The National Science Foundation (NSF) awarded a $757,763 Project Grant under the Geosciences program (CFDA 47.050) to Carnegie Mellon University (CMU) to develop a new artificial intelligence (AI) framework for analyzing hyperspectral data to map ore deposits. The project, titled "COLLABORATIVE RESEARCH: CAIG: MAPPING ORE DEPOSITS WITH ARTIFICIAL INTELLIGENCE (MODAI)", will leverage advanced hyperspectral remote sensing techniques and AI to improve the identification of critical...
This $589,708 Project Grant award from the National Science Foundation's Geosciences program (CFDA 47.050) supports research to advance artificial intelligence (AI) methods for imaging and monitoring the Earth's subsurface. The project aims to develop a multi-task deep learning inversion framework that can simultaneously estimate subsurface velocity structures and earthquake source parameters using passive seismic data. By integrating deep learning with conventional full-waveform inversion...
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...
The National Science Foundation (NSF) awarded a $274,361 Project Grant under the NSF Technology, Innovation, and Partnerships (CFDA 47.084) program to Terra AI, Inc., a for-profit company based in Sunnyvale, CA. The award period is from September 1, 2024 to August 31, 2025. The grant will fund the development of AI systems and methods to accelerate the discovery and extraction of critical natural resources such as copper, nickel, cobalt, and rare earth minerals. The project aims to improve...
This Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) provides $899,999.00 to Princeton University to develop new artificial intelligence (AI) technologies that can automatically analyze images of rock cross-sections. The goal is to accelerate scientific discovery and improve reproducibility in geoscience research. The project will address the challenge of limited training data by creating a large volume of labeled synthetic data for rock...
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 Project Grant award of $213,433.00 from the National Science Foundation's (NSF) Integrative Activities program (CFDA 47.083) supports a six-month fellowship to enhance the lead researcher's career trajectory in data science and geoinformatics at the Carnegie Institution for Science in Washington, DC. The primary goals are to establish a reusable methodology for efficiently deploying data science in geoscience studies to accelerate scientific discoveries, with a focus on mineralogy. The...
This Project Grant award from the National Science Foundation (CFDA 47.050 - Geosciences) supports a partnership between geoscientists and computer scientists to co-develop novel AI-based approaches to quantify and explain uncertainty and inequity in geoscience. The $813,628 award, effective September 1, 2024 through April 25, 2025, will fund three key algorithmic innovations: A computationally efficient framework to produce interpretable estimates of aleatoric and epistemic uncertainty in...
This Project Grant award of $344,819 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program will advance fundamental machine learning research methods for analyzing satellite remote sensing data. The research objectives include: (1) developing a hypermodal geospatial foundation model to accommodate diverse sensor modalities, (2) creating a novel zero-shot mapping algorithm using natural language prompts, (3) building a testbed to evaluate...
This federal Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) provides $193,567 to the University of Texas at Dallas (UTD) to advance artificial intelligence (AI) methods for imaging and monitoring the Earth's subsurface. The research team will develop a multi-task deep learning inversion framework that simultaneously estimates subsurface velocity structures and earthquake source parameters using passive seismic data. This unified framework aims to...