Project Grant 2310260
- The National Science Foundation (NSF) awarded a $400,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the New Jersey Institute of Technology (NJIT) to investigate the identification and mitigation of shortcut features in deep learning models. The project aims to develop data-centric mechanisms to facilitate the generalization of deep neural networks, which can suffer from poor predictive performance when data distributions change or there...
- 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...
- This $400,000 federal Project Grant award was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to The Ohio State University. The grant supports collaborative research to develop a principled and unified mathematical framework for deep learning on low-dimensional data structures. The key objectives are to: 1) Design "white-box" deep neural networks optimized for information gain and representation...
- This Project Grant award, titled "CAREER: ENABLING NEXT-GENERATION DECENTRALIZED LEARNING: STRUCTURE, MODELS, AND METHODS", was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070). The $131,520 grant, awarded on April 15, 2025, supports research to advance decentralized learning methods that improve data privacy, reduce communication bottlenecks, and enhance learning performance in...
- This Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) supports research to develop adaptive control and learning algorithms to solve complex problems in networked systems with uncertainties. The $140,000 award to William Marsh Rice University, located in Houston, TX, will contribute to advancing national prosperity and social and economic welfare by creating new algorithms that can learn from data and adapt in real-time while accounting for...
- This federal Project Grant award, with a total funding amount of $264,299, was provided by the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The grant is being awarded to William Marsh Rice University in Houston, TX to support a research project titled "Reimagining Communication Bottlenecks in GNN Acceleration Through Collaborative Locality Enhancement and Compression Co-Design." The project aims to develop a...
- This Project Grant award from the National Science Foundation's (NSF) Division of Mathematical Sciences (CFDA 47.049 - Mathematical and Physical Sciences) supports research to develop novel mathematical models and efficient algorithms for deep learning on large-scale graph-structured data. The $249,999 award, spanning September 2024 to August 2027, aims to produce innovations in areas like graph convolutional networks, graph matching, and graph clustering. The research will involve graduate...
- This Project Grant award, titled "TOWARDS PRACTICAL GRADIENT CODING -MACHINE LEARNING SYSTEMS HAVE MADE REVOLUTIONARY ADVANCES IN SEVERAL AREAS, INCLUDING (BUT NOT LIMITED TO) AUTOMATED SPEECH AND IMAGE RECOGNITION, SCIENTIFIC DISCOVERY, HUMAN HEALTH AND NATIONAL SECURITY", is funded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070). The $582,000 award, with a period of performance from July 1,...
- 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...
- This Project Grant award of $500,000 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) supports research at the University of California, Riverside to study security vulnerabilities in machine learning (ML) models. The project aims to characterize and develop mitigation approaches for a threat that exploits unused parameters in trained ML models to potentially install malicious functionality without being...
This Project Grant award was provided by the National Science Foundation's Division of Information and Intelligent Systems under the Computer and Information Science and Engineering (CFDA 47.070) program. The $600,000 grant, awarded to William Marsh Rice University on October 1, 2023, will support research to systematically investigate the identification and mitigation of "shortcut learning" in deep neural networks (DNNs) from a data-centric perspective. The goal is to develop data-centric mechanisms to facilitate generalization in deep learning and mitigate the drawbacks of shortcut learning, which causes DNNs to make decisions based on spurious correlations that fail when applied to real-world scenarios. The research program also integrates machine learning, industrial engineering, and health informatics to train students with essential data analytics tools. There are no planned sub-awards for this grant.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $600.0k | 8/22/23 |