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...
The University of Wisconsin-Madison was awarded a $916,000 Project Grant from the National Science Foundation under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The grant will fund the "SHF: MEDIUM: PROGRAM SYNTHESIS FOR WEAK SUPERVISION" project from July 2021 through June 2025. The project aims to advance the development of research cyberinfrastructure to enable and accelerate discovery and innovation in computing, communications, and...
The National Science Foundation (NSF) has awarded a $533,995 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to Iowa State University of Science and Technology (Iowa State University). The 4-year grant, spanning from October 1, 2023 to September 30, 2027, aims to improve the performance, robustness, generalizability, and efficiency of deep learning models for critical software assurance tasks such as bug detection, debugging, test input...
This $719,494 federal Project Grant award was provided by the National Science Foundation (NSF) through its Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to the University of California, San Diego (UCSD). The award will fund research to develop more scalable and general program synthesis algorithms that can support the composition of larger software systems. This work will build on the Semantics-Guided Synthesis (SemGuS) framework to enable the synthesis of...
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...
This Project Grant award of $225,000.00 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports collaborative research to improve the code generation capabilities of large language models (LLMs). The project aims to integrate program analysis techniques, such as symbolic execution and Bayesian analysis, to develop metrics for evaluating the quality of LLM-generated code and train a differentiable reward model to...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will provide $600,000 to the University of Wisconsin-Madison to advance the understanding and applications of deep learning models that underpin modern artificial intelligence (AI) systems. The research aims to deepen the theoretical foundations of deep learning by exploring vector-valued, multi-output mappings, compositional function spaces, and the...
This National Science Foundation Project Grant of $250,000 supports research at the University of California, San Diego to develop automated techniques for lemma synthesis in interactive theorem provers. The goal is to reduce the manual proof effort required when using interactive theorem provers to prove correctness and security properties of software. The project will explore multiple formulations of reducing the lemma synthesis problem to data-driven program synthesis, where the objective...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award (CFDA 47.070) provides $250,000 to the University of California, Irvine (UC Irvine) to develop a novel neuro-symbolic framework that combines advanced hyperdimensional mathematics with deterministic finite automata (DFA) and knowledge graphs. The goal is to create robust, interpretable models for efficient, data-driven knowledge transfer between simulated and real-world...