NeurIPS 2025 Workshop: Tackling Climate Change with Machine Learning

About

Many in the ML community wish to take action on climate change, but are unsure how to have the most impact. This workshop will highlight work that demonstrates that, while ML is no silver bullet, it 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 advancing theory to deploying new technology. Many of these actions represent high-impact opportunities for real-world change, and simultaneously pose interesting academic research problems.

This workshop is part of a series that aims to bring together those applying ML to climate change challenges and facilitate cross-pollination between ML researchers and experts in climate-relevant fields.

The main workshop will take place on December 7, 2025.

About NeurIPS

This workshop is part of the thirty ninth annual conference on Neural Information Processing Systems, one of the premier conferences on machine learning, which will be held between December 2 and 7, 2025 in San Diego, California. For information on how to attend the NeurIPS conference, please see https://neurips.cc/.

Important Dates

Speakers

Keynotes

Dr. Duncan Watson-Parris
Assistant Professor at the Halicioglu Data Science Institute, UC San Diego
Dr. Bichlien Nguyen
Principal Researcher, Microsoft Research

Panelists

Dr. Kalai Ramea
Co-Founder and CTO, Planette AI
Dr. Kelsey Doerksen
Postdoctoral Researcher, Arizona State University
Dr. Lauren Harrell
Data Scientist, Google Research
Dr. Cristian Bodnar
Co-Founder and Chief Scientist, Silurian AI
Dr. Steffen Knoblauch
Postdoctoral Researcher and Lecturer, Heidelberg University

Accepted Works

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

Click the links below for information about each submission.

Papers

Title Authors
(1) Uncertainty-Aware Prediction of Climate Extremes Using Fine-Tuned Time-Series Foundation Models Imran Nasim (IBM); Joao Lucas De Sousa Almeida (IBM)
(2) Fewer Shots, Better Implosions: Sample-Efficient Optimization for Inertial Confinement Fusion Ricardo Luna Gutierrez (Hewlett Packard Enterprise); Vineet Gundecha (Hewlett Packard Enterprise); Rahman Ejaz (Laboratory for Laser Energetics); Varchas Gopalaswamy (Laboratory for Laser Energetics); Riccardo Betti (Laboratory for Laser Energetics); Sahand Ghorbanpour (Hewlett Packard Enterprise); Aarne Lees (Laboratory for Laser Energetics); Soumyendu Sarkar (Hewlett Packard Enterprise)
(3) Discovering EV Charging Site Archetypes Through Few Shot Forecasting: The First U.S.-Wide Study Kshitij Nikhal (University of Nebraska Lincoln); Luke Ackerknecht (Alpha Grid); Benjamin Riggan (University of Nebraska Lincoln); Phil Stahlfeld (Alpha Grid)
(4) Bugs in Citizen-Science Data: Robust Biodiversity AI Begins with Clean Images Nikita Gavrilov (Fontys University of Applied Science); Gerard Schouten (Fontys University of Applied Science); Georgiana Manolache (Fontys University of Applied Science)
(5) Efficient Reinforcement Learning Implementations for Sustainable Operation of Liquid Cooled HPC Data Centers Avisek Naug (Hewlett Packard Enterprise); Antonio Guillen-Perez (Hewlett Packard Enterprise); Vineet Gundecha (Hewlett Packard Enterprise); Ashwin Ramesh Babu (Hewlett Packard Enterprise); Sahand Ghorbanpour (Hewlett Packard Enterprise); Ricardo Luna Gutierrez (Hewlett Packard Enterprise); Soumyendu Sarkar (Hewlett Packard Enterprise)
(6) AI-based Waste Mapping for Addressing Climate-Exacerbated Flood Risk Steffen Knoblauch (Heidelberg Institute for Geoinformation Technology); Levi Szamek (Heidelberg Institute for Geoinformation Technology); Iddy Chazua (OpenMap Development Tanzania); Benedcto Adamu (OpenMap Development Tanzania); Innocent Maholi (OpenMap Development Tanzania); Alexander Zipf (Heidelberg University)
(7) A Novel Integrated ML Approach Utilizing Radar & Satellite Imagery for Selective Logging Detection Saraswathy Amjith (MIT); Joshua Fan (Cornell University)
(8) Holistic Sustainability for Geo-Distributed Data Centers using Hierarchical Optimization Antonio Guillen-Perez (Hewlett Packard Enterprise); Avisek Naug (Hewlett Packard Enterprise); Vineet Gundecha (Hewlett Packard Enterprise); Sahand Ghorbanpour (Hewlett Packard Enterprise); Ricardo Luna Gutierrez (Hewlett Packard Enterprise); Soumyendu Sarkar (Hewlett Packard Enterprise)
(9) AI-driven Grid Optimization Can Reduce Emissions Kyri Baker (Google DeepMind); Jackie Kay (Google DeepMind); Miha Zgubic (Google DeepMind); Eric Perim Martins (Google DeepMind); Sofia Liguori (Google DeepMind); Steven Bohez (Google DeepMind); Sephora Madjiheurem (Google DeepMind); Marc Deisenroth (Google DeepMind); Laura Toni (Google DeepMind); Sims Witherspoon (Google DeepMind); Sophie Elster (Google DeepMind); Kyle Levin (Google DeepMind); Luis Piloto (Google DeepMind)
(10) Spatial Uncertainty Quantification in Wildfire Forecasting for Climate-Resilient Emergency Planning Aditya Chakravarty (Texas A&M University)
(11) Pure and Physics-encoded Spatiotemporal Deep Learning for Climate-Vegetation Dynamics Qianqiu Longyang (Kansas Geological Survey, The University of Kansas); Ruijie Zeng (School of Sustainable Engineering and the Built Environment, Arizona State University)
(12) Mapping Farmed Landscapes from Remote Sensing Michelangelo Conserva (Google); Alex WIlson (Google); Charlotte Stanton (Google); Vishal Batchu (Google); Varun Gulshan (Google)
(13) Spatio-Temporal Modelling of Rainfall via Frame-Level Autoregression Cristian Meo (TUDelft); Varun Sarathchandran (TUDelft); Avijit Majhi (TUDelft); Shao Hung (TUDelft); Carlo Saccardi (TUDelft); Ruben Imhoff (Deltares); Roberto Deidda (University of Cagliari); Remko Uijlenhoet (TUDelft); Justin Dauwels (TUDelft)
(14) TC-GTN: Temporal Convolution Graph Transformer Network for Hydrological Forecasting Ana Samac (The Institute for Artificial Intelligence Research and Development of Serbia); Milan Dotlic (The Institute for Artificial Intelligence Research and Development of Serbia); Luka Vinokic (The Institute for Artificial Intelligence Research and Development of Serbia); Milan Stojkovic (The Institute for Artificial Intelligence Research and Development of Serbia); Veljko Prodanovic (The Institute for Artificial Intelligence Research and Development of Serbia)
(15) Differentially Private Federated Learning for High-Accuracy Carbon Footprint Prediction that Protects Sensitive Industrial Data Vijay Narasimhan (EMD Electronics); Hanna Jarlaczyńska (Unit8); Tingting Ou (Columbia University)
(16) Image2image Tropical Cyclone wind field diagnosis with Pix2Pix generative adversarial networks (GANs) Sarah Ollier (Worldsphere)
(17) MEQNet: Deep Learning for Methane Point Source Emission Quantification from Sentinel-2 Observations Di Xu (Imperial College London); Philippa Mason (Imperial College London); Jianguo Liu (Imperial College London); Yanghua Wang (Imperial College London)
(18) FedRAIN-Lite: Federated Reinforcement Algorithms for Improving Idealised Numerical Weather and Climate Models Pritthijit Nath (University Of Cambridge); Sebastian Schemm (University Of Cambridge); Henry Moss (University Of Cambridge); Peter Haynes (University Of Cambridge); Emily Shuckburgh (University Of Cambridge); Mark Webb (Met Office)
(19) Adaptive Learning in Spatial Agent-Based Models for Climate Risk Assessment: A Geospatial Framework with Evolutionary Economic Agents Yara Mohajerani (Pluripotent Technologies Inc)
(20) Interactive Atmospheric Composition Emulation for Next-Generation Earth System Models Mohammad Erfani (Columbia University); Kara Lamb (Columbia University); Susanne Bauer (NASA Goddard Institute for Space Studies); Kostas Tsigaridis (Columbia University); Marcus van Lier-Walqui (Columbia University); Gavin Schmidt (NASA Goddard Institute for Space Studies)
(21) Foundation Models for Mapping Emission Sources and Acute Respiratory Infection (ARI) Hotspots Usman Nazir (University of Oxford); Sara Khalid (University of Oxford)
(22) Can Artificial Intelligence Global Weather Forecasting Models Capture Extreme Events? A Case Study of the 2022 Pakistan Floods Rodrigo Almeida (Fraunhofer HHI); Noelia Otero Felipe (Fraunhofer HHI); Miguel-Ángel Fernández-Torres (UC3M); Jackie Ma (Fraunhofer HHI)
(23) Newfoundland Marine Refuge Fish Classification Dataset (N-MARINE) Kameswari Devi Ayyagari (Dalhousie University); Maurice Drautz (Dalhousie University); Daniel Porter (Fisheries and Oceans Canada); Joshua Barnes (National Research Council Canada); Corey Morris (Fisheries and Oceans Canada); Christopher Whidden (Dalhousie University)
(24) Ensembles of Neural Surrogates for Parametric Sensitivity in Ocean Modeling Yixuan Sun (Argonne National Laboratory); Romain Egele (Oak Ridge National Laboratory); Sri Hari Krishna Narayanan (Argonne National Laboratory); Luke Van Roekel (Los Alamos National Laboratory); Carmelo Gonzales (NIVDIA); Steven Brus (Argonne National Laboratory); Balu Nadiga (Los Alamos National Laboratory); Sandeep Madireddy (Argonne National Laboratory); Prasanna Balaprakash (Oak Ridge National Laboratory)
(25) Revisiting Deep AC-OPF Oluwatomisin Dada (University of Cambridge)
(26) AI-Driven Temporal Super-Resolution for Flooding Prediction in Norfolk, Virginia Chetan Kumar (Old Dominion University); Diana McSpadden (Thomas Jefferson National Accelerator Facility); Malachi Schram (Thomas Jefferson National Accelerator Facility); Heather Richter (Old Dominion University); Yidi Wang (University of Virginia); Binata Roy (University of Virginia); Jonathan Goodall (University of Virginia)
(27) Quantifying Climate Policy Action and Its Links to Development Outcomes: A Cross-National Data-Driven Analysis Aditi Dutta (University of Exeter)
(28) Towards Energy-Efficient Buildings: A Hybrid Approach for Chiller Fault Detection Timothy Mulumba (NYU); Rita Sousa (NYU)
(29) CuMoLoS-MAE: A Masked Autoencoder for Remote Sensing Data Reconstruction Anurup Naskar (New York University); Nathanael Zhixin Wong (New York University); Sara Shamekh (New York University)
(30) Learning in Stackelberg Markov Games Jun He (Purdue University); Andrew Liu (Purdue University); Yihsu Chen (University of California, Santa Cruz)
(31) Probability calibration for precipitation nowcasting Lauri Kurki (Vaisala); Yaniel Cabrera (Vaisala); Samu Karanko (Vaisala)
(32) Sparse Local Implicit Image Function for sub-km Weather Downscaling Yago del Valle Inclan Redondo (Recursive); Enrique Arriaga-Varela (Recursive); Dmitry Lyamzin (Recursive); Pablo Cervantes (Recursive); Tiago Ramalho (Recursive)
(33) Machine Learning and Multi-source Remote Sensing in Forest Aboveground Biomass Estimation: A Review Autumn Nguyen (Mount Holyoke College); Sulagna Saha (Mount Holyoke College)
(34) Probabilistic modelling for methane leak detection in gas distribution networks Katherine Green (Guidehouse); Rubab Atwal (Guidehouse)
(35) Smallholder Agricultural Landscape Understanding Radhika Dua (New York University); Aditi Agarwal (Google DeepMind); Alex Wilson (Google Research); Hoang Tran (Google); Nikita Saxena (Google DeepMind); Ishan Deshpande (Google DeepMind); Bogdan Floristean (Google); Neelabh Goyal (Google); Ramya Cheruvu (Google); Ujwal Singh (Google); Jitendra Jalwaniya (Google); Amandeep Kaur (Arizoana State University); Batchu Venkat Vishal (Google Research); Yan Mayster (Google); Gaurav Aggarwal (Jio Platforms Limited); Alok Talekar (Google DeepMind); Vaibhav Rajan (Google DeepMind)
(36) Facade Segmentation for Solar Photovoltaic Suitability Ayca Duran (ETH Zurich); Christoph Waibel (VITO); Bernd Bickel (ETH Zurich); Iro Armeni (Stanford University); Arno Schlueter (ETH Zurich)
(37) Neural Network–enabled Domain-consistent Robust Optimisation for Global CO2 Reduction Potential of Gas Power Plants Waqar Ashraf (University College London); Talha Ansar (University of Engineering and Technology); Abdulelah Alshehri (King Saud University); Peipei Chen (University of Cambridge); Ramit Debnath (University of Cambridge); Vivek Dua (University College London)
(38) Spectral Channel Attention Network: A Method for Hyperspectral Semantic Segmentation of Cloud and Shadows Manuel Pérez-Carrasco (University of Concepción); Maya Nasr (Environmental Defense Fund, Harvard University); Sébastien Roche (Environmental Defense Fund, Harvard University); Chris Chan Miller (Environmental Defense Fund, Harvard University); Zhan Zhang (Harvard University); Core Francisco Park (Harvard University); Eleanor Walker (Harvard University); Cecilia Garraffo (Center for Astrophysics $|$ Harvard & Smithsonian); Douglas Finkbeiner (Harvard University); Ritesh Gautam (Environmental Defense Fund); Steven Wofsy (Harvard University)
(39) Training-Free Data Assimilation with GenCast Thomas Savary (ENS Paris-Saclay); François Rozet (University of Liège); Gilles Louppe (University of Liège)
(40) EcoEval: A Benchmark for Evaluating Large Language Model Handling of Climate Change Misinformation, False Beliefs, and Climate Policy Sentiment Nick Lechtenboerger (HPI); Pat Pataranutaporn (MIT Media Lab); Pattie Maes (MIT Media Lab)
(41) SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation Kai Cohrs (University of Valencia); Maria Gonzalez-Calabuig (University of Valencia); Vishal Nedungadi (Wageningen University and Research); Zuzanna Osika (Delft University of Technology); Ruben Cartuyvels (European Space Agency); Steffen Knoblauch (Heidelberg University); Joppe Massant (Ghent University); Shruti Nath (University of Oxford); Patrick Ebel (European Space Agency); Vasileios Sitokonstantinou (University of Valencia)
(42) DemandCast: Global hourly electricity demand forecasting Kevin Steijn (Open Energy Transition); Vamsi Priya Goli (Open Energy Transition); Enrico Antonini (Open Energy Transition)
(43) Physics-Informed Machine Learning Model for In-situ Life-Cycle Prediction of Condensation Trails Rambod Mojgani (RTRC); Sudeepta Mondal (RTRC); Soumalya Sarkar (RTRC); Miad Yazdani (RTRC)
(44) Understanding Ice Crystal Habit Diversity with Self-Supervised Learning Joseph Ko (Columbia University); Hariprasath Govindarajan (Linköping University); Fredrik Lindsten (Linköping University); Vanessa Przybylo (University at Albany); Kara Sulia (University at Albany); Marcus van Lier-Walqui (Columbia University); Kara Lamb (Columbia University)
(45) Physically Consistent Sampling For Ocean Model Initialization Blandine Gorce (Laboratoire d'Océanographie et du Climat (LOCEAN)); Luther Ollier (ULCO université Lille); David Kamm (Laboratoire d'Océanographie et du Climat LOCEAN); Etienne Meunier (Inria)
(46) Emulating Climate Across Scales with Conditional Spherical Fourier Neural Operators Jeremy McGibbon (Allen Institute for Artificial Intelligence); Troy Arcomano (Allen Institute for Artificial Intelligence); Spencer Clark (Allen Institute for Artificial Intelligence); James Duncan (Allen Institute for Artificial Intelligence); Brian Henn (Allen Institute for Artificial Intelligence); Anna Kwa (Allen Institute for Artificial Intelligence); W. Andre Perkins (Allen Institute for Artificial Intelligence); Oliver Watt-Meyer (Allen Institute for Artificial Intelligence); Elynn Wu (Allen Institute for Artificial Intelligence); Christopher Bretherton (Allen Institute for Artificial Intelligence)
(47) Early Reforestation Detection in Kenya Using Multi-Temporal Analysis Angela John (Saarland University)
(48) Inverse Modeling of Laser Pulse Shapes in Inertial Confinement Fusion with Auto-Regressive Models Vineet Gundecha (Hewlett Packard Enterprise); Ricardo Luna Gutierrez (Hewlett Packard Enterprise); Rahman Ejaz (Laboratory for Laser Energetics); Varchas Gopalaswamy (Laboratory for Laser Energetics); Riccardo Betti (Laboratory for Laser Energetics); Aarne Lees (Laboratory for Laser Energetics); Sahand Ghorbanpour (Hewlett Packard Enterprise); Soumyendu Sarkar (Hewlett Packard Enterprise)
(49) Multiscale Neural PDE Surrogates for Prediction and Downscaling: Application to Ocean Currents Abdessamad El-Kabid (Mila - Quebec AI Institute); Loubna Benabbou (Mila - Quebec AI Institute); Redouane Lguensat (Institut Pierre-Simon Laplace); Alex Hernandez-Garcia (Mila - Quebec AI Institute)
(50) Detection and Simulation of Urban Heat Islands Using a Fine-Tuned Geospatial Foundation Model for Microclimate Impact Prediction Jannis Fleckenstein (IBM); David Kreismann (IBM); Tamara Rosemary Govindasamy (IBM); Thomas Brunschwiler (IBM); Etienne Vos (IBM); Mattia Rigotti (IBM)
(51) Coupled Climate Simulations with ACE and Samudra Elynn Wu (Ai2); James Duncan (Ai2); Troy Arcomano (Ai2); Spencer Clark (Ai2); Brian Henn (Ai2); Anna Kwa (Ai2); Jeremy McGibbon (Ai2); Andre Perkins (Ai2); Oliver Watt-Meyer (Ai2); Christopher Bretherton (Ai2); Surya Dheeshjith (NYU); Adam Subel (NYU); Laure Zanna (NYU); William Hurlin (GFDL); William Gregory (Princeton University); Alistair Adcroft (Princeton University)
(52) Machine Learning Approaches to Identifying Tropical Waves That Develop into Hurricanes Haochang Luo (City College of New York)
(53) Ecosystem Insights through Extreme Values: A Fresh Look at Meteorological Drivers Christian Reimers (Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry); Claire Robin (Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry); Alexander Winkler (Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry)
(54) Satellite-Based Estimation of Soil Geologic Properties Using Physics-Guided Machine Learning Hrusikesha Pradhan (NASA Jet Propulsion Laboratory, California Institute of Technology)
(55) Operator Learning for Power Systems Simulation Matthew Schlegel (University of Calgary); Matthew Taylor (University of Alberta); Mostafa Farrokhabadi (University of Calgary)
(56) Scalable Geospatial Data Generation Using AlphaEarth Foundations Model Luc Houriez (X, The Moonshot Factory); Sebastian Pilarski (X, The Moonshot Factory); Behzad Vahedi (X, The Moonshot Factory); Ali Ahmadalipour (X, The Moonshot Factory); Teo Honda Scully (X, The Moonshot Factory); Nicholas Aflitto (X, The Moonshot Factory); David Andre (X, The Moonshot Factory); Caroline Jaffe (X, The Moonshot Factory); Martha Wedner (X, The Moonshot Factory); Rich Mazzola (X, The Moonshot Factory); Josh Jefferey (X, The Moonshot Factory); Ben Messinger (X, The Moonshot Factory); Sage McGinley-Smith (X, The Moonshot Factory); Sarah Russell (X, The Moonshot Factory)
(57) Swift: An Autoregressive Consistency Model for Efficient Weather Forecasting Jason Stock (Argonne National Laboratory); Troy Arcomano (AI2, ANL); Rao Kotamarthi (Argonne National Laboratory)
(58) Saving Wildlife with Generative AI: Latent Composite Flow Matching for Poaching Prediction Lingkai Kong (Harvard University); Haichuan Wang (Harvard University); Charles Emogor (University of Cambridge); Vincent B¨orsch-Supan (Harvard University); Lily Xu (Columbia University); Milind Tambe (Harvard University)
(59) Generative AI for weather data assimilation Ruizhe Huang (MIT); Qidong Yang (MIT); Jonathan Giezendanner (MIT); Sherrie Wang (MIT)
(60) Scalable & explainable ML for wildfire risk modeling in Southern Europe: A case-study in Portugal Ophélie Meuriot (Denmark Technical University); Jorge Soto Martin (Denmark Technical University); Beichen Zhang (Lawrence Berkeley National Laboratory); Francisco Camara Pereira (Denmark Technical University); Martin Drews (Denmark Technical University)
(61) A Graph Neural Network Approach for Localized and High-Resolution Temperature Forecasting Joud El-Shawa (Western University / Vector Institute); Elham Bagheri (Western University / Vector Institute); Sedef Akinli Kocak (Vector Institute); Yalda Mohsenzadeh (Western University / Vector Institute)
(62) Towards a Climate Counterfactual Autoencoder Frieder Loer (Institute for Meteorology, Leipzig University); Sebastian Sippel (Institute for Meteorology, Leipzig University)
(63) Global 3D Reconstruction of Clouds & Tropical Cyclones Shirin Ermis (University of Oxford); Cesar Aybar (Universitat de València); Lilli Freischem (University of Oxford); Stella Girtsou (National Observatory of Athens/National Technical University of Athens); Kyriaki-Margarita Bintsi (Harvard Medical School and Massachusetts General Hospital); Emiliano Diaz Salas-Porras (Universitat de València); William Jones (University of Oxford); Anna Jungbluth (European Space Agency); Benoit Tremblay (Environment and Climate Change Canada)
(64) Sensitivity Analysis for Climate Science with Generative Flow Models Alex Dobra (University of Oxford); Jakiw Pidstrigach (University of Oxford); Tim Reichelt (Univeristy of Oxford); Paolo Fraccaro (IBM Research Europe); Johannes Jakubik (IBM Research Europe); Anne Jones (IBM Research Europe); Christian Schroeder de Witt (University of Oxford); Philip Torr (University of Oxford); Philip Stier (University of Oxford)
(65) Integrating Flood Susceptibility and Deforestation Mapping for Climate Vulnerability Assessment: A Geospatial and AI-Based Approach. Serah Akojenu (Data Scientists Network); Chinazo Anebelundu (Data Scientists Network); Godwin Adegbehinde (Data Scientists Network); Olamide Shogbamu (Data Scientists Network); Blessing Agboola (Data Scientists Network); Tochuckwu Abia (Data Scientists Network); Anthony Soronnadi (Data Scientists Network)
(66) ML-IAM: Emulating Integrated Assessment Models With Machine Learning Yen Shin (KAIST); Haewon McJeon (KAIST); Changyoon Lee (KAIST); Eunsu Kim (KAIST); Junho Myung (KAIST); Kiwoong Park (KAIST); Jung-Hun Woo (Seoul National University); Min-Young Choi (Seoul National University); Bomi Kim (Seoul National University); Hyun W. Ka (KAIST); Alice Oh (KAIST)
(67) Helping mitigate climate change through efficient reinforcement learning-based wind farm flow control Elie KADOCHE (TotalEnergies); Pascal BIANCHI (Télécom Paris); Florence CARTON (TotalEnergies); Philippe CIBLAT (Télécom Paris); Damien ERNST (Montefiore Institute)
(68) Geospatial Chain of Thought Reasoning for Enhanced Visual Question Answering on Satellite Imagery Shambhavi Shanker (IIT Bombay); Manikandan Padmanaban (IBM Research India); Jagabondhu Hazra (IBM Research India)
(69) Causal Effects of Winter Wheat on Soil Organic Carbon Under Climate Variability Georgios Giannarakis (National Observatory of Athens); Vasileios Sitokonstantinou (University of Valencia); Dimitrios Bormpoudakis (National Observatory of Athens); Ilias Tsoumas (Wageningen University and Research); Nikiforos Samarinas (Aristotle University of Thessaloniki); Gustau Camps-Valls (University of Valencia); Charalmpos Kontoes (NATIONAL OBSERVATORY OF ATHENS)
(70) ADECEES: Anomaly DEtection of CO2 Emissions via Ensemble Segmentation Andrianirina Rakotoharisoa (Imperial College London); Simone Cenci (University College London); Rossella Arcucci (Imperial College London)
(71) EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting Hammed Akande (Concordia University); Abdulrauf Gidado (Algoma University)
(72) Climate Adaptation-Aware Flood Prediction for Coastal Cities Using Deep Learning Bilal Hassan (New York University Abu Dhabi); Areg Karapetyan (New York University Abu Dhabi); Aaron CH Chow (New York University Abu Dhabi); Samer Madanat (New York University Abu Dhabi)
(73) Camera-Trap Classification With Deep Learning Under Ground Truth Uncertainty Leonard Hockerts (University of Glasgow); Peter Stewart (University of Glasgow); Tiffany Vlaar (University of Glasgow)
(74) Reflexive Evidence-Based Multimodal Learning for Clean Energy Transitions: Causal Insights on Cooking Fuel Access, Urbanization, and Carbon Emissions Shan Shan (Zhejiang University)
(75) Identifying Spatial Patterns of Biases in Ocean Turbulence Parameterizations using Unsupervised Machine Learning Ratnaksha Lele (University of California San Diego)
(76) Using Time Series Foundation Models for Atmospheric CO2 Concentration Forecasting Kumar Saurav (IBM); Vinamra Baghel (IBM); Ayush Jain (IBM); Ranjini Guruprasad (IBM)
(77) Advancing Multimodal Fact-Checking Against Climate Misinformation: A Benchmark Dataset and Comparison of Lightweight Models Omar El Baf (IRD); Quentin Senatore (IRD); Amira Mouakher (Université de Perpignan); Laure Berti-Equille (IRD)
(78) Pathways to Sustainability: Carbon-Aware Routing for Global AI Data Transfers Nikolas Schmitz (RWTH Aachen University Hospital); Dayana Savostianova (RWTH Aachen University Hospital); Leon Niggemeier (RWTH Aachen University Hospital); Martin Strauch (RWTH Aachen University Hospital); Peter Boor (RWTH Aachen University Hospital)
(79) Robust Energy Storage Operation via Generative Wasserstein Distributionally Robust Optimization Han Xu (California Institute of Technology); Christopher Yeh (California Institute of Technology)
(80) ClimForGe: A Diffusion-based Forcing–Response Climate Emulator on Daily Timescales Jack Kai Lim (UC San Diego); Salva Rühling Cachay (UC San Diego); Duncan Watson-Parris (UC San Diego)
(81) Bridging the Temporal Gap: From Historical Monthly Invoices to Granular Hourly Energy Forecasting for Sustainable Operations Pratha Pawar (AWS); Alec Hewitt (AWS); William Schuerman (AWS); Seyma Gunes (AWS); Will Sorenson (AWS)
(82) Machine learning discovery of regional and social disparities in electric vehicle charging reliability with GPT-5 Yifan Liu (Georgia Institute of Technology); Lindsey Snyder (Georgia Institute of Technology); Omar Asensio (Georgia Institute of Technology)
(83) Enabling Machine Learning-Assisted Discovery of Polyamines for Solid-State CO₂ Capture A N M Nafiz Abeer (Texas A&M University); Junhe Chen (Georgia Institute of Technology); Alif Bin Abdul Qayyum (Texas A&M University); Zhihao Feng (Georgia Institute of Technology); Hyun-Myung Woo (Incheon National University); Seung Soon Jang (Georgia Institute of Technology); Byung-Jun Yoon (Texas A&M University)
(84) One Stone Three Birds: Three-Dimensional Implicit Neural Network for Compression and Continuous Representation of Multi-Altitude Climate Data Alif Bin Abdul Qayyum (Texas A&M University); Xihaier Luo (Brookhaven National Laboratory); Nathan Urban (Brookhaven National Laboratory); Xiaoning Qian (Texas A&M University); Byung-Jun Yoon (Texas A&M University)
(85) Bioacoustic Multi-Step Attention: Underwater Ecosystem Monitoring in Climate Change Context Amine Razig (Insitut Polytechnique de Paris); Youssef Soulaymani (Universite de Montreal); Loubna Benabbou (Universite du Quebec a Rimouski); Pierre Cauchy (Universite du Quebec a Rimouski)
(86) Downscaling climate projections to 1 km with single-image super resolution Petr Košťál (Czech Technical University in Prague); Pavel Kordík (Czech Technical University in Prague); Ondřej Podsztavek (Czech Technical University in Prague)
(87) Data-Driven Approach for Ship Emissions Prediction: A Case Study on the Saint Lawrence River Abdelhak EL AISSI (UQAR); Ismail Bourzak (Xpert Solutions Technologiques (XST)); Loubna Benabbou (Université du Québec à Rimouski (UQAR)); Abdelaziz BERRADO (Mohammadia School of Engineers (EMI))
(88) Probabilistic bias adjustment of seasonal predictions of Arctic Sea Ice Concentration Parsa Gooya (Environment and Climate Change Canada (ECCC)); Reinel Sospedra-Alfonso (Environment and Climate Change Canada (ECCC))
(89) Saliency-guided deployment-adaptive compression for wildlife camera traps Tianhong Xie (Harvey Mudd College); Justin Kay (MIT); Timm Haucke (MIT); Sara Beery (MIT)
(90) Coupling Agent-based Modeling and Life Cycle Assessment to Analyze Trade-offs in Resilient Energy Transitions Beichen Zhang (Lawrence Berkeley National Laboratory); Mohammed Tamim Zaki (Lawrence Berkeley National Laboratory); Hanna Breunig (Lawrence Berkeley National Laboratory); Newsha Ajami (Lawrence Berkeley National Laboratory)
(91) Seg the HAB: Language-Guided Geospatial Algae Bloom Reasoning and Segmentation Patterson Hsieh (UC San Diego); Chia-Jui Yeh (UC Berkeley); Mao-Chi He (UC Berkeley); Wen-Han Hsieh (UC Berkeley); Haw-Ting Hsieh (Berkeley)
(92) Causal Inference Framework for Ocean Microbial Community Responses to Warmer Temperature Minh Viet Tran (Helmholtz Munich, Ludwig-Maximilians-Universität München, Munich Center for Machine Learning); Christian L. Müller (Helmholtz Munich, Ludwig-Maximilians-Universität München, Munich Center for Machine Learning)
(93) Utilizing a Geospatial Foundation Model for Coastline Delineation in Small Sandy Islands Tishya Chhabra (MIT); Walter Zesk (Self Assembly Lab at MIT); Skylar Tibbits (Self Assembly Lab at MIT)
(94) Deep learning for short-range monsoon rainfall forecast using ground truth rainfall data Aastha Jain (Columbia University); Jatin Batra (Tata Institute of Fundamental Research); Apoorva Narula (Georgia Institute of Technology); Rajeevan Madhavan Nair (Atria University); Sandeep Juneja (Ashoka University)
(95) Mass Conservation on Rails – Rethinking Physics-Informed Learning of Ice Flow Vector Fields Kim Bente (The University of Sydney); Roman Marchant (The University of Technology Sydney); Fabio Ramos (NVIDIA)
(96) Exploring Variational Graph Autoencoders for Distribution Grid Data Generation Syed Zain Abbas (Technical University of Munich (TUM)); Ehimare Okoyomon (Technical University of Munich (TUM))
(97) AgriVolT: A Multi-Modal Temporal Vision Transformer for Climate-Informed Commodity Price Forecasting Sharanya Roy (Algoverse); Krisha Agarwal (Algoverse); Sahir Gupta (Algoverse); Anshul Patil (Algoverse); Ahan M R (Algoverse)
(98) Theory-Guided Deep Learning with AlphaEarth Embeddings for Flash Flood Prediction in Data-Scarce Regions Hassan Ashfaq (Ghulam Ishaq Khan Institute of Engineering Sciences and Technology); Muhammad Arsal (Ghulam Ishaq Khan Institute of Engineering Sciences and Technolo); Anas Ashfaq (Cornell University)

Proposals

Title Authors
(99) Historical Reconstruction and Future Projection of Land Surface Boundary Conditions Amirpasha Mozaffari (Barcelona Supercomputing Center); Marina Castaño (Barcelona Supercomputing Center); Stefano Materia (Barcelona Supercomputing Center); Amanda Duarte (Barcelona Supercomputing Center)
(100) Multi-Resolution Analysis of the Convective Structure of Tropical Cyclones for Short-Term Intensity Guidance Elizabeth Cucuzzella (Carnegie Mellon University); Ann B. Lee (Carnegie Mellon University); Tria McNeely (Carnegie Mellon University); Kimberly Wood (The University of Arizona)
(101) Bayesian Methods for Enhanced Greenhouse Gas Emissions Inventories Michael Pekala (JHU/APL); Michael Pekala (JHU/APL)
(102) A modular framework to run AI-based models from high-resolution climate projections Aina Gaya-Àvila (Barcelona Supercomputing Center); Amirpasha Mozaffari (Barcelona Supercomputing Center); Amanda Duarte (Barcelona Supercomputing Center); Oriol Tintó Prims (Barcelona Supercomputing Center)
(103) Machine Learning Prediction of Soil Organic Carbon in Southeast Asia: Methods and Climate Implications Tram Tran (Denison University)
(104) CC-GRMAS: A Multi-Agent Graph Neural System for Spatiotemporal Landslide Risk Assessment in High Mountain Asia Mihir Panchal (Dwarakadas Jivanlal Sanghvi College of Engineering); Ying-Jung Chen (Georgia Institute of Technology); Surya Parkash (National Institute of Disaster Management)
(105) From Sparse to Representative: Machine Learning to Densify IAM Scenario Ensembles for Policy Insight Georgia Ray (Imperial College London)
(106) Using Machine Learning to improve the representation of Phytoplankton dynamics in Earth System Models Sandupal Dutta (Johns Hopkins University); Anand Gnanadesikan (Johns Hopkins University)
(107) Graphs for Scalable Building Decarbonisation: A Transferable Approach to HVAC Control Anaïs Berkes (University of Cambridge); Donna Vakalis (Mila); David Rolnick (Mila); Yoshua Bengio (Mila)
(108) Low-Power Weakly-Supervised Audio Detection for Real-World Mosquito Surveillance Danika Gupta (The Harker Upper School); Ming Zhao (Arizona State University); Neha Rajendra Vadnere (Arizona State University)
(109) Scalable Country-Level Crop Yield Modeling for Food Security and Risk Mitigation Andrew Hobbs (University of San Francisco); Jesse Anttila-Hughes (University of San Francisco)
(110) Extracting Structured Policy Information from Climate Action Plans Tom Corringham (Scripps Institution of Oceanography); Nupoor Gandhi (Carnegie Mellon); Bryan Flores (Independent Researcher); Emma Strubell (Carnegie Mellon); Sireesh Gururaja (Carnegie Mellon); Tristan Romanov (Independent Researcher); Jacob Dunafon (Independent Researcher)
(111) GeoWaste: Leveraging GIS and Machine Learning for Urban Waste Management in African Cities Bakumor Yolo (University of Calabar)
(112) Empowering our Critters: Running Energy Efficient Deep Learning Models for On-Edge Bioacoustic Monitoring Paritosh Borkar (Rochester Institute of Technology); Rishabh Malviya (Indian Institute of Technology, Bombay); Jayant Sachdev (Cornell University)
(113) Tracking the spread of climate change skepticism on X with simulations and deep learning Uwaila Ekhator (Boise State University); Mason Youngblood (Institute for Advanced Computational Science, Stony Brook University); Vicken Hillis (Boise State University)
(114) Mapping small-scale irrigation for climate adaptation Anna Boser (UCSB); Jackson Coldiron (UCSB); Karena Lai (UCSB); Madhav Rao (UCSB); Jasper Luo (UCSB); Kathy Baylis (UCSB); Tamma Carleton (UC Berkeley); Kelly Caylor (UCSB)
(115) Accelerating Security-Constrained Optimal Power Flow with Graph Neural Networks for Renewable Energy Integration Zhenhua Zhang (UC San Diego)
(116) Prioritization Learning for Equitable Residential Decarbonization Investments Eva Geierstanger (Stanford University)
(117) AI Agents For Decision-Making in Climate Governance Using Policy Benchmarks Shan Shan (Zhejiang University)

Tutorials

Title Authors
(118) AI‑Powered Measurement & Verification: Building Interpretable Counterfactual Models to Verify Energy Savings in Buildings Benedetto Grillone (Ento)
(119) Agricultural Monitoring with Fields of The World (FTW) Hannah Kerner (Arizona State University); Caleb Robinson (Microsoft); Isaac Corley (Wherobots); Matthias Mohr (Taylor Geospatial Engine); Gedeon Muhawenayo (Arizona State University); Ivan Zvonkov (University of Maryland); Tristan Grupp (World Resources Institute); Nathan Jacobs (Washington University St. Louis)
(120) Flood mapping with optical and microwave satellite data: from indices to machine learning Pratyush Tripathy (University of California, Santa Barbara)
(121) PiggyCast - Improving Weather Prediction Accuracy through a Stacking-Based Ensemble AI Approach. Josiah Kimani (African Institute for Mathematical Sciences (AIMS) - South Africa)); Oliver Angélil (Ishango.ai); Chris Toumping (Inshango.ai); Steffen Knoblauch (University of Heidelberg)
(122) Climate Policy Radar's Open Knowledge Graph Kalyan Dutia (Climate Policy Radar); Anne Sietsma (Climate Policy Radar); Julie Saigusa (Climate Policy Radar); Harrison Pim (Climate Policy Radar)
(123) Question Answering over Sustainability Reports: Information Richness and Answer Quality Tobias Schimanski (University of Zurich)

Call for Submissions

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

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

The theme of this workshop, “Roots to Routes: A Dialogue on Different Machine Learning Methods for Climate Impact,” invites submissions that explore the strengths of diverse machine learning approaches in climate-related contexts. We particularly encourage work that demonstrates the effectiveness of classical ML methods under real-world constraints, such as limited data availability, privacy concerns, or restricted computational resources. At the same time, we welcome contributions that showcase how scaling up data and computing resources combined with modern tools and techniques can unlock new possibilities for tackling global-scale climate prediction challenges. Our goal is to foster a rich and constructive dialogue around when and where small- or large-scale models are most impactful.

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, proposals, and tutorial 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.

Besides adhering to the NeurIPS 2025 LLM Policy on the Use of Large Language Models, this workshop requires that no CCAI website materials, including its calls for papers, should be shared with LLMs in whole or in part during the paper-writing process. Please keep in mind that the CCAI content mentioned above is copyrighted material.

Please see the Tips for Submissions and FAQ, and contact climatechangeai.neurips2025@gmail.com with questions.

The NeurIPS conference Financial Aid and Volunteer applications will be available on the NeurIPS website in late August or early September.

Submission Tracks

There are three tracks for submissions: (i) Papers, (ii) Proposals, (iii) Tutorials. Submissions are limited to four pages for the Papers track, and three pages for the Proposals track, in PDF format (see examples from previous workshops here). References do not count towards this total. Supplementary appendices are allowed but will be read at the discretion of the reviewers. Tutorial submissions are in executable notebook format that follows CCAI’s NeurIPS 2025 Tutorial Template. 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 welcome. Datasets should be properly documented with regards to their provenance and contents and designed to permit machine learning research (e.g. formatted with clear benchmarks for evaluation).

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, a discussion of why current methods are inadequate, an explanation of the proposed method, and a 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 demonstrate the use of machine learning methods and tools (e.g., libraries, packages, services, datasets, or frameworks) to address climate-relevant challenges.

Tutorial submissions should include a clear and concise description of user learning outcomes. Tutorial submissions will be reviewed based on their potential impact, pedagogical value, and usability by the climate and AI research community. Submissions of tutorials featuring AI-for-climate applications not yet featured in the CCAI Tutorials repository, are encouraged. Please review our list of CCAI tutorials.

Notebooks will undergo two review cycles to ensure high quality submissions. In the first round, Round I, reviews will be made upon the initial (80% complete) submission (due August 20, 2025) and the second round, Round II, of reviews will be made upon the complete (100%) submission (due October 6, 2025) which should address and revise content based on feedback from Round I. Please see CCAI’s NeurIPS 2025 Tutorials guidelines for more details.

Tutorials should be in the form of executable notebooks that follow CCAI’s NeurIPS 2025 Tutorial Template. Authors must submit the notebook along with an accompanying requirements.txt file to ensure users and reviewers are able to run the tutorial in a self-contained runnable environment both locally and remotely (e.g., Python virtual environments, Colab).

We expect tutorials to be 80% complete by the Round I deadline in order for our reviewers to provide adequate feedback and select tutorials for spotlight presentations.

Notebook submissions will be assessed based on clarity, accessibility, and code quality. We ask that authors emphasize real-world impacts of their ML models by answering questions such as Who will be using the models/outputs, and how will they be used? What decisions will be made based on these models? How will this impact existing systems/the environment/affected communities on the ground? For more information, please review our tutorial development guidelines. For questions with respect to tutorials, please email tutorials@climatechange.ai.

Tips for Submissions

Addressing Impact

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

Q&A

If you have further questions on how to participate in the workshop, you can also contact us via email at climatechangeai.neurips2025@gmail.com. For recordings of informational webinars of previous editions of our workshop, please see here.

Organizers

Hari Prasanna Das (Amazon)
Raluca Stevenson (Microsoft Research)
Joaquín Salas (Instituto Politecnico Nacional Mexico)
Salva Rühling Cachay (UC San Diego)
Nadia Ahmed (UC Irvine)
Yoshua Bengio (Mila, Université de Montréal)

Tutorials Track Organizers

Isabelle Tingzon (The World Bank/GFDRR)
Nadia Ahmed (UC Irvine)

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 (August 1 - August 20) as they prepare submissions for this workshop.

Examples of mentor-mentee interactions may include:

Please keep in mind that mentors are not expected to write code for mentees.

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 August 20th.

Tips for submissions may prove useful in guiding feedback on the mentee’s project.

We suggest that after the mentor-mentee matching is made, a first (physical or digital) meeting should take place within the first week (August 1 - August 8) to discuss the Paper or Proposal and set expectations for the mentorship period. The mentee is recommended to come to this meeting with an agenda of specific questions or requests for feedback, including the level of feedback (low- or high- level).

In particular, during the first meeting, mentees and mentors should

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 August 20.

Mentees should continue coming to meetings prepared with an agenda, in order to ensure that mentor-mentee interactions are as productive as possible.

Mentors should not do work for the mentee, but should instead offer concrete guidance. For instance, mentors might point mentees to references about relevant climate change domains or machine learning techniques, or explain why a proposed method or application might be incomplete.

Depending on the extent of interactions between the mentor and mentee, at the end of the mentorship program, a mentee can choose to invite the mentor as a co-author to their submission. The mentor should not initiate this conversation, nor should they expect to be given authorship. We do not expect mentors or mentees to feel obligated to maintain the mentor-mentee relationship after the end of the mentorship program. We nevertheless hope that this program is also able to cultivate meaningful relationships between mentors and mentees that continue beyond the workshop.

Mentors and mentees must abide by the Climate Change AI Code of Conduct. If at any point, there are complications or complex circumstances that might impact the mentorship duration, please raise these issues with the workshop organizing committee by emailing climatechangeai.neurips2025@gmail.com.

Application

Applications are due by July 28, 2025.

Sponsors

Bronze Sponsors

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.neurips2025@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 the field of 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.neurips2025@gmail.com with any questions.

Q: Can I submit work to this workshop if I am also submitting to another NeurIPS 2025 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.

Acknowledgement

The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.