Project Grant 2224150
- 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 $499,237 National Science Foundation project grant supports research at the University of Michigan from October 1, 2022 to September 30, 2025 under the Computer and Information Science and Engineering program (CFDA 47.070). The research aims to develop a theoretical framework for algorithmic and statistical fraud detection applicable across domains. The investigator will design methods with formal guarantees on efficiency and efficacy, studying abstract fraud detection games and applying...
- 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 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...
- Grant Award Summary The University of Massachusetts received a $249,998 Project Grant from the National Science Foundation (NSF) Division of Computer and Network Systems under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), effective October 1, 2025, through September 30, 2028. This collaborative research project addresses corruption-robust online optimization algorithms designed to enhance the resilience of real-world systems against adversarial attacks and...
- The National Science Foundation awarded The Johns Hopkins University a $900,000 Project Grant under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to conduct collaborative research focused on understanding robustness in machine learning via parsimonious structures from October 1, 2022 to September 30, 2025. Specifically, the University will research conditions under which one can detect adversarial attacks on networks or data poisoning and reconstruct...
- This award from the National Science Foundation (CFDA 47.049 - Mathematical and Physical Sciences) provides $160,000.00 to Carnegie Mellon University to develop a new game-theoretic approach to statistical inference. The key products and services to be delivered include: Developing a fundamental theory and methodology for nonparametric, game-theoretic statistical inference, including hypothesis testing, confidence intervals/sequences, and change point detection. This work aims to create more...
- This $299,886 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to develop new big data algorithms that are robust to adversarial input. The award supports research to address emerging vulnerabilities in areas such as black-box streaming algorithms, white-box streaming algorithms, and adaptive data analysis with bounded space. This work will focus on improving the reliability, security, and trustworthiness of...
- 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 two-year, $500,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop foundational theory and optimal methods for robust statistical inference in the presence of adversarial data falsification. The grantee, Oregon State University, will investigate the fundamental limits of performing hypothesis testing and estimation when an adversary can manipulate input data. They will also develop robust inference methods supported by theoretical analysis to mitigate the impact of falsified data. Using a game-theoretic formulation, the grantee will model the complex interaction between the defender designing robust inference and the adversary optimizing falsification strategies. Techniques from optimization, game theory, and probability will be applied to derive optimal robust inference methods and analyze their properties. As a case study, the grantee will also develop and evaluate a robust power system state estimator to address falsified meter measurements. The results of this research will advance the state-of-the-art in robust statistics, machine learning security, and resilience of safety-critical systems against data falsification attacks.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $250.0k | 6/15/22 |