Project Grant 2530469

Award Date 6/15/25
Completion Date 5/31/26
Dollars Obligated $102K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Riverside, CA 92521, USA
Similar Awards
The National Science Foundation (NSF) awarded a $889,209 Project Grant under the Geosciences Program (CFDA 47.050) to the University of California, Irvine (UC Irvine) to advance wildfire science, prediction, and management using machine learning. The key products and services to be delivered include: Developing a large new public dataset of fire-related environmental observations to support large-scale machine learning and reproducible research on wildfire spread modeling. Advancing innovative...
The National Science Foundation awarded a $100,000 Project Grant to the University of California, Berkeley under the NSF Technology, Innovation, and Partnerships program (CFDA 47.084) to develop context-specific scientific simulation models and an advanced visualization platform to mitigate wildfire risks. The award period is from July 1, 2022 to June 30, 2023. Specifically, the University will create an integrated simulation framework capturing the dynamics of wildfire evacuation, including...
This $399,933 Project Grant awarded by the National Science Foundation's (NSF) Engineering program (CFDA 47.041) to the University of California, Los Angeles (UCLA) will support the development of new artificial intelligence-based infrastructure damage prediction models for enhanced resilience and emergency response planning. The 3-year project aims to advance the state-of-the-art in multimodal data integration and analytics to enable near real-time assessments of physical damage to...
The University of California, Los Angeles (UCLA) received a $100,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program. The grant will support a collaborative research project to develop rapid computational modeling and prediction capabilities for wildfire spread, with a focus on incorporating the impacts of human activity during and after wildfires. The research aims to improve the accuracy of short-term wildfire...
This $199,993 Project Grant was awarded on April 15, 2025 by the National Science Foundation's Engineering program (CFDA 47.041) to the University of Texas at Arlington. The grant will fund a 3-month turbidity data collection campaign and 9-month follow-up monitoring to establish a baseline for understanding the impact of the 2025 Los Angeles wildfires on water quality. The project will also use real-time satellite imagery to estimate fire-related metrics like char cover and soil burn severity...
This $250,000 National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) Project Grant, awarded to the University of California, Irvine on June 1, 2025, aims to transform the way artificial intelligence (AI) transfers learned knowledge from simulated environments to real-world applications. The project will develop a novel neuro-symbolic framework that combines advanced hyperdimensional mathematics with deterministic finite automata and knowledge graphs to...
This $250,000 project grant was awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to the University of California, Los Angeles (UCLA). The project aims to develop generative artificial intelligence (AI) frameworks to aid scientific reasoning and accelerate sustainable development. Specifically, the grant will fund the creation of new generative AI architectures, objectives, and techniques to efficiently...
The National Science Foundation awarded a $150,000 Project Grant to the National Center for Atmospheric Research (NCAR) under the Computer and Information Science and Engineering program (CFDA 47.070). The award will support development of a closed-loop sensing, modeling, and communications system to aid in wildfire detection, mapping, and prediction from July 2022 through June 2025. The system aims to leverage detailed 3D environmental models incorporating fuel, terrain, weather and other...
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 $1,500,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) supports the University of Southern California's research on safe multi-agent systems using a neurosymbolic approach. The project aims to develop new theories and algorithms for the design of safe learning-enabled multi-agent systems, with applications in areas like wildfire prevention using drone swarms and semi-automated...

This $101,562 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of an autonomous, end-to-end Large Language Model-based platform called the LA Fire Knowledge Graph-Agent (LAFIREKG-Agent). The platform aims to enhance situational awareness and decision-making for wildfire risk management in Los Angeles County by integrating multisource remote sensing data, meteorological variables, and other relevant information into a comprehensive knowledge graph. The key objectives are to enable rapid decision-making, predictive modeling, and complex reasoning to improve disaster preparedness and response strategies. The project will be executed by the Regents of the University of California at Riverside, a public research university and Hispanic-serving institution, over the period from June 2025 to May 2026. The resulting tools and best practices will be shared publicly to promote open science and reproducible research in AI-driven environmental studies.

Generated 7/1/25, 3:30 AM