This four-year $800,000 Project Grant from the National Science Foundation's (NSF) Computer and Information Science and Engineering program (CFDA 47.070) will support research at the University of California, Los Angeles (UCLA) to develop theoretical tools for understanding deep neural networks (DNNs). The research aims to identify common themes in how artificial and biological systems like the human brain learn. It will investigate the hypothesis that DNNs succeed when learning tasks exhibit...
This $298,450 National Science Foundation project grant supports research to quantify the error landscape of deep neural networks. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), the awardee New York University will employ statistical mechanics methods to characterize the basins of attraction in high-dimensional parameter spaces of deep learning models. The university will measure basin volume distributions and flatness as a function of network parameters...
The National Science Foundation (NSF) awarded the University of California, Los Angeles (UCLA) a $539,999 project grant under the Computer and Information Science and Engineering (CISE) program. The grant, effective October 1, 2023 through September 30, 2026, will fund a collaborative research project to develop formal verification frameworks and training/inference algorithms to ensure the safety and adherence to constraints of AI-based sequential generation models across critical applications...
This federal Project Grant award of $800,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) is supporting research to develop methods for certifying the safety of autonomous systems that use deep learning-enabled perception, prediction, and control components. The key goals of the project are: (1) to develop techniques for learning safety certificates and control policies for these types of learning-enabled autonomous...
The National Science Foundation (NSF) awarded a $1,174,741 Project Grant to Rector & Visitors Of The University Of Virginia, doing business as the University of Virginia, to conduct research under the NSF Division of Computing and Communication Foundations program (CFDA 47.070). The project, titled "SHF: MEDIUM: MORE RELIABLE IMAGE NETWORKS THROUGH SCENE-BASED SPECIFICATION, NEURO-SYMBOLIC TRAINING, AND SYSTEMATIC SPECIFICATION-DRIVEN TESTING", seeks to develop techniques to assure...
This $500,000 project grant, awarded on January 1, 2024 by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), aims to address the urgent need for end-to-end safety in learning-enabled autonomous systems across various application scenarios, such as self-driving cars and urban air mobility. The project, titled "COLLABORATIVE RESEARCH: SLES: GUARANTEED TUBES FOR SAFE LEARNING ACROSS AUTONOMY ARCHITECTURES,"...
This $1.6 million Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research at the University of Pennsylvania from October 2022 through September 2026. The research focuses on developing theoretical tools to build an understanding of why deep neural networks (DNNs) work and when they can fail. Investigators will seek to identify common themes in how artificial and biological systems like the human brain learn. They will...
This $213,679 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) supports the development of a novel compositional framework for designing and verifying safe, learning-enabled cyber-physical systems (CPS). The primary awardee, the University of California, Berkeley, will pursue a hierarchical and modular approach to reason about the uncertainty and approximations introduced by machine...
The National Science Foundation (NSF) has awarded a $266,589 Project Grant under the Computer and Information Science and Engineering (CISE) program to the University of California, Berkeley to develop safe learning-enabled systems that can navigate uncertain environments. The project aims to create a two-phase design process that combines an offline robust synthesis phase with an online safety monitoring and adaptation phase, enabling provable end-to-end safety guarantees for learning-enabled...
This three-year, $500,000 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), will support research to advance neural network verification techniques. The grantee, Stanford University, will partner with the Hebrew University of Jerusalem to pursue three goals: developing more scalable verification methods using abstraction and compositional reasoning;...