Project Grant 2512128
- 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...
- This is a Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070). The $275,000 award supports research to develop cooperative online learning algorithms that allow multiple heterogeneous devices to collaboratively explore a large decision space and identify optimal solutions in computer network applications. The project aims to extend existing theory on online learning to settings with...
- This federal Project Grant award of $236,099 was provided by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program, which supports fundamental and applied research across computational domains. The research aims to develop robust optimization and learning algorithms capable of handling dynamic and uncertain data scenarios, advancing our understanding of learning in modern machine learning environments. Key objectives include enhancing supervised...
- 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 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 $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 National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program award provides $298,660 to the University of Massachusetts (UMass) for a project titled "COLLABORATIVE RESEARCH: AF: SMALL: RESILIENT DATA STREAM ALGORITHMS". The project aims to design resilient data stream algorithms that are less dependent on explicit assumptions and more reliable in practical applications. Key focus areas include developing adversarially robust...
- 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 $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...
- This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) supports collaborative research addressing fundamental challenges in discrete and continuous optimization. The $263,764 award, with a period of performance from October 1, 2025 to September 30, 2029, aims to develop faster and more accurate optimization techniques for modern algorithms, including those used in artificial intelligence (AI)...
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 such as online advertising, content delivery networks, and recommendation systems. This research directly supports the national interest by strengthening the resilience of critical cyberinfrastructure and advancing the scientific foundations of trustworthy AI. The project will run from Oct 1, 2025 to Sep 30, 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $250.0k | 7/31/25 |