This $399,162 Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) aims to develop interpretable, stable, and mass-conserving artificial intelligence (AI) models to improve the computational speed and efficiency of geoscientific models, such as those used for air pollution and climate research. The project will create simpler "surrogate" machine learning models for key components like atmospheric chemistry and wildfire plume rise, allowing for...
This Project Grant award of $500,000.00 from the National Science Foundation's Geosciences Program (CFDA 47.050) is funding the development of a transformative AI-based framework for generating high-fidelity, physically consistent, and uncertainty-calibrated geoscience data. The goal is to overcome limitations in observational infrastructure and computational cost to produce enhanced datasets that can improve decision-making for disaster preparedness, emergency response, and infrastructure...
This $541,276 Project Grant awarded by the National Science Foundation's Geosciences Program (CFDA 47.050) will fund collaborative research by Oregon State University to develop artificial intelligence (AI) models that leverage gene sequence data to understand ecosystem processes in methane seep habitats. The research will focus on building two new AI models - one that codes genes and classifies them into pathways, and another that uses text and sequence protein representation to identify...
This $586,557 federal Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) supports a collaborative research initiative to develop artificial intelligence (AI) models that can leverage gene sequence data to better understand ecosystem processes, with a focus on methane seep habitats. The project will collect new microbial samples from methane seeps off the coasts of Oregon and Washington and employ novel natural language processing AI approaches to predict...
This $100,000 Project Grant awarded by the National Science Foundation's (NSF) Biological Sciences (CFDA 47.074) program will fund Cornell University to develop and validate an AI framework that can use a broad array of image data, such as satellite, drone, and internet-posted images, to automate and accelerate the generation of interpretable environmental science hypotheses at a planetary scale. The framework will integrate new techniques into foundational models for satellite imagery that...
This $650,000 Project Grant award from the National Science Foundation (NSF) under the NSF Technology, Innovation, and Partnerships (CFDA 47.084) program will accelerate development of a national-scale wetlands decision support toolkit for the United States. The project will integrate advances in wetland science, computing, remote sensing, and geospatial tools to create an equitable, user-friendly platform that provides accurate wetland mapping, characterization of ecosystem services, and...
This $1,324,092 project grant, awarded by the National Science Foundation's Geosciences program (CFDA 47.050) to Florida International University (FIU), establishes the South Florida Coastal Environmental Data and Modeling Center. The center will focus on developing AI/ML techniques for understanding and predicting key processes affecting coastal environments in Southeast Florida, with an emphasis on addressing challenges like flood damage, sea-level rise, urban flooding, water quality, and...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program will provide $103,500 to the University of Notre Dame to develop and validate an AI framework that can use a broad array of image data collected from different sensing modalities to automate and accelerate the generation of interpretable environmental scientific hypotheses at a planetary scale. The goal is to address the fundamental challenge in environmental...
This Project Grant award of $199,316.00 from the National Science Foundation's (NSF) Geosciences Program (CFDA 47.050) aims to advance the understanding of how extreme weather events, such as heavy rainfall and flooding, may change in response to future climate scenarios. The project, titled "EMBRACE-AGS-SEED: Harnessing the Power of Machine Learning to Generate Ensembles of Regional Climate Projections," will evaluate whether artificial intelligence and machine learning can provide...
This two-year, $259,515 Project Grant from the National Science Foundation's Integrative Activities program will fund the development of deep learning models and tools to assess dynamic connectivity and nitrate removal efficacy across wetlandscapes. Awarded on January 15, 2023 to the University of Kansas Center for Research, Inc., the grant provides support for an assistant professor fellowship and graduate student training in collaboration with the U.S. Environmental Protection Agency's...