsubashi University)
(56) Estimating Corporate Scope 1 Emissions Using Tree-Based Machine Learning Methods	Elham Kheradmand (University of Montreal); Maida Hadziosmanovic (Concordia University); Nazim Benguettat (Concordia); H. Damon Matthews (Concordia University); Shannon M. Lloyd (Concordia University)
(57) Analyzing Micro-Level Rebound Effects of Energy Efficient Technologies	Mayank Jain (University College Dublin); Mukta Jain (Delhi School of Economics); Tarek T. Alskaif (Wageningen University); Soumyabrata Dev (University College Dublin)
(58) Comparing the carbon costs and benefits of low-resource solar nowcasting	Ben Dixon (UCL); Jacob Bieker (Open Climate Fix); Maria Perez-Ortiz (University College London)
(59) Climate Policy Tracker: Pipeline for automated analysis of public climate policies	Artur Żółkowski (Warsaw University of Technology); Mateusz Krzyziński (Warsaw University of Technology); Piotr Wilczyński (Warsaw University of Technology); Stanisław Giziński (University of Warsaw); Emilia Wiśnios (University of Warsaw); Bartosz Pieliński (University of Warsaw); Julian Sienkiewicz (Warsaw University of Technology); Przemysław Biecek (Warsaw University of Technology)
(60) Topic correlation networks inferred from open-ended survey responses reveal signatures of ideology behind carbon tax opinion	Maximilian Puelma Touzel (Mila)
(61) Controllable Generation for Climate Modeling	Moulik Choraria (University of Illinois at Urbana-Champaign); Daniela Szwarcman (IBM Research); Bianca Zadrozny (IBM Research); Campbell D Watson (IBM Reserch); Lav Varshney (UIUC: ECE)
(62) Learn to Bid: Deep Reinforcement Learning with Transformer for Energy Storage Bidding in Energy and Contingency Reserve Markets	Jinhao Li (Monash University); Changlong Wang (Monash University); Yanru Zhang (University of Electronic Science and Technology of China); Hao Wang (Monash University)
(63) Curriculum Based Reinforcement Learning to Avert Cascading Failures in the Electric Grid	Amarsagar Reddy Ramapuram Matavalam (Arizona State University); Kishan Guddanti (Pacific Northwest National Lab); Yang Weng (Arizona State University)
(64) Short-range forecasts of global precipitation using deep learning-augmented numerical weather prediction	Manmeet Singh (The University of Texas at Austin); Vaisakh SB (Indian Institute of Tropical Meteorology); Nachiketa Acharya (Department of Meteorology and Atmospheric Science,Pennsylvania State University); Aditya Grover (UCLA); Suryachandra A. Rao (Indian Institute of Tropical Meteorology); Bipin Kumar (Indian Institute of Tropical Meteorology); Zong-Liang Yang (The University of Texas at Austin); Dev Niyogi (The University of Texas at Austin)
(65) A Multi-Scale Deep Learning Framework for Projecting Weather Extremes Best Paper: ML Innovation	Antoine Blanchard (MIT); Nishant Parashar (Verisk Analytics); Boyko Dodov (Verisk Analytics); Christian Lessig (Otto-von-Guericke-Universitat Magdeburg); Themis Sapsis (MIT)
(66) A Global Classification Model for Cities using ML	Doron Hazan (MIT); Mohamed Habashy (Massachusetts Institute of Technology); Mohanned ElKholy (Massachusetts Institute of Technology); Omer Mousa (American University in Cairo); Norhan M Bayomi (MIT Environmental Solutions Initiative); Matias Williams (Massachusetts Institute of Technology); John Fernandez (Massachusetts Institute of Technology)
(67) EnhancedSD: Downscaling Solar Irradiance from Climate Model Projections	Nidhin Harilal (University of Colorado, Boulder); Bri-Mathias S Hodge (University of Colorado Boulder); Claire Monteleoni (University of Colorado Boulder); Aneesh Subramanian (University of California, San Diego)
(68) Positional Encoder Graph Neural Networks for Geographic Data	Konstantin Klemmer (Microsoft Research); Nathan S Safir (University of Georgia); Daniel B Neill (New York University)
(69) Image-based Early Detection System for Wildfires	Omkar Ranadive (Alchera X); Jisu Kim (Alchera); Serin Lee (Alchera X); Youngseo Cha (Alchera); Heechan Park (Alchera); Minkook Cho (Alchera); Young Hwang (Alchera)
(70) Towards Global Crop Maps with Transfer Learning	Hyun-Woo Jo (Korea University); Alkiviadis Marios Koukos (National Observatory of Athens); Vasileios Sitokonstantinou (National Observatory of Athens); Woo-Kyun Lee (Korea University); Charalampos Kontoes (National Observatory of Athens)
(71) Pyrocast: a Machine Learning Pipeline to Forecast Pyrocumulonimbus (PyroCb) Clouds	Kenza Tazi (University of Cambridge); Emiliano Díaz Salas-Porras (University of Valencia); Ashwin Braude (Institut Pierre-Simon Laplace); Daniel Okoh (National Space Research and Development Agency); Kara D. Lamb (Columbia University); Duncan Watson-Parris (University of Oxford); Paula Harder (Fraunhofer ITWM); Nis Meinert (Pasteur Labs)
(72) Evaluating Digital Tools for Sustainable Agriculture using Causal Inference	Ilias Tsoumas (National Observatory of Athens); Georgios Giannarakis (National Observatory of Athens); Vasileios Sitokonstantinou (National Observatory of Athens); Alkiviadis Marios Koukos (National Observatory of Athens); Dimitra A Loka (Hellenic Agricultural Organization ELGO DIMITRA); Nikolaos S Bartsotas (National Observatory of Athens); Charalampos Kontoes (National Observatory of Athens); Ioannis N Athanasiadis (Wageningen University and Research)
(73) Generating physically-consistent high-resolution climate data with hard-constrained neural networks	Paula Harder (Mila); Qidong Yang (New York University); Venkatesh Ramesh (Mila); Prasanna Sattigeri (IBM Research); Alex Hernandez-Garcia (Mila - Quebec AI Institute); Campbell D Watson (IBM Reserch); Daniela Szwarcman (IBM Research); David Rolnick (McGill University, Mila)
(74) Transformers for Fast Emulation of Atmospheric Chemistry Box Models	Herbie Bradley (University of Cambridge); Nathan Luke Abraham (National Centre for Atmospheric Science, UK); Peer Nowack (Imperial College London); Doug McNeall (Met Office Hadley Centre, UK)
(75) Flood Prediction with Graph Neural Networks	Arnold N Kazadi (Rice University); James Doss-Gollin (Rice University); Antonia Sebastian (UNC Chapel Hill); Arlei Silva (Rice University)
(76) Neural Representation of the Stratospheric Ozone Layer	Helge Mohn (Alfred Wegener Institute for Polar and Marine Research); Daniel Kreyling (Alfred Wegener Institute for Polar and Marine Research); Ingo Wohltmann (Alfred Wegener Institute for Polar and Marine Research); Ralph Lehmann (Alfred Wegener Institute for Polar and Marine Research); Peter Maaß (University of Bremen); Markus Rex (Alfred Wegener Institute for Polar and Marine Research)
(77) DL-Corrector-Remapper: A grid-free bias-correction deep learning methodology for data-driven high-resolution global weather forecasting	Tao Ge (Washington University in St. Louis); Jaideep Pathak (NVIDIA Corporation); Akshay Subramaniam (NVIDIA); Karthik Kashinath (NVIDIA)
(78) Industry-scale CO2 Flow Simulations with Model-Parallel Fourier Neural Operators	Philipp A Witte (Microsoft); Russell Hewett (Microsoft); Ranveer Chandra (Microsoft Research)
(79) Adaptive Bias Correction for Improved Subseasonal Forecasting	Soukayna Mouatadid (University of Toronto); Paulo Orenstein (IMPA); Genevieve E Flaspohler (MIT); Judah Cohen (AER); Miruna Oprescu (Cornell University); Ernest Fraenkel (MIT); Lester Mackey (Microsoft Research New England)
(80) Nowformer : A Locally Enhanced Temporal Learner for Precipitation Nowcasting	Jinyoung Park (KAIST); Inyoung Lee (KAIST); Minseok Son (KAIST); Seungju Cho (KAIST); Changick Kim (KAIST)
(81) An Unsupervised Learning Perspective on the Dynamic Contribution to Extreme Precipitation Changes	Griffin S Mooers (UC Irvine); Tom Beucler (University of Lausanne); Michael Pritchard (UCI); Stephan Mandt (University of California, Irivine)
(82) An Interpretable Model of Climate Change Using Correlative Learning	Charles Anderson (Colorado State University); Jason Stock (Colorado State University)
(83) Multimodal Wildland Fire Smoke Detection	Mai Nguyen (University of California San Diego); Shreyas Anantha Ramaprasad (University of California San Diego); Jaspreet Kaur Bhamra (University of California San Diego); Siddhant Baldota (University of California San Diego); Garrison Cottrell (UC San Diego)
(84) Using uncertainty-aware machine learning models to study aerosol-cloud interactions	Maëlys Solal (University of Oxford); Andrew Jesson (University of Oxford); Yarin Gal (University of Oxford); Alyson Douglas (University of Oxford)
(85) Accessible Large-Scale Plant Pathology Recognition	Marcos V. Conde (University of Würzburg); Dmitry Gordeev (H2O.ai)
(86) Dynamic weights enabled Physics-Informed Neural Network for simulating the mobility of Engineered Nano Particles in a contaminated aquifer	Shikhar Nilabh (Amphos21)
(87) Calibration of Large Neural Weather Models	Andre Graubner (Nvidia); Kamyar Kamyar Azizzadenesheli (Nvidia); Jaideep Pathak (NVIDIA Corporation); Morteza Mardani (Nvidia); Mike Pritchard (Nvidia); Karthik Kashinath (Nvidia); Anima Anandkumar (NVIDIA/Caltech)
(88) Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs	Claire Robin (Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Jena, Germany); 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); Jeran Poehls (Max-Planck-Institute for Biogeochemistry); Lazaro Alonzo (Max-Planck-Institute for Biogeochemistry Max-Planck-Institute for Biogeochemistry); Nuno Carvalhais (Max-Planck-Institute for Biogeochemistry); Markus Reichstein (Max Planck Institute for Biogeochemistry, Jena; Michael Stifel Center Jena for Data-Driven and Simulation Science, Jena)
(89) Generative Modeling of High-resolution Global Precipitation Forecasts	James Duncan (University of California, Berkeley); Peter Harrington (Lawrence Berkeley National Laboratory (Berkeley Lab)); Shashank Subramanian (Lawrence Berkeley National Laboratory)
(90) Learning Surrogates for Diverse Emission Models	Edgar Ramirez Sanchez (MIT); Catherine H Tang (Massachusetts Institute of Technology); Vindula Jayawardana (MIT); Cathy Wu (MIT)
(91) Continual VQA for Disaster Response Systems	Aditya Kane (Pune Institute of Computer Technology); V Manushree (Manipal Institute Of Technology); Sahil S Khose (Georgia Institute of Technology)
(92) Performance evaluation of deep segmentation models on Landsat-8 imagery	Akshat Bhandari (Manipal Institute of Technology, Manipal); Pratinav Seth (Manipal Institute of Technology); Sriya Rallabandi (Manipal Institute of Technology); Aditya Kasliwal (Manipal Institute of Technology); Sanchit Singhal (Manipal Institute of Technology)
(93) Synthesis of Realistic Load Data: Adversarial Networks for Learning and Generating Residential Load Patterns	Xinyu Liang (Monash University); Hao Wang (Monash University)

Proposals

Title	Authors
(94) Guided Transformer Network for Detecting Methane Emissions in Sentinel-2 Satellite Imagery	Satish Kumar (University of California, Santa Barbara); William Kingwill (Orbio Earth); Rozanne Mouton (Orbio Earth); Wojciech Adamczyk (ETH, Zurich); Robert Huppertz (Orbio Earth); Evan D Sherwin (Stanford University, Energy and Resources Engineering)
(95) Identification of medical devices using machine learning on distribution feeder data for informing power outage response	Paraskevi Kourtza (University of Edinburgh); Maitreyee Marathe (University of Wisconsin-Madison); Anuj Shetty (Stanford University); Diego Kiedanski (Yale University)
(96) Analyzing the global energy discourse with machine learning	Malte Toetzke (ETH Zurich); Benedict Probst (ETH Zurich); Yasin Tatar (ETH Zurich); Stefan Feuerriegel (LMU Munich); Volker Hoffmann (ETH Zurich)
(97) Deep-S2SWind: A data-driven approach for improving Sub-seasonal wind predictions	Noelia Otero Felipe (University of Bern); Pascal Horton (University of Bern)
(98) Towards Low Cost Automated Monitoring of Life Below Water to De-risk Ocean-Based Carbon Dioxide Removal and Clean Power	Kameswari Devi Ayyagari (Dalhousie University); Christopher Whidden (Dalhousie University); Corey Morris (Department of Fisheries and Oceans); Joshua Barnes (National Research Council Canada)
(99) Towards the Automatic Analysis of Ceilometer Backscattering Profiles using Unsupervised Learning	Michael Dammann (HAW Hamburg); Ina Mattis (Deutscher Wetterdienst); Michael Neitzke (HAW Hamburg); Ralf Möller (University of Lübeck)
(100) Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics	Max C Schrader (University of Alabama); Navish Kumar (IIT Kharagpur); Nicolas Collignon (University of Edinburgh); Maria S Astefanoaei (IT University of Copenhagen); Esben Sørig (Kale Collective); Soonmyeong Yoon (Kale Collective); Kai Xu (University of Edinburgh); Akash Srivastava (MIT-IBM)
(101) An Inversion Algorithm of Ice Thickness and InSAR Data for the State of Friction at the Base of the Greenland Ice Sheet	Aryan Jain (Amador Valley High School); Jeonghyeop Kim (Stony Brook University); William Holt (Stony Brook University)
(102) Deep learning-based bias adjustment of decadal climate predictions	Reinel Sospedra-Alfonso (Environment and Climate Change Canada); Johannes Exenberger (Graz University of Technology); Marie C McGraw (Cooperative Institute for Research in the Atmosphere \| CIRA); Trung Kien Dang (National University of Singapore)
(103) Surrogate Modeling for Methane Dispersion Simulations Using Fourier Neural Operator	Qie Zhang (Microsoft); Mirco Milletari (Microsoft); Yagna Deepika Oruganti (Microsoft); Philipp A Witte (Microsoft)
(104) Detecting Floods from Cloudy Scenes: A Fusion Approach Using Sentinel-1 and Sentinel-2 Imagery	Qiuyang Chen (University of Edinburgh); Xenofon Karagiannis (Earth-i Ltd.); Simon M. Mudd (University of Edinburgh)
(105) Urban Heat Island Detection and Causal Inference Using Convolutional Neural Networks	Zachary D Calhoun (Duke University); Ziyang Jiang (Duke University); Mike Bergin (Duke University); David Carlson (Duke University)
(106) Forecasting Global Drought Severity and Duration Using Deep Learning	Akanksha Ahuja (NOA); Xin Rong Chua (Centre for Climate Research Singapore)
(107) ForestBench: Equitable Benchmarks for Monitoring, Reporting, and Verification of Nature-Based Solutions with Machine Learning	Lucas Czech (Carnegie Institution for Science); Björn Lütjens (MIT); David Dao (ETH Zurich)
(108) Estimating Heating Loads in Alaska using Remote Sensing and Machine Learning Methods	Madelyn Gaumer (University of Washington); Nick Bolten (Paul G. Allen School of Computer Science and Engineering, University of Washington); Vidisha Chowdhury (Heinz College of Information Systems and Public Policy, Carnegie Mellon University); Philippe Schicker (Heinz College of Information Systems and Public Policy, Carnegie Mellon University); Shamsi Soltani (Department of Epidemiology and Population Health, Stanford University School of Medicine); Erin D Trochim (University of Alaska Fairbanks)
(109) Interpretable Spatiotemporal Forecasting of Arctic Sea Ice Concentration at Seasonal Lead Times	Matthew Beveridge (Independent Researcher); Lucas Pereira (ITI, LARSyS, Técnico Lisboa)
(110) CliMedBERT: A Pre-trained Language Model for Climate and Health-related Text	Babak Jalalzadeh Fard (University of Nebraska Medical Center); Sadid A. Hasan (Microsoft); Jesse E. Bell (University of Nebraska Medical Center)
(111) Improving accuracy and convergence of federated learning edge computing methods for generalized DER forecasting applications in power grid	Vineet Jagadeesan Nair (MIT)
(112) Personalizing Sustainable Agriculture with Causal Machine Learning Best Paper: Proposals	Georgios Giannarakis (National Observatory of Athens); Vasileios Sitokonstantinou (National Observatory of Athens); Roxanne Suzette Lorilla (National Observatory of Athens); Charalampos Kontoes (National Observatory of Athens)

Tutorials

Title	Authors
(113) Disaster Risk Monitoring Using Satellite Imagery	Kevin Lee (NVIDIA); Siddha Ganju (NVIDIA); Edoardo Nemni (UNOSAT)
(114) Machine Learning for Predicting Climate Extremes	Hritik Bansal (UCLA); Shashank Goel (University of California Los Angeles); Tung Nguyen (University of California, Los Angeles); Aditya Grover (UCLA)
(115) FourCastNet: A practical introduction to a state-of-the-art deep learning global weather emulator	Jaideep Pathak (NVIDIA Corporation); Shashank Subramanian (Lawrence Berkeley National Laboratory); Peter Harrington (Lawrence Berkeley National Laboratory (Berkeley Lab)); Thorsten Kurth (Nvidia); Andre Graubner (Nvidia); Morteza Mardani (NVIDIA Corporation); David M. Hall (NVIDIA); Karthik Kashinath (Lawrence Berkeley National Laboratory); Anima Anandkumar (NVIDIA/Caltech)
(116) Automating the creation of LULC datasets for semantic segmentation	Sambhav S Rohatgi (Spacesense.ai); Anthony Mucia (Spacesense.ai)

Organizers

Peetak Mitra (Excarta Inc.)
Maria João Sousa (IST, ULisboa)
Mark Roth (Climate LLC)
Ján Drgoňa (PNNL)
Emma Strubell (CMU)
Yoshua Bengio (Mila, UdeM)

Tutorials Track Organizers

Ankur Mahesh (UC Berkeley)
Isabelle Tingzon (Thinking Machines Data Science)
Melanie Hanna (DataRobot)
Shafat Rahman (Otoll)
Sharon Xu (Indigo Ag)

Mentors

Adam Riesselman (Hippo Harvest)
Adeesh Kolluru (Carnegie Mellon University)
Aditya Grover (UCLA)
Andrii Krutsylo (Institute of Computer Science Polish Academy of Sciences)
Angelos Chronis (Austrian Institute of Technology)
Anuroop Sriram (Meta FAIR)
Aranildo Lima (Aquatic Informatics)
Bharathan Balaji (Amazon)
Elie Alhajjar (USMA)
Eunika Mercier-Laurent (IFIP)
Evan Sherwin (Stanford University)
Gopinath Rajendiran (Atsuya Technologies Pvt Ltd, India)
Gregory Gruz (EY)
Ibrahim El-chami (University of British Columbia)
James Doss-Gollin (Rice University)
Josephine Sullivan (Royal Institute of Technology, Stockholm)
Kumar Saurav (IBM Research Labs)
Lester Mackey (Microsoft Research)
Lucas Pereira (ITI, LARSyS, Técnico Lisboa)
Peetak Mitra (PARC)
Sadid Hasan (Microsoft)
Santiago Correa Cardona (BlocPower)
Sara Khalid (University of Oxford )
Tabia Ahmad (University of Strathclyde)
Veronica Nieves (University of Valencia)
Ying-Jung Deweese (Descartes Labs)
Yongli Zhu (Texas A&M University)

Program Committee

Alan Fortuny (Adidas)
Alberto C. Chapchap (GS Cap)
Alexandra V. Puchko (Western Washington University)
Andrew Ross (Arcadia)
Andrey Bernstein (NREL)
Aneesh Rangnekar (Rochester Institute of Technology)
Armi Tiihonen (Massachusetts Institute of Technology)
Arvind T. Mohan (Los Alamos National Laboratory)
Bedartha Goswami (University of Tübingen)
Bertrand Le Saux (European Space Agency (ESA))
Bianca Zadrozny (IBM Research)
Bill Cai (Amazon)
Bingqing Chen (Carnegie Mellon University)
Biswadip Dey (Siemens Corporation, Technology)
Brian Hutchinson (Western Washington University)
Catarina Barata (Institute for Systems and Robotics, Instituto Superior Técnico)
Christopher Yeh (California Institute of Technology)
Dan Lu (Oak Ridge National Laboratory)
Daniel Salles Civitarese (IBM Research, Brazil)
Daniela Szwarcman (IBM Research)
Dara M. Farrell (Graduate of University of Washington)
Dario Augusto Borges Oliveira (Technische Universität München)
David Dao (ETH)
David Rolnick (McGill University, Mila)
David Russell (Carnegie Mellon University)
Diego Kiedanski
Difan Zhang (PNNL)
Donna Vakalis (University of Toronto)
Duncan Watson-Parris (University of Oxford)
Evan D. Sherwin (Stanford University, Energy and Resources Engineering)
Frank Liu (Oak Ridge National Lab)
Fred Otieno (IBM)
Frederik Gerzer (Recogni)
Gege Wen (Stanford University)
Genevieve E. Flaspohler (MIT)
Geneviève Patterson
Hamid Alizadeh Pahlavan (Rice University)
Hannah R. Kerner (Arizona State University)
Hao Sheng (Stanford University)
Hari Prasanna Das (UC Berkeley)
Hovig Bayandorian
Ioana Colfescu (National Centre for Atmospheric Science)
Isabelle Tingzon (Thinking Machines Data Science)
Jan Drgona (Pacific Northwest National Laboratory)
Jeremy A. Irvin (Stanford)
Jonathan Fürst (NEC Laboratories Europe)
Jorge Montalvo Arvizu (Solario)
Joris Guerin (LAAS-CNRS)
Joyjit Chatterjee (University of Hull)
Julian de Hoog (The University of Melbourne)
Kaiser Olga (UNIversity)
Kalai Ramea (PARC)
Katarzyna B. Tokarska (ETH Zurich)
Kate Duffy (BAER Institute / NASA)
Kelly Kochanski (McKinsey & Company)
Kevin Barker (PNNL)
Kevin McCloskey (Google)
Kidane W. Degefa (Haramaya University)
Konstantin Klemmer (Microsoft Research)
Kris Sankaran (University of Wisconsin-Madison)
Kritika Gadpayle (CSTEP)
Lea Boche (EPRI)
Lester Mackey (Microsoft Research New England)
Levente Klein (IBM Research)
Lucas Kruitwagen (University of Oxford)
Lucas Spangher (U.C. Berkeley)
Lukas Kondmann (German Aerospace Center)
Lynn Kaack (Hertie School)
Maike Sonnewald (Princeton University)
Malachi Schram (Thomas Jefferson National Accelerator Facility)
Marcus Voss (TU Berlin)
Maria João Sousa (IDMEC, Instituto Superior Técnico, Universidade de Lisboa)
Maria Kaselimi (National Technical University of Athens)
Mark Roth (Climate LLC)
Markus Leippold (University of Zurich)
Massimo A. Bollasina (University of Edinburgh)
Matias Quintana (National University of Singapore)
Matteo Turchetta (ETH Zurich)
Meareg A. Hailemariam (Addis Ababa University)
Melanie Hanna (DataRobot)
Michael F. Howland (Stanford University)
Miguel Molina-Solana (Universidad de Granada)
Miguel-Ángel Fernández-Torres (Universitat de València)
Milan Jain (PNNL)
Mohamad Khalil (Newcastle University)
Muhammad F. Kasim (University of Oxford)
Nahian Ahmed (Oregon State University)
Nathan Kiner (Climate Change AI)
Niccolo Dalmasso (J.P. Morgan Chase)
Noman Bashir (University of Massachusetts Amherst)
Olalekan J. Akintande (University of Ibadan)
Olivia Mendivil Ramos (OneThree Biotech)
Paweł Gora (TensorCell)
Peetak P. Mitra (Excarta Inc.)
Priya L. Donti (Cornell Tech, MIT)
Qiao Kang (Memorial University)
Rambod Mojgani (Rice University)
Rasika V. Bhalerao (New York University)
Redouane Lguensat (IPSL)
Rishikesh Ranade (Ansys Inc)
Robin Dunn (Novartis)
Roman Leventov
Samarth Vadia (LMU Munich)
Samrat Chatterjee (Pacific Northwest National Laboratory)
Sara El Mekkaoui (EMI Engineering School)
Sasha Luccioni (Hugging Face)
Sayak Mukherjee (Pacific Northwest National Laboratory)
Sebastian Ruf (Northeastern University)
Shafat Rahman (Otoll)
Sharon Xu (Indigo Ag)
Shruti Kulkarni (Indian Institute of Science (IISc))
Simon Letzgus (Technische Universität Berlin)
Simone Fobi (Columbia University)
Soledad Galli (Climate Change AI)
Sookyung Kim (PARC)
Soumya Vasisht (Pacific Northwest National Laboratory)
Subhashis Hazarika (Palo Alto Research Center)
Thomas Walther (Utrecht University)
Tianle Yuan (NASA)
Tom Corringham (Scripps Institution of Oceanography)
Utkarsha Agwan (U.C. Berkeley)
Valentina Zantedeschi (INRIA, UCL)
Victoria Preston (MIT)
Vili Hätönen (Emblica)
Y. Qiang Sun (Rice University)
Yifei Guan (Rice University)
Yimeng Min (Cornell University)
Zhecheng Wang (Stanford University)
Zikri Bayraktar (Schlumberger Doll Research)
Zoltan Nagy (The University of Texas at Austin)

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:

Agriculture and food
Behavioural and social science
Buildings
Carbon capture and sequestration
Cities and urban planning
Climate finance and economics
Climate justice
Climate science and climate modeling
Disaster management and relief
Earth observations and monitoring
Earth science
Ecosystems and biodiversity
Extreme weather
Forestry and other land use
Health
Heavy industry and manufacturing
Local and indigenous knowledge systems
Materials science and discovery
Oceans and marine systems
Power and energy systems
Public policy
Societal adaptation and resilience
Supply chains
Transportation

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

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

All papers and proposals submissions must be through the submission website. Submissions will be reviewed double-blind; do your best to anonymize your submission, and do not include identifying information for authors in the PDF. Authors are required to use the workshop style template (based on the NeurIPS style files), available for LaTeX and docx format.

All tutorials submissions must be through the submission website.

Please see the Tips for Submissions and FAQ, and contact climatechangeai.neurips2022@gmail.com with questions.

Submission Tracks

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

PAPERS Track

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

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

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

Submissions creating novel datasets are welcomed. Datasets should be designed to permit machine learning research (e.g. formatted with clear benchmarks for evaluation). In this case, baseline experimental results on the dataset are preferred, but not required.

PROPOSALS Track

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

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

TUTORIALS Track

Interactive notebooks for insightful step-by-step walkthroughs

Submissions for the Tutorials track should introduce or demonstrate the use of ML methods and tools such as libraries, packages, services, datasets, or frameworks to address a problem related to climate change. Tutorial proposals (due Aug 18) should take the form of an abstract and should include a clear and concise description of users’ expected learning outcomes from the tutorial. Midterm tutorial submissions (due Sep 18) and Final tutorial submissions (due Nov 3) should be in the form of executable notebooks (e.g. Jupyter, Colab). Submissions will be reviewed based on their potential impact and overall usability by the climate and AI research community.

Tips for Submissions

For examples of typical formatting and content, see submissions from our previous workshops at NeurIPS 2021, ICML 2021, NeurIPS 2020, ICLR 2020, NeurIPS 2019, and ICML 2019.
Be explicit: Describe how your proposed approach addresses climate change, demonstrating an understanding of the application area.
Frame your work: The specific problem and/or data proposed should be contextualized in terms of prior work.
Address the impact: Describe the practical implications of your method in addressing the problem you identify, as well as any relevant societal impacts or potential side-effects. We recommend reading our further guidelines on this aspect here.
Explain the ML: Readers may not be familiar with the exact techniques you are using or may desire further detail.
Justify the ML: Describe why the ML method involved is needed, and why it is a good match for the problem.
Avoid jargon: Jargon is sometimes unavoidable but should be minimized. Ideal submissions will be accessible both to an ML audience and to experts in other relevant fields, without the need for field-specific knowledge. Feel free to direct readers to accessible overviews or review articles for background, where it is impossible to include context directly.

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.

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.

Mentorship Program

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

Expectations:

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

Examples of mentor-mentee interactions may include:

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 Sep 18.

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

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

Application

Applications are due by Aug 18.

Application to be a mentee: https://forms.gle/cZGoxDRYG3U7K4Lr9
Application to be a mentor: https://forms.gle/v2SNz1JTok3drf6w5

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.neurips2022@gmail.com and we will do our best to help resolve the situation. Potential breaches of the Code of Conduct will be responded to promptly as detailed therein.

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

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

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

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

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

Submission FAQ

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

Q: I’m not in machine learning. Can I still submit?
A: Yes, absolutely! We welcome submissions from many fields. Do bear in mind, however, that the majority of attendees of the workshop will have a machine learning background; therefore, other fields should be introduced sufficiently to provide context for the work.

Q: What if my submission is accepted but I can’t attend the workshop?
A: You may ask someone else to present your work in your stead.

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

Q: Can I send submissions directly by email?
A: No, please use the CMT website to make submissions.

Q: The submission website is asking for my name. Is this a problem for anonymization?
A: You should fill out your name and other info when asked on the submission website; CMT will keep your submission anonymous to reviewers.

Q: Do submissions for the Proposals track need to have experimental validation?
A: No, although some initial experiments or citation of published results would strengthen your submission.

Q: The submission website never sent me a confirmation email. Is this a problem?
A: No, the CMT system does not send automatic confirmation emails after a submission, though the submission should show up on the CMT page once submitted. If in any doubt regarding the submission process, please contact the organizers. Also please avoid making multiple submissions of the same article to CMT.

Q: Can I submit previously published work to this workshop?
A: Yes, though under limited circumstances. In particular, work that has previously been published at non-machine learning venues may be eligible for submission; however, work that has been published in conferences on machine learning or related fields is likely not eligible. If your work was previously accepted to a Climate Change AI workshop, this work should have changed or matured substantively to be eligible for resubmission. Please contact climatechangeai.neurips2022@gmail.com with any questions.

Q: Can I submit work to this workshop if I am also submitting to another NeurIPS 2022 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.