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 three-year National Science Foundation project grant of $418,380 supports research at the University of Wisconsin-Madison to develop a neurosymbolic framework for semantics-aware program synthesis. Funded under the NSF's Computer and Information Science and Engineering program (CFDA 47.070), the award period runs from August 1, 2022 to July 31, 2025. Specifically, the university researchers will couple large neural models trained on source code with symbolic methods from formal methods to...
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...
This $750,000 Project Grant was awarded by the National Science Foundation (NSF) Division of Computing and Communication Foundations under the CFDA program "Computer and Information Science and Engineering." The grant was awarded to the University of Texas at Austin to develop a new paradigm for robot learning from demonstrations (LfD) using program synthesis techniques. The research aims to address limitations in existing neural network-based LfD approaches by combining them with...
The National Science Foundation awarded a $275,000 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 program synthesis techniques to help software developers manage schema changes to databases. The project aims to simplify the schema modification process through automated techniques for migrating data between formats and updating code to reflect schema...
The National Science Foundation (NSF) has awarded a $599,468 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of Texas at Austin. The grant, titled "HCC:SMALL: NEURAL SHAPE GENERATORS UNDER GEOMETRIC, PHYSICAL, AND TOPOLOGICAL PRIORS -SHAPE SYNTHESIS?CREATING FORMAL DESCRIPTIONS OF NOVEL 3-D SHAPES," will fund research to develop a computational framework that incorporates physical, topological, and geometrical preferences...
The University of Texas at Austin received a $175,000 Project Grant award from the National Science Foundation Division of Information and Intelligent Systems. The award is part of the NSF's Computer and Information Science and Engineering program (CFDA 47.070) and will fund research using linguistic variation to understand deep neural models of language from July 1, 2021 to June 30, 2023. As part of the grant, the University will conduct investigator-initiated research exploring how...
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 National Science Foundation (NSF) $900,000 "COLLABORATIVE RESEARCH: SHF: MEDIUM: NATURAL LANGUAGE MODELS WITH EXECUTION DATA FOR SOFTWARE TESTING" Project Grant, awarded under the Computer and Information Science and Engineering program (CFDA 47.070), aims to develop natural language processing (NLP) models to simplify the development and maintenance of software tests. Key objectives include test generation, completion, update, and migration across programming languages, targeting...
This $256,710 National Science Foundation award under the Computer and Information Science and Engineering (CFDA 47.070) program supports a collaborative research project led by the University of Texas at Austin. The project investigates full-stack implementation methodologies for developing expressive programming systems that bridge the gap between high-level specifications and high-performance implementations of complex reasoning tasks at scale. Key focus areas include extending declarative...