Project Grant 2512127
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) provides $249,998.00 to the University of Massachusetts to conduct collaborative research on developing corruption-robust online optimization and learning algorithms. The goal is to design learning algorithms that are both theoretically sound and practical to implement, enabling more robust decision-making in real-world applications...
- 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,...
- The National Science Foundation (NSF) awarded a $431,250 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to Carnegie Mellon University (CMU) to develop principled defenses against vulnerabilities in modern machine learning (ML) systems. The goal is to make robustness a core design property rather than an afterthought, bridging rigorous analysis with practical experimentation. The project will proceed along three technical thrusts: 1) robust...
- The National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program, CFDA 47.070, awarded Northeastern University a 4-year, $755,040 grant to develop new algorithmic techniques that achieve favorable tradeoffs between the robustness of machine learning (ML) algorithms and compression. The project aims to enable the deployment of robust ML algorithms on edge devices like phones, sensors, and IoT devices, which have computational, storage, and...
- The National Science Foundation (NSF) awarded a 5-year, $331,063 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program to North Carolina State University. The grant aims to develop transformative methods to enhance the resilience and reliability of machine learning (ML) systems in dynamic, open-world environments. Key objectives include enhancing robustness generalization across data distributions, introducing new learning algorithms to...
- 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 $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 Massachusetts Institute of Technology (MIT) was awarded a $500,000 Project Grant from the National Science Foundation (NSF) Division of Computer and Network Systems. The award is part of the NSF's Computer and Information Science and Engineering program (CFDA 47.070) and will support research into practical private information retrieval from October 1, 2021 to September 30, 2024. Specifically, MIT researchers will develop techniques to advance the security and privacy of data access...
- This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) (CFDA 47.070) Project Grant award of $108,000 to the Georgia Tech Research Corp, Office of Sponsored Programs, will fund collaborative research to develop a unified framework for analyzing adaptive stochastic optimization methods for machine learning applications. The research aims to produce self-tuning optimization algorithms with rigorous guarantees to reduce wasteful computation required by current...
- This National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) Project Grant award of $600,000 to the Massachusetts Institute of Technology (MIT) supports research into developing better algorithms for machine learning problems that involve sequential data with rich dependency structures. The project will explore learning methods for linear dynamical systems, graphical models, and hidden Markov models, with the goal of proving rigorous theoretical...
The National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program, CFDA 47.070, has awarded a $250,000 Project Grant to the Massachusetts Institute of Technology (MIT) to develop corruption-robust online optimization and learning algorithms. The research aims to strengthen the resilience of critical cyberinfrastructure and advance the scientific foundations of trustworthy AI. Specifically, the project will design learning algorithms that are theoretically sound and practical to implement, enabling more robust decision-making in real-world applications like online advertising, content delivery networks, and recommendation systems. The research will advance the theoretical understanding of vulnerabilities and resilience of online learning under malicious attacks, filling a critical gap in this area. The project will run from October 1, 2025 to September 30, 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $250.0k | 7/31/25 |