This $191,868 National Science Foundation project grant supports research to understand surface-to-bed meltwater pathways across the Greenland Ice Sheet. Funded through the Polar Programs grant program (CFDA 47.078), the award will utilize machine learning and physics-based models to map cracks and draining lakes on the ice sheet from satellite imagery. Researchers at the University of Kansas Center for Research will develop open-source artificial intelligence codes to automatically detect ice...
This Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) will support a collaborative research project to address data irregularities in the automated classification of sea ice using artificial intelligence (AI) methods. The total funding amount is $317,922, awarded on September 1, 2025, with a project period ending on August 31, 2028. The key objectives of this project are to develop novel, weakly supervised AI techniques that can effectively learn...
This $805,132 Project Grant awarded by the National Science Foundation's Geosciences Program (CFDA 47.050) will support a collaborative research effort to address label-data irregularities in sea ice classification using artificial intelligence (AI) methods. The project aims to develop novel weakly supervised AI techniques that can effectively learn from existing label data, despite irregularities, in order to automate sea ice classification. Key focus areas include reformulating the...
This $411,096 federal Project Grant award was provided by the National Science Foundation's Polar Programs (CFDA 47.078) to The Leland Stanford Junior University (Stanford University). The project aims to utilize machine learning and physics-based models to map and understand how surface meltwater on the Greenland ice sheet reaches the bedrock, which can impact ice sheet dynamics and response to climate change. Key products and services to be delivered include: Development of open-source deep...
This three-year, $100,000 Project Grant from the National Science Foundation's Office of Integrative Activities, under the Geosciences program (CFDA 47.050), will support the development of cybertraining programs focused on artificial intelligence and machine learning techniques for Arctic scientists. The Woodwell Climate Research Center will lead an initiative to establish an Arctic-AI research network and provide customized training through both in-person workshops and online self-paced...
This $100,000 Project Grant from the National Science Foundation's Geosciences program (CFDA 47.050) will fund the development of a cybertraining program to increase the capacity of Arctic researchers to employ artificial intelligence (AI)-driven techniques on Arctic data. Arizona State University will lead the effort to establish an Arctic-AI research network for collecting AI training needs and sharing resources. Customized training will be provided through in-person workshops and online...
This $199,242 federal Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) supports the development and application of novel artificial intelligence (AI)-based methodologies for detailed classification, 3D shape reconstruction, and comprehensive analysis and profiling of snowflakes and snowfall. The key products and services to be delivered through this 4-year award include: A novel AI/machine learning-enabled methodology for automatic classification of...
The National Science Foundation awarded a $269,223 Project Grant to the University of Texas at Austin from the Polar Programs federal grant program (CFDA 47.078) to support research titled "Collaborative Research: Machine-Enabled Modeling of Terminus Ablation for Greenland's Outlet Glaciers." The research aims to improve projections of sea-level rise from Greenland through developing physics-based modeling of ice-ocean interactions at outlet glaciers using machine learning analysis...
This $207,737 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will fund research to develop a new class of machine learning models called "Programmatic Foundation Models" that can efficiently analyze large-scale satellite, aerial, and ground imagery. The goal is to create interpretable, robust AI models that can understand global and local phenomena from images, providing insights...
This National Science Foundation (NSF) Integrative Activities (CFDA 47.083) project grant, valued at $286,102, will fund research at the University of North Dakota (UND) to develop advanced ice flow modeling capabilities using graphics processing units (GPUs). The goal is to better assess the Antarctic ice sheet's contribution to sea level rise and its sensitivity to climate change uncertainties. Specifically, the principal investigator and a graduate student will investigate an accelerated,...