Project Grant 2414652
- 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...
- 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 $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 $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 $171,387 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to advance the efficiency of machine learning model inference through the development of a compression-aware computing framework. The project will focus on two core research objectives: 1) Enabling machine learning models to detect, identify, and localize the effects of lossy compression techniques, and 2) Leveraging this self-awareness to recover...
- 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 three-year, $276,611 National Science Foundation Project Grant supports research at the University at Albany to develop algorithms and theory for compressing deep neural networks. Funded through NSF's Mathematical and Physical Sciences program (CFDA 47.049), this award will advance knowledge in discrete optimization and machine learning. Key products include new coarse gradient and thresholding algorithms to enable efficient deployment of AI systems on mobile and low-power platforms. By...
- 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 $236,099 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program is supporting research to develop robust optimization and machine learning algorithms capable of handling dynamic and uncertain data environments. The research aims to advance optimization techniques for fundamental supervised learning tasks, yielding computationally and data-efficient algorithms with provable error guarantees. This work will...
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 $755,040 award to Northeastern University will fund research to design new robustness-enhancing training regularization penalties and adversarial training techniques, which will be applied to large-scale transformer architectures and combined with model compression techniques like pruning and quantization. The research will also explore continual learning approaches to enable dynamic adaptation of the robust ML models. The project aims to enable the deployment of robust ML in safety-critical applications like autonomous vehicles, medical applications, and smart cities.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $755.0k | 7/18/25 |