Project Grant 2531140
- This $500,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE, CFDA 47.070) program supports research by the Regents of the University of California at Riverside to study security vulnerabilities in machine learning (ML) models. The project aims to understand how malicious actors could exploit unused parameters in trained ML models to install additional, potentially harmful functionality without detection. The research...
- This $900,000 Project Grant award from the National Science Foundation (NSF) Integrative Activities program (CFDA 47.083) supports the development of an infrastructure to track the full lifecycle of scientific datasets using data provenance methods. The project aims to enhance transparency and accountability in AI-driven scientific discovery across domains such as biomedical research, environmental modeling, and genomics. The three-layer architecture will capture provenance events from hardware,...
- This Project Grant award, funded by the National Science Foundation (NSF) under the Technology, Innovation, and Partnerships (TIP) program (CFDA 47.084), provides $129,032 to develop an end-to-end framework for privacy-preserving sharing of machine learning models trained on sensitive healthcare data. The research aims to address the challenge of enabling researchers to share AI models without compromising patient privacy, in order to advance medical research and discovery. Key activities...
- This Project Grant award from the National Science Foundation (CFDA 47.084 - NSF Technology, Innovation, and Partnerships) will support research by Vanderbilt University Medical Center (VUMC) to develop methods for auditing and certifying the privacy guarantees of machine learning models trained on sensitive patient data. The $150,000 project, running from October 1, 2025 to September 30, 2028, aims to address the challenge of safely sharing such models to advance medical research and scientific...
- This $319,942 Project Grant awarded by the National Science Foundation (CFDA 47.084 - NSF Technology, Innovation, and Partnerships) to the University of North Carolina at Chapel Hill (UNC-CH) focuses on building a collaborative and transparent open-source ecosystem to strengthen the reliability of artificial intelligence (AI) applications in healthcare. The project aims to enhance the safety, robustness, and interpretability of medical AI systems by developing shared infrastructure, governance...
- The National Science Foundation (NSF) awarded a $499,997 Project Grant under the Computer and Information Science and Engineering (CISE) Federal Grant Program to Trustees of Boston University. The grant, titled "CAREER: MAKING DOMAIN-SPECIFIC AI MODELS STEERABLE BY LEVERAGING FOUNDATIONAL MODELS," aims to develop new methods to make AI systems more transparent and adjustable for clinical applications. The project focuses on improving the fairness and reliability of medical AI by...
- This Project Grant award from the National Science Foundation's (NSF) Integrative Activities program (CFDA 47.083) provides $899,838 to Georgia TECH Research Corp to develop secure and reliable methodologies for ensuring the integrity, provenance, and authenticity (IPA) of data and AI models in scientific AI applications, with a focus on medical applications. The project aims to extend the underlying system infrastructure to enable more functional and certified logging to support...
- This Project Grant award from the National Science Foundation's (NSF) Technology, Innovation, and Partnerships (CFDA 47.084) program provides $128,985.00 to Virginia Polytechnic Institute & State University to develop methods for auditing and certifying the privacy guarantees of machine learning (ML) models trained on sensitive patient data. The goal is to enable secure sharing of these models to advance medical research and scientific discovery, while protecting personal privacy rights. The...
- The National Science Foundation (NSF) awarded a $1,000,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program to the Rector & Visitors of the University of Virginia (UVA). This 4-year project, running from September 2025 to August 2029, will develop strategies and methods to ensure the robustness of deep learning models used in healthcare applications. The key goals are to: 1) Create synthetic data-driven robustness audits that can...
- This federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, CFDA 47.070, supports research to develop improved methods for assessing privacy risks in machine learning (ML) models trained on sensitive tabular data such as patient records or financial information. The $379,224 award to The Pennsylvania State University aims to create frameworks for auditing attribute inference risks and disparities in both...
This federal Project Grant award of $900,000.00 from the National Science Foundation (NSF) Integrative Activities program (CFDA 47.083) will support the development of a data provenance framework for medical machine learning (ML) research. The framework aims to address critical challenges faced by artificial intelligence (AI) systems in medical applications, such as data integrity issues and patient data withdrawal concerns. The key products and services to be delivered under this award include: This grant will advance the foundations of secure medical data provenance, machine unlearning, and provide open-source tools and educational resources to prepare the next generation of medical and computer scientists for building trustworthy AI systems. The award period is from November 1, 2025 to October 31, 2028 and is being performed by the University of California, Los Angeles (UCLA).
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $900.0k | 7/24/25 |