Project Grant 2145630

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 two-year, $250,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop techniques for improving the interpretability and robustness of deep neural networks. Specifically, the University of California, Santa Barbara will apply ideas from communication theory and neuroscience to actively shape the features extracted by individual layers of neural networks in addition to end-to-end training. By learning "matched...

This $659,678 NSF CAREER (Faculty Early Career Development) award, funded through the Engineering program (CFDA 47.041) and administered by the Division of Electrical, Communications and Cyber Systems, supports a five-year project (April 1, 2026 – March 31, 2031) at MIT to develop foundational technologies for trustworthy learning-enabled autonomous systems. The primary deliverables include new mathematical theory and efficient algorithms for constraint-satisfying learning, uncertainty-aware...

The U.S. National Science Foundation (NSF) awarded a $120,120 CAREER grant under the Computer and Information Science and Engineering (CISE) program to The Johns Hopkins University. The project, titled "DEEPMATTER: A Scalable and Programmable Embedded Deep Neural Network", will develop novel methodologies for optimizing deep neural network (DNN) models to enable their deployment on embedded systems with limited hardware resources and power budgets. The research aims to create new DNN...

This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program grant awarded to North Carolina State University (NC State) provides $208,745 to develop scalable and stable neural network paradigms to address variability issues in emerging device-based platforms for large-scale neuromorphic computing. The project aims to improve the reliability and sustainability of deep learning accelerators for data centers by explicitly modeling weight uncertainties,...

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 $150,000 EAGER: IMPRESS-U Project Grant awarded on January 1, 2024 by the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will support research at Pennsylvania State University (Penn State) to develop new Random Matrix Theory (RMT) techniques and apply them to improving the performance of Deep Neural Networks (DNNs). The key objectives are to: (1) further advance RMT methods, and (2) leverage these techniques to enhance accuracy, convergence, and...

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 awarded a $245,043 Project Grant to Carnegie Mellon University under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The five-year award will support research towards theoretical foundations of neural network-based representation learning. Specifically, the awardee will build a comprehensive theory for new neural network representation learning techniques. This includes characterizing statistical properties of...

The National Science Foundation awarded a $599,304 three-year Project Grant to the University of California, Santa Barbara under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The grant will support the development of novel methods and hardware architectures for optimization and acceleration of spiking neural networks. Key products will include algorithms, software design tools, and field-programmable gate array hardware architectures and...