Project Grant 2442625
Award Date
8/1/25
Completion Date
7/31/30
Dollars Obligated
$129K
Federal Agency
Division of Computer and Network Systems
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Princeton, NJ 08544, USA
Similar Awards
The National Science Foundation (NSF) awarded a $240,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the University of Central Florida (UCF) Board of Trustees Office of Research. The grant supports a 3-year research project to develop a theoretical analysis that sheds light on the robustness of neural network-based methods and the properties of adversarial training. The research aims to contribute to the development of more robust neural network-based...
This $300,000 National Science Foundation project grant supports research into robust machine learning under sparse adversarial attacks through 2025. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), the University of California, Santa Barbara will develop theoretical frameworks and defense methods to make machine learning models resilient against perturbations affecting few data points. Specifically, the researchers aim to establish fundamental limits of...
This $123,859 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will enable the University of California, Santa Barbara (UCSB) to develop a closed-loop machine learning (ML) pipeline that iteratively refines training data collection to improve the generalizability of ML models for network operations. The key objectives are to: (1) design a programmable data-collection platform to acquire flexible and...
This $524,981 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research at Northeastern University to develop novel methods and tools to investigate and mitigate poisoning attacks against machine learning (ML) models used for cybersecurity threat detection. The project aims to create explanation-based ML and generative model approaches to identify stealthy poisoning attacks, as well as...
This three-year $300,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program aims to advance understanding of robustness in machine learning models. Specifically, the University of Maryland, College Park will research conditions under which adversarial attacks on deep networks can be detected and original data reconstructed. It will also study fundamental limits of robustness guarantees against poisoning attacks, especially with a...
This $197,997 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of a novel Safety-Centric Network Learning (SNL) framework at the University of Connecticut (UConn). The project aims to design reliable network data and learning models, ensure the stability and consistency of network outputs, and advance the explainability of network learning usage. Key research...
The National Science Foundation (NSF) is providing a $206,382 Project Grant under its Computer and Information Science and Engineering (CISE) program to the Illinois Institute of Technology (IIT) Sponsored Research and Programs Division. The objective of this 5-year award is to design a trustworthy, flexible, and generalizable machine learning framework that can provide robustness against common privacy and security attacks. The project will develop novel information-theoretic representation...
This three-year, $425,000 Project Grant from the National Science Foundation's Division of Electrical, Communications and Cyber Systems, under the Engineering program (CFDA 47.041), will fund research at The Pennsylvania State University to develop provably secure, online learning-based adaptive cyber defenses for real-world servers. The research aims to provide mathematically rigorous guarantees by synthesizing new models integrating machine learning, game theory, and control theory. It will...
This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program provides $201,337 to San Diego State University Foundation to conduct research on the robustness and efficiency of deep neural networks. The key objectives are to: Investigate methods for simultaneously achieving high performance, computational efficiency, and adversarial robustness in deep learning models. This includes developing a fundamental...
The National Science Foundation (NSF) Division of Computer and Network Systems awarded a $174,178 project grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the President and Board of Trustees of Santa Clara College. The project aims to develop methods and a system for deploying complex machine learning (ML) models on network processing units (NPUs) to enable ultra low-latency performance for modern applications such as self-driving, security threat...