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 $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 $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 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, $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 $316,672 Project Grant awarded by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program supports research at San Diego State University Foundation to develop a comprehensive understanding of the robustness and computational efficiency of deep neural networks. The key focus areas include: 1) formulating theoretical frameworks for subnetwork adversarial robustness, 2) characterizing transferability through curriculum learning, and 3)...
This three-year, $300,000 project grant from the National Science Foundation's Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), will support the development of new algorithms and computational methods for trustworthy machine learning via bi-level optimization. The grantee, Michigan State University, will advance the theoretical understanding and practical implementation of robust and fair deep learning....
The National Science Foundation (NSF) awarded a $597,292 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program to the Research Foundation of the City University of New York (RFCUNY) - Hunter College to improve the long-term reliability and evolvability of machine learning (ML) systems. This 3-year project will develop methodologies and automated refactoring techniques to address technical debt in ML systems, which can negatively impact the...
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 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program, with a total funding of $101,476, supports research to develop reliable decision-making algorithms for machine learning applications in complex systems. The 5-year project, which commenced on April 1, 2025, aims to address the challenge of ensuring safety and performance when deploying machine learning predictions in feedback loops, such as in weather...