Project Grant 2425834

Award Date 9/15/24
Completion Date 8/31/27
Dollars Obligated $541K
Federal Grant Program
47.050
Assistance Type
Project Grant
Place of Performance
Corvallis, OR 97331, USA
Similar Awards
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 $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 $200,000 Project Grant award from the National Science Foundation's (NSF) Biological Sciences (CFDA 47.074) program supports the development of a Multi-Modality Microbiome Foundation Model (M3FM) - an artificial intelligence model that integrates diverse soil microbiome data sets to provide a more comprehensive framework for understanding soil microbiomes. The project aims to address the challenges of soil metagenomic data sparsity by leveraging large public data sets through...
This $193,500 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) funds the development and validation of an AI framework that can leverage a broad array of image data, including satellite, drone, and online imagery, to automate and accelerate the generation of interpretable environmental science hypotheses at a planetary scale. The overarching goal is to overcome limitations of traditional...
This $198,141 project grant awarded by the National Science Foundation's Biological Sciences program (CFDA 47.074) seeks to enhance predictive understanding of global species distributions through the development of advanced generative artificial intelligence (AI) models. The project aims to create a versatile foundation model that can be used by scientists, conservationists, and educators to better understand and protect the natural world. By training this model on a vast array of environmental...
The National Science Foundation (NSF) awarded a $320,000 Project Grant under the Geosciences Program (CFDA 47.050) to the University of Delaware. The funding will support collaborative research to empower artificial intelligence (AI) to reveal phytoplankton community dynamics in coastal oceans. The project aims to address the scarcity of in-situ data for estuarine-coastal phytoplankton by constructing a large-scale database of phytoplankton observations, enabling global data sharing. It will...
This $452,604 Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) supports the development of a foundational Artificial Intelligence (AI) model for advanced seismic data analysis to revolutionize earthquake science. The project aims to train the AI model on vast archives of seismic data to identify and characterize earthquake signals, leveraging cutting-edge techniques like transformer models. This research will focus on improving earthquake detection,...
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 $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 Project Grant award for $529,636 from the National Science Foundation's Geosciences Program (CFDA 47.050) will enable the University of Louisiana at Lafayette to develop innovative artificial intelligence methods to analyze hyperspectral satellite imagery and construct a large-scale database to better understand phytoplankton community dynamics in coastal waters. The key products and services to be delivered include: Establishing a comprehensive database of phytoplankton observations to...

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 putative genes responsible for key biogeochemical cycles. These AI models will be applied to new microbial samples collected from methane seep areas off the coast of Oregon and Washington, with the goal of empirically validating the identified "DL-genes" by testing if they are transcribed when associated geochemical processes are observed. To broaden the use of the methods, the project will include a tutorial and workshop, as well as the production of a documentary showcasing how AI can advance the understanding of Earth systems.

Generated 6/17/25, 5:18 AM