Project Grant 2442716
- This Project Grant award of $331,063, provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), aims to develop transformative methods for enhancing the resilience and reliability of machine learning (ML) systems in dynamic, real-world environments. The award will address three key challenges: 1) improving robustness generalization across data distributions, 2) ensuring robustness against multiple attacks simultaneously,...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, with CFDA Number 47.070, will support the development of new algorithmic techniques to achieve favorable tradeoffs between the robustness of machine learning (ML) algorithms and compression. The goal is to enable the deployment of robust ML algorithms on edge devices, such as phones, sensors, and IoT devices, which have computational and power limitations. The...
- This $395,842 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research at Princeton University to improve the reliability of machine learning (ML) for critical networking functions. The project, titled "CAREER: Contextual Robustness for ML-Powered Network-Based Functions," aims to develop an open-source framework called "NetFortify" that helps researchers and engineers test and...
- 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 three-year, $500,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research at the University of California, Davis to develop trustworthy machine learning systems through adversarial robust reinforcement learning. Specifically, the award supports investigating potential vulnerabilities in reinforcement learning models and algorithms, developing robust RL approaches that mitigate impacts from adversarial attacks, and...
- This $271,343 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research into developing robust machine learning and inference methods that can withstand data corruption and distribution shifts. The project aims to explore new techniques for structured learning, supervised learning, and reinforcement learning that are resilient to these challenges, with potential applications in healthcare,...
- This $500,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE, CFDA 47.070) program supports research by the Regents of the University of California at Riverside to study security vulnerabilities in machine learning (ML) models. The project aims to understand how malicious actors could exploit unused parameters in trained ML models to install additional, potentially harmful functionality without detection. The research...
- This three-year National Science Foundation project grant of $300,000 will fund research to advance trustworthy machine learning through bi-level optimization. The grantee, the University of California, Santa Barbara, will develop new algorithms and computational methods to achieve robust and fair deep learning. Specifically, the project will create a bi-level optimization framework for robust learning, defenses against adversarial examples and distribution shifts, and a full-stack robustness...
- 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 $299,998 federal Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CFDA 47.070) program will support collaborative research at Carnegie Mellon University to develop new big data algorithms that are robust to adversarial input. The key focus areas include: 1) adversarial robustness in black-box and white-box streaming settings, and 2) adaptive data analysis with bounded space. The research team will also explore emerging attack...
This $431,250 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research by Carnegie Mellon University to develop robust machine learning (ML) systems that can operate reliably in complex, real-world environments. The 5-year project (8/1/2025 - 7/31/2030) aims to bridge theoretical analysis and practical experimentation to address the brittleness of current ML models, which can fail unexpectedly under adversarial attacks or distribution shifts. Key focus areas include: 1) robust fine-tuning techniques for large pre-trained models to preserve generalization across domains, 2) defenses against adversarial attacks on language models to ensure safety and consistency, and 3) robustness in autonomous AI systems composed of multiple subsystems. By developing principled defenses and algorithmic tools, this project seeks to advance the reliability and trustworthiness of AI systems operating in high-stakes real-world settings.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $431.3k | 7/16/25 |