Project Grant 2505098
Award Date
10/1/25
Completion Date
9/30/28
Dollars Obligated
$333K
Federal Agency
Division of Information and Intelligent Systems
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Ithaca, NY, USA
Similar Awards
This $333,667 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program will support the development of a novel neurosymbolic programming framework called "Foundation Model Programming." This framework aims to generate symbolically interpretable scientific hypotheses from high-dimensional observational data across disciplines such as neuroscience, genomics, biomechanics, and ecology. The key objectives are to create...
This $333,000 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program supports the development of a novel neurosymbolic programming framework called "Foundation Model Programming." The project aims to enable the generation of symbolically interpretable scientific hypotheses from high-dimensional observational data, such as images and videos, across domains like neuroscience, genomics, and ecology. The framework will...
This Project Grant from the National Science Foundation's Division of Computing and Communication Foundations, under the Computer and Information Science and Engineering program (CFDA 47.070), provides $800,000 to Rice University from August 1, 2022 to July 31, 2025. The funding supports the development of a neurosymbolic program-synthesis framework that closely couples deep learning and classical symbolic methods for program synthesis. Specifically, the university researchers will explore new...
The National Science Foundation awarded a $800,000 Project Grant to the University of Texas at Austin under the Computer and Information Science and Engineering program (CFDA 47.070). The three-year award will support the development of a neurosymbolic program-synthesis framework that closely couples deep learning and classical symbolic methods for program synthesis. Researchers will explore new learning algorithms exposing neural models of code to explicit knowledge about program semantics....
This federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $631,953 to Yale University to develop a general foundation model framework for graph-structured data in scientific discovery. The researchers will address key limitations in existing graph foundation models by incorporating novel approaches such as multi-level graph neural networks, graph signal processing, multimodal graph...
This $279,399 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to adapt foundation models, which are advanced neural networks trained on large datasets, for sequential decision-making applications. The project aims to develop novel techniques to leverage foundation models for multimodal sequential decision-making, such as in smart manufacturing, multi-agent systems, and human-machine...
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) Project Grant award under the Engineering program (CFDA 47.041) provides $249,999 to the University of California, Irvine (UCI) to develop a novel neuro-symbolic framework that combines advanced hyperdimensional mathematics with deterministic finite automata and knowledge graphs. The goal is to create robust, interpretable models for efficient, data-driven knowledge transfer across diverse cyber-physical systems. The framework aims to reduce data...
The National Science Foundation (NSF) awarded a 4-year, $252,007 Project Grant under the Engineering (CFDA 47.041) program to Northeastern University. The grant supports the development and testing of AI-based programming tools to assist social and natural scientists with computer programming tasks. The research team is developing large language models and associated tools to support programming languages commonly used in the sciences, such as MATLAB and R, in order to make programming easier...
This federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $316,000 in funding to The Leland Stanford Junior University (Stanford University) to develop a general foundation model framework for scientific discovery using graph-structured data. The research aims to address key limitations in existing graph foundation models, such as the inability to handle complex graph structures or...