NeurIPS 2025 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 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

All Tracks camera-ready materials due: November 14, 2025, 23:59 AoE
Workshop: December 7, 2025

About the Workshop

The workshop will take place on December 7 in Upper Level Room 24ABC, San Diego Convention Center from 8:00a PST to 5:00p PST. Please find the preliminary schedule below.

Schedule

Time (Workshop)	Time (Local)	Event
		Opening Remarks Details: (click to expand) Welcome and opening remarks
		Keynote Talk - Dr. Bichlien Nguyen Details: (click to expand) Title: Tackling Climate Change from Materials to Systems Abstract: This presentation examines the pressing challenge of reducing carbon emissions within the built environment. It uses case studies to illustrate how artificial intelligence and machine learning can support the discovery of innovative materials. The discussion also addresses the current limitations of AI in materials design, especially when considering the broader context of scaling new technologies, and underscores the importance of advancing other AI-driven approaches to achieve meaningful impact. Bio: As a principal researcher at Microsoft Research with a PhD in chemistry, Dr. Bichlien Nguyen works at the intersection of chemistry, biology, computer science, and engineering to advance sustainable technologies. Her research leverages ML and AI to drive the inverse design of sustainable materials for cloud applications, support the clean energy transition, and promote the green carbon economy. In addition to scientific pursuits, She strongly believes that innovation is fueled by people and their experiences, and actively champions engagement between the scientific community and policy stakeholders.
		Spotlight talks
		(58) "Saving Wildlife with Generative AI: Latent Composite Flow Matching for Poaching Prediction"
		(89) "Saliency-guided deployment-adaptive compression for wildlife camera traps"
		(23) "Newfoundland Marine Refuge Fish Classification Dataset (N-MARINE)"
		(67) "Helping mitigate climate change through efficient reinforcement learning-based wind farm flow control"
		(60) "Scalable & explainable ML for wildfire risk modeling in Southern Europe: A case-study in Portugal"
		(118) "AI‑Powered Measurement & Verification: Building Interpretable Counterfactual Models to Verify Energy Savings in Buildings"
		(111) "GeoWaste: Leveraging GIS and Machine Learning for Urban Waste Management in African Cities"
		Panel 1 - Methodological Trade-offs in ML for climate change Details: (click to expand) Panelists: Lauren Harrell (Data Scientist & Researcher, Google Research), Cristian Bodnar (Co-founder & Chief Scientist, Silurian AI), Kelsey Doerksen (Postdoctoral Researcher, Arizona State University) Topic: The exponential growth in model complexity is opening up important and broad points of discussion, related, among other things, to performance, carbon footprint, training and inference time, explainability, operational costs, and ethical concerns. In this panel we will hear our experts opinions on the trade-offs of the development and deployment of small and large models, and on the implications of their use for solving problems related to climate change, biodiversity, and population impacts.
		Keynote Talk - Dr. Duncan Watson-Parris Details: (click to expand) Title: Roots and Routes to Robust Climate Projections: Balancing Physical Constraints with Deep Learning Abstract: Uncertainties in Earth’s future climate are driven largely by the complex, microscopic interactions between aerosols and clouds, which are computationally expensive to simulate. In this talk, I will explore the "roots" of this challenge by demonstrating a hybrid framework that anchors detailed physical process models with "top-down" observations of historical warming and the Earth's energy budget. This combination allows us to rule out implausible climate futures and significantly narrow projection uncertainty. I will then discuss the "routes" offered by machine learning emulation to accelerate this science, examining the critical trade-offs between traditional pattern scaling and frontier deep learning. Drawing on recent benchmarking work, I will show how the chaotic noise of natural climate variability can confound complex deep learning models, often making simpler, linear methods more robust for predicting temperature. Ultimately, I argue that while AI offers powerful new routes for discovery, we must rigorously benchmark these tools against physical roots to distinguish the climate signal from the noise. Bio: Duncan Watson-Parris leads the Climate Analytics Lab (CAL) at UC San Diego, which investigates how aerosols interact with clouds and how these processes are represented in global climate models. His work combines theory, observations, and machine learning to better constrain and improve these models. CAL develops AI-based methods to integrate diverse satellite and aircraft observations and reduce uncertainty in aerosol–cloud interactions. He also leads the GAIA Initiative at UCSD, advancing the use of artificial intelligence for Earth and ocean science more broadly. His research is supported by an NSF CAREER award, a Google Academic Research Award, and DARPA.
		Lunch break and Poster Session I (In person with Live Tours, and Virtual) Details: (click to expand) Posters presented in this slot are listed below. Papers track: (1) Uncertainty-Aware Prediction of Climate Extremes Using Fine-Tuned Time-Series Foundation Models (2) Fewer Shots, Better Implosions: Sample-Efficient Optimization for Inertial Confinement Fusion (3) Discovering EV Charging Site Archetypes Through Few Shot Forecasting: The First U.S.-Wide Study (4) Bugs in Citizen-Science Data: Robust Biodiversity AI Begins with Clean Images (5) Efficient Reinforcement Learning Implementations for Sustainable Operation of Liquid Cooled HPC Data Centers (6) AI-based Waste Mapping for Addressing Climate-Exacerbated Flood Risk (9) AI-driven Grid Optimization Can Reduce Emissions (12) Mapping Farmed Landscapes from Remote Sensing (15) Differentially Private Federated Learning for High-Accuracy Carbon Footprint Prediction that Protects Sensitive Industrial Data (23) Newfoundland Marine Refuge Fish Classification Dataset (N-MARINE) (24) Ensembles of Neural Surrogates for Parametric Sensitivity in Ocean Modeling (26) AI-Driven Temporal Super-Resolution for Flooding Prediction in Norfolk, Virginia (30) Learning in Stackelberg Markov Games (31) Probability calibration for precipitation nowcasting (35) Smallholder Agricultural Landscape Understanding (39) Training-Free Data Assimilation with GenCast (40) EcoEval: A Benchmark for Evaluating Large Language Model Handling of Climate Change Misinformation, False Beliefs, and Climate Policy Sentiment (41) SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation (42) DemandCast: Global hourly electricity demand forecasting (43) Physics-Informed Machine Learning Model for In-situ Life-Cycle Prediction of Condensation Trails (44) Understanding Ice Crystal Habit Diversity with Self-Supervised Learning (47) Early Reforestation Detection in Kenya Using Multi-Temporal Analysis (50) Detection and Simulation of Urban Heat Islands Using a Fine-Tuned Geospatial Foundation Model for Microclimate Impact Prediction (53) Ecosystem Insights through Extreme Values: A Fresh Look at Meteorological Drivers (54) Satellite-Based Estimation of Soil Geologic Properties Using Physics-Guided Machine Learning (58) Saving Wildlife with Generative AI: Latent Composite Flow Matching for Poaching Prediction (64) Sensitivity Analysis for Climate Science with Generative Flow Models (66) ML-IAM: Emulating Integrated Assessment Models With Machine Learning (67) Helping mitigate climate change through efficient reinforcement learning-based wind farm flow control (73) Camera-Trap Classification With Deep Learning Under Ground Truth Uncertainty (79) Robust Energy Storage Operation via Generative Wasserstein Distributionally Robust Optimization (80) ClimForGe: A Diffusion-based Forcing–Response Climate Emulator on Daily Timescales (81) Bridging the Temporal Gap: From Historical Monthly Invoices to Granular Hourly Energy Forecasting for Sustainable Operations (82) Machine learning discovery of regional and social disparities in electric vehicle charging reliability with GPT-5 (83) Enabling Machine Learning-Assisted Discovery of Polyamines for Solid-State CO₂ Capture (92) Causal Inference Framework for Ocean Microbial Community Responses to Warmer Temperature (93) Utilizing a Geospatial Foundation Model for Coastline Delineation in Small Sandy Islands (95) Mass Conservation on Rails – Rethinking Physics-Informed Learning of Ice Flow Vector Fields (97) AgriVolT: A Multi-Modal Temporal Vision Transformer for Climate-Informed Commodity Price Forecasting Proposals track: (99) Historical Reconstruction and Future Projection of Land Surface Boundary Conditions (100) Multi-Resolution Analysis of the Convective Structure of Tropical Cyclones for Short-Term Intensity Guidance (103) Machine Learning Prediction of Soil Organic Carbon in Southeast Asia: Methods and Climate Implications (104) CC-GRMAS: A Multi-Agent Graph Neural System for Spatiotemporal Landslide Risk Assessment in High Mountain Asia (106) Using Machine Learning to improve the representation of Phytoplankton dynamics in Earth System Models (109) Scalable Country-Level Crop Yield Modeling for Food Security and Risk Mitigation (112) Empowering our Critters: Running Energy Efficient Deep Learning Models for On-Edge Bioacoustic Monitoring (114) Mapping small-scale irrigation for climate adaptation (115) Accelerating Security-Constrained Optimal Power Flow with Graph Neural Networks for Renewable Energy Integration (116) Prioritization Learning for Equitable Residential Decarbonization Investments (117) AI Agents For Decision-Making in Climate Governance Using Policy Benchmarks Tutorials track: (119) Agricultural Monitoring with Fields of The World (FTW)
		Spotlight talks
		(51) "Coupled Climate Simulations with ACE and Samudra"
		(121) "PiggyCast - Improving Weather Prediction Accuracy through a Stacking-Based Ensemble AI Approach."
		(63) "Global 3D Reconstruction of Clouds & Tropical Cyclones"
		(17) "MEQNet: Deep Learning for Methane Point Source Emission Quantification from Sentinel-2 Observations"
		(119) "Agricultural Monitoring with Fields of The World (FTW)"
		(72) "Climate Adaptation-Aware Flood Prediction for Coastal Cities Using Deep Learning"
		Panel 2 - Nurturing Collaboration Details: (click to expand) Panelists: Kalai Ramea (Founder, Planette), Steffen Knoblauch (Postdoctoral Researcher, Heidelberg Institute for Geoinformation Technology), Priscilla Yāhuitzin Baltezar (Remote Sensing & GIS Analyst, Massachussetts Institute of Technology) Topic: Forging collaborations among domain specialists, machine learning practitioners, indigenous and local communities, and citizen scientists is crucial for project development and successful execution. In this panel, we explore applications of machine learning to climate change-related work, including helpful domain questions that remain unsolved and pathways to solutions towards solving them with the right team and strategy.
		Awards Ceremony
		Poster session II (In person with Live Tours, and Virtual) Details: (click to expand) Posters presented in this slot are listed below. Papers track: (8) Holistic Sustainability for Geo-Distributed Data Centers using Hierarchical Optimization (10) Spatial Uncertainty Quantification in Wildfire Forecasting for Climate-Resilient Emergency Planning (11) Pure and Physics-encoded Spatiotemporal Deep Learning for Climate-Vegetation Dynamics (13) Spatio-Temporal Modelling of Rainfall via Frame-Level Autoregression (14) TC-GTN: Temporal Convolution Graph Transformer Network for Hydrological Forecasting (22) Can Artificial Intelligence Global Weather Forecasting Models Capture Extreme Events? A Case Study of the 2022 Pakistan Floods (27) Quantifying Climate Policy Action and Its Links to Development Outcomes: A Cross-National Data-Driven Analysis (29) CuMoLoS-MAE: A Masked Autoencoder for Remote Sensing Data Reconstruction (32) Sparse Local Implicit Image Function for sub-km Weather Downscaling (33) Machine Learning and Multi-source Remote Sensing in Forest Aboveground Biomass Estimation: A Review (34) Probabilistic modelling for methane leak detection in gas distribution networks (36) Facade Segmentation for Solar Photovoltaic Suitability (38) Spectral Channel Attention Network: A Method for Hyperspectral Semantic Segmentation of Cloud and Shadows (46) Emulating Climate Across Scales with Conditional Spherical Fourier Neural Operators (48) Inverse Modeling of Laser Pulse Shapes in Inertial Confinement Fusion with Auto-Regressive Models (51) Coupled Climate Simulations with ACE and Samudra (55) Operator Learning for Power Systems Simulation (56) Scalable Geospatial Data Generation Using AlphaEarth Foundations Model (57) Swift: An Autoregressive Consistency Model for Efficient Weather Forecasting (59) Generative AI for weather data assimilation (61) A Graph Neural Network Approach for Localized and High-Resolution Temperature Forecasting (63) Global 3D Reconstruction of Clouds & Tropical Cyclones (68) Geospatial Chain of Thought Reasoning for Enhanced Visual Question Answering on Satellite Imagery (69) Causal Effects of Winter Wheat on Soil Organic Carbon Under Climate Variability (70) ADECEES: Anomaly DEtection of CO2 Emissions via Ensemble Segmentation (72) Climate Adaptation-Aware Flood Prediction for Coastal Cities Using Deep Learning (75) Identifying Spatial Patterns of Biases in Ocean Turbulence Parameterizations using Unsupervised Machine Learning (78) Pathways to Sustainability: Carbon-Aware Routing for Global AI Data Transfers (84) One Stone Three Birds: Three-Dimensional Implicit Neural Network for Compression and Continuous Representation of Multi-Altitude Climate Data (86) Downscaling climate projections to 1 km with single-image super resolution (87) Data-Driven Approach for Ship Emissions Prediction: A Case Study on the Saint Lawrence River (89) Saliency-guided deployment-adaptive compression for wildlife camera traps (90) Coupling Agent-based Modeling and Life Cycle Assessment to Analyze Trade-offs in Resilient Energy Transitions (91) Seg the HAB: Language-Guided Geospatial Algae Bloom Reasoning and Segmentation (94) Deep learning for short-range monsoon rainfall forecast using ground truth rainfall data (98) Theory-Guided Deep Learning with AlphaEarth Embeddings for Flash Flood Prediction in Data-Scarce Regions Proposals track: (101) Bayesian Methods for Enhanced Greenhouse Gas Emissions Inventories (102) A modular framework to run AI-based models from high-resolution climate projections (107) Graphs for Scalable Building Decarbonisation: A Transferable Approach to HVAC Control (110) Extracting Structured Policy Information from Climate Action Plans Tutorials track: (120) Flood mapping with optical and microwave satellite data: from indices to machine learning (121) PiggyCast - Improving Weather Prediction Accuracy through a Stacking-Based Ensemble AI Approach. (122) Climate Policy Radar's Open Knowledge Graph

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

Dr. Priscilla Yāhuitzin Baltezar

Remote Sensing & GIS Analyst

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	Poster Session (registration required)
(1) Uncertainty-Aware Prediction of Climate Extremes Using Fine-Tuned Time-Series Foundation Models	Imran Nasim (IBM); Joao Lucas De Sousa Almeida (IBM)	Session 1 View Poster
(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)	Session 1 View Poster
(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)	Session 1 View Poster
(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)	Session 1 View Poster
(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)	Session 1 View Poster
(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)	Session 1 View Poster
(7) A Novel Integrated ML Approach Utilizing Radar & Satellite Imagery for Selective Logging Detection	Saraswathy Amjith (MIT); Joshua Fan (Cornell University)	Virtual View Poster
(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)	Session 2 View Poster
(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)	Session 1 View Poster
(10) Spatial Uncertainty Quantification in Wildfire Forecasting for Climate-Resilient Emergency Planning	Aditya Chakravarty (Texas A&M University)	Session 2 View Poster
(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)	Session 2 View Poster
(12) Mapping Farmed Landscapes from Remote Sensing	Michelangelo Conserva (Google); Alex WIlson (Google); Charlotte Stanton (Google); Vishal Batchu (Google); Varun Gulshan (Google)	Session 1 View Poster
(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)	Session 2 View Poster
(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)	Session 2 View Poster
(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)	Session 1 View Poster
(16) Image2image Tropical Cyclone wind field diagnosis with Pix2Pix generative adversarial networks (GANs)	Sarah Ollier (Worldsphere)	Virtual View Poster
(17) MEQNet: Deep Learning for Methane Point Source Emission Quantification from Sentinel-2 Observations Overall Best Paper	Di Xu (Imperial College London); Philippa Mason (Imperial College London); Jianguo Liu (Imperial College London); Yanghua Wang (Imperial College London)	Virtual View Poster
(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)	Virtual View Poster
(19) Adaptive Learning in Spatial Agent-Based Models for Climate Risk Assessment: A Geospatial Framework with Evolutionary Economic Agents	Yara Mohajerani (Pluripotent Technologies Inc)	Virtual View Poster
(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)	Virtual View Poster
(21) Foundation Models for Mapping Emission Sources and Acute Respiratory Infection (ARI) Hotspots	Usman Nazir (University of Oxford); Sara Khalid (University of Oxford)	Virtual View Poster
(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)	Session 2 View Poster
(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)	Session 1 View Poster
(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)	Session 1 View Poster
(25) Revisiting Deep AC-OPF	Oluwatomisin Dada (University of Cambridge)	Virtual View Poster
(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)	Session 1 View Poster
(27) Quantifying Climate Policy Action and Its Links to Development Outcomes: A Cross-National Data-Driven Analysis	Aditi Dutta (University of Exeter)	Session 2 View Poster
(28) Towards Energy-Efficient Buildings: A Hybrid Approach for Chiller Fault Detection	Timothy Mulumba (NYU); Rita Sousa (NYU)	Virtual View Poster
(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)	Session 2 View Poster
(30) Learning in Stackelberg Markov Games	Jun He (Purdue University); Andrew Liu (Purdue University); Yihsu Chen (University of California, Santa Cruz)	Session 1 View Poster
(31) Probability calibration for precipitation nowcasting	Lauri Kurki (Vaisala); Yaniel Cabrera (Vaisala); Samu Karanko (Vaisala)	Session 1 View Poster
(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)	Session 2 View Poster
(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)	Session 2 View Poster
(34) Probabilistic modelling for methane leak detection in gas distribution networks	Katherine Green (Guidehouse); Rubab Atwal (Guidehouse)	Session 2 View Poster
(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)	Session 1 View Poster
(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)	Session 2 View Poster
(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)	Virtual View Poster
(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)	Session 2 View Poster
(39) Training-Free Data Assimilation with GenCast	Thomas Savary (ENS Paris-Saclay); François Rozet (University of Liège); Gilles Louppe (University of Liège)	Session 1 View Poster
(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)	Session 1 View Poster
(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)	Session 1 View Poster
(42) DemandCast: Global hourly electricity demand forecasting	Kevin Steijn (Open Energy Transition); Vamsi Priya Goli (Open Energy Transition); Enrico Antonini (Open Energy Transition)	Session 1 View Poster
(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)	Session 1 View Poster
(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)	Session 1 View Poster
(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)	Virtual View Poster
(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)	Session 2 View Poster
(47) Early Reforestation Detection in Kenya Using Multi-Temporal Analysis	Angela John (Saarland University)	Session 1 View Poster
(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)	Session 2 View Poster
(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)	Virtual View Poster
(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)	Session 1 View Poster
(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)	Session 2 View Poster
(52) Machine Learning Approaches to Identifying Tropical Waves That Develop into Hurricanes	Haochang Luo (City College of New York)	Virtual View Poster
(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)	Session 1 View Poster
(54) Satellite-Based Estimation of Soil Geologic Properties Using Physics-Guided Machine Learning	Hrusikesha Pradhan (NASA Jet Propulsion Laboratory, California Institute of Technology)	Session 1 View Poster
(55) Operator Learning for Power Systems Simulation	Matthew Schlegel (University of Calgary); Matthew Taylor (University of Alberta); Mostafa Farrokhabadi (University of Calgary)	Session 2 View Poster
(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)	Session 2 View Poster
(57) Swift: An Autoregressive Consistency Model for Efficient Weather Forecasting	Jason Stock (Argonne National Laboratory); Troy Arcomano (AI2, ANL); Rao Kotamarthi (Argonne National Laboratory)	Session 2 View Poster
(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)	Session 1 View Poster
(59) Generative AI for weather data assimilation	Ruizhe Huang (MIT); Qidong Yang (MIT); Jonathan Giezendanner (MIT); Sherrie Wang (MIT)	Session 2 View Poster
(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)	Virtual View Poster
(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)	Session 2 View Poster
(62) Towards a Climate Counterfactual Autoencoder	Frieder Loer (Institute for Meteorology, Leipzig University); Sebastian Sippel (Institute for Meteorology, Leipzig University)	Virtual View Poster
(63) Global 3D Reconstruction of Clouds & Tropical Cyclones Best ML Innovation	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)	Session 2 View Poster
(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)	Session 1 View Poster
(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)	Virtual View Poster
(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)	Session 1 View Poster
(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)	Session 1 View Poster
(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)	Session 2 View Poster
(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)	Session 2 View Poster
(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)	Session 2 View Poster
(71) EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting	Hammed Akande (Concordia University); Abdulrauf Gidado (Algoma University)	Virtual View Poster
(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)	Session 2 View Poster
(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)	Session 1 View Poster
(74) Reflexive Evidence-Based Multimodal Learning for Clean Energy Transitions: Causal Insights on Cooking Fuel Access, Urbanization, and Carbon Emissions	Shan Shan (Zhejiang University)	Virtual View Poster
(75) Identifying Spatial Patterns of Biases in Ocean Turbulence Parameterizations using Unsupervised Machine Learning	Ratnaksha Lele (University of California San Diego)	Session 2 View Poster
(76) Using Time Series Foundation Models for Atmospheric CO2 Concentration Forecasting	Kumar Saurav (IBM); Vinamra Baghel (IBM); Ayush Jain (IBM); Ranjini Guruprasad (IBM)	Virtual View Poster
(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)	Virtual View Poster
(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)	Session 2 View Poster
(79) Robust Energy Storage Operation via Generative Wasserstein Distributionally Robust Optimization	Han Xu (California Institute of Technology); Christopher Yeh (California Institute of Technology)	Session 1 View Poster
(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)	Session 1 View Poster
(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)	Session 1 View Poster
(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)	Session 1 View Poster
(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)	Session 1 View Poster
(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)	Session 2 View Poster
(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)	Virtual View Poster
(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)	Session 2 View Poster
(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))	Session 2 View Poster
(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))	Virtual View Poster
(89) Saliency-guided deployment-adaptive compression for wildlife camera traps	Tianhong Xie (Harvey Mudd College); Justin Kay (MIT); Timm Haucke (MIT); Sara Beery (MIT)	Session 2 View Poster
(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)	Session 2 View Poster
(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)	Session 2 View Poster
(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)	Session 1 View Poster
(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)	Session 1 View Poster
(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)	Session 2 View Poster
(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)	Session 1 View Poster
(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))	Virtual View Poster
(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)	Session 1 View Poster
(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)	Session 2 View Poster

Proposals

Title	Authors	Poster Session (registration required)
(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)	Session 1 View Poster
(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)	Session 1 View Poster
(101) Bayesian Methods for Enhanced Greenhouse Gas Emissions Inventories	Michael Pekala (JHU/APL); Michael Pekala (JHU/APL)	Session 2 View Poster
(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)	Session 2 View Poster
(103) Machine Learning Prediction of Soil Organic Carbon in Southeast Asia: Methods and Climate Implications	Tram Tran (Denison University)	Session 1 View Poster
(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)	Session 1 View Poster
(105) From Sparse to Representative: Machine Learning to Densify IAM Scenario Ensembles for Policy Insight	Georgia Ray (Imperial College London)	Virtual View Poster
(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)	Session 1 View Poster
(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)	Session 2 View Poster
(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)	Virtual View Poster
(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)	Session 1 View Poster
(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)	Session 2 View Poster
(111) GeoWaste: Leveraging GIS and Machine Learning for Urban Waste Management in African Cities Best Pathway to Impact	Bakumor Yolo (University of Calabar)	Virtual View Poster
(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)	Session 1 View Poster
(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)	Virtual View Poster
(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)	Session 1 View Poster
(115) Accelerating Security-Constrained Optimal Power Flow with Graph Neural Networks for Renewable Energy Integration	Zhenhua Zhang (UC San Diego)	Session 1 View Poster
(116) Prioritization Learning for Equitable Residential Decarbonization Investments	Eva Geierstanger (Stanford University)	Session 1 View Poster
(117) AI Agents For Decision-Making in Climate Governance Using Policy Benchmarks	Shan Shan (Zhejiang University)	Session 1 View Poster

Tutorials

Title	Authors	Poster Session (registration required)
(118) AI‑Powered Measurement & Verification: Building Interpretable Counterfactual Models to Verify Energy Savings in Buildings	Benedetto Grillone (Ento)	Virtual View Poster
(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)	Session 1 View Poster
(120) Flood mapping with optical and microwave satellite data: from indices to machine learning	Pratyush Tripathy (University of California, Santa Barbara)	Session 2 View Poster
(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)	Session 2 View Poster
(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)	Session 2 View Poster
(123) Question Answering over Sustainability Reports: Information Richness and Answer Quality	Tobias Schimanski (University of Zurich)	Virtual View Poster

Virtual Poster Session and Live Tours of In-person Poster Presentations

Some posters will be presented virtually using Zoom platform.

Additionally, to facilitate interaction between virtual audience of the workshop, and the in-person poster presentors, we are organizing Live Poster Tours of the physical poster hall.

During these tours, a volunteer “Poster Tour Guide” will move through the physical poster area with a smartphone or tablet and a handheld microphone. The guide will introduce each poster, facilitate a short pitch by the presenter, and moderate a live Q&A with virtual attendees. There will be dedicated Zoom breakout rooms in the virtual Zoom link for these live tours. We advise virtual attendees to have the corresponding poster PDFs from our website handy when participating in the live poster tours.

Zoom Link for Poster Sessions

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.

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)