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...
The National Science Foundation awarded a $666,000 Project Grant to The Trustees of Columbia University in the City of New York (Columbia University) through the Computer and Information Science and Engineering Program (CFDA #47.070). The objective is to improve the performance, robustness, generalizability, and efficiency of deep learning models for software assurance tasks such as bug detection, debugging, test input generation, and test suite prioritization. The research focuses on encoding...
This $206,568 National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) Project Grant award to Auburn University aims to develop AI/machine learning-assisted system and package-level co-design methodologies to optimize the power, performance, chip area, reliability, and cost of next-generation AI hardware. The key research thrusts include analytically modeling chiplet-based AI hardware metrics, developing techniques to detect and repair faulty hybrid...
This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to enable developers to perform fine-grained software testing, thereby increasing software quality. The research project will: (1) develop a language and framework for expressing and using fine-grained tests; (2) automatically generate fine-grained tests from code or existing tests; (3) adapt fine-grained tests to software evolution and...
This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to enable developers to perform fine-grained software testing, thereby increasing software quality. The key objectives are to: (1) develop a language and framework for expressing and using fine-grained tests; (2) automatically generate fine-grained tests from code or existing tests; (3) adapt fine-grained tests to software evolution and...
This $400,000 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to develop a principled and unified mathematical framework for deep learning on low-dimensional data structures. The project aims to bridge the gap between theory and practice of deep learning by designing "white-box" deep neural networks using unrolled optimization schemes to maximize information gain in...
The University of Virginia received a $498,460 project grant award from the National Science Foundation on October 1, 2021 to support research titled "SHF: SMALL: DISTRIBUTION-AWARE TESTING FOR NEURAL NETWORKS." The project aims to develop testing techniques for neural networks that account for data distribution properties and will be completed by September 30, 2024. The award is provided through the NSF's Computer and Information Science and Engineering program (CFDA 47.070), which...
This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) program aims to develop software frameworks that can efficiently serve and deploy machine learning models for a variety of AI-powered applications. The $600,000 award, spanning from October 2024 to September 2027, tasks the prime awardee, Georgia Tech Research Corporation, with creating agile mechanisms and policies to serve a family of AI models across...
The University of Utah received a $220,500 Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070). This 5-year award, beginning March 1, 2024, supports research to develop scalable security testing techniques for large software systems. The project focuses on advancing "fuzzing" - a predominant software vulnerability detection method - to address the unique challenges posed by software...
This Project Grant award of $680,733 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research towards developing methods for interpreting deep learning models. The University of Houston System is the prime awardee for this 1.7-year project, which aims to improve the usability and trust in deep learning systems for real-world applications like healthcare and cybersecurity. The research explores post-hoc...