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 Project Grant award, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070), aims to develop a novel neurosymbolic programming framework called "Foundation Model Programming." This framework is designed to generate symbolically interpretable scientific hypotheses from high-dimensional observational data, such as images and videos, across various scientific disciplines like...
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 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...
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 $611,492 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program to the University of Texas at Austin focuses on developing neurosymbolic AI techniques to enhance the accessibility and efficiency of interactive formal theorem provers. The goal is to automate the low-level aspects of theorem-proving, enabling wider use of formal verification tools for applications like safer software, more robust hardware, and...
The National Science Foundation (NSF) awarded a $329,183 Project Grant to the College of William & Mary under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant, awarded on October 1, 2023, will fund the development of a framework and methodology to enable researchers and software engineers to better interpret the behavior of AI-powered developer tools that leverage neural language models for source code. The project aims to generate global and local...
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'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 $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...