Project Grant 2530655
- 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 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 $140,000 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will develop a next-generation statistical framework to improve the reliability and reproducibility of data science (DS) and artificial intelligence (AI) methods. The project, titled "Collaborative Research: Performance Guaranteed Statistical Learning with Multiple Classes of Models (Guided by PCS)," aims to advance a framework called...
- This $300,000 Project Grant award from the National Science Foundation's Biological Sciences (CFDA 47.074) program will develop an open and scalable data infrastructure to enable AI-enabled ecological and biodiversity research. The infrastructure will address challenges of fragmented, inconsistent, and inaccessible data by automating standardized access across various data sources while preserving data quality, provenance, and attribution. This 3-year project will create a dynamic inventory...
- This $599,411 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of a "Trustworthy Toolbox for Double-Correct Predictive Modeling in Sciences." The project aims to create advanced artificial intelligence (AI) and machine learning (ML) models that can make accurate predictions while also providing transparent, scientifically-grounded rationales for their outputs. This...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program will fund a research project to study data management practices at NSF-funded user facilities. The $177,000 award, spanning October 2025 to September 2028, will assess current data organization, storage, and sharing practices at selected major and mid-scale NSF facilities. The goal is to create a roadmap aligned with FAIR data principles to help...
- This $1,297,636 Project Grant was awarded by the National Science Foundation (NSF) under the Technology, Innovation, and Partnerships (CFDA 47.084) program. The grant funds the development of the Trusted Integration Data Exchange System, a secure platform that enables government agencies, healthcare organizations, and research institutions to perform data analysis on sensitive datasets while maintaining compliance with privacy regulations. The key products and services being delivered include:...
- This $175,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will fund research to develop new statistical and computational methods to enhance the reliability of data analysis in modern, large-scale datasets, particularly in the era of AI. The key areas of focus include: (1) analyzing the robustness of manifold and deep learning algorithms for high-dimensional, noisy, and nonlinear data; (2) developing statistical theory...
- This $141,000 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports a collaborative research project to study data management practices at NSF-funded user facilities. The goal is to create a roadmap aligned with FAIR data principles to improve how research data is organized, stored, and shared across these facilities. The project will assess current data management practices through surveys of facility...
- This $174,995 Project Grant was awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The grant, awarded to the San Diego State University Research Foundation, aims to develop methods for applying differential privacy techniques to provenance graphs, which track the origin, usage, and modifications of data. The project has two main thrusts: 1) Identifying privacy risks in current provenance-based machine learning...
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, operating systems, and scientific applications, merging them into a unified provenance graph for scalable storage and analysis. The research also includes privacy-preserving anomaly detection techniques using federated machine learning to identify suspicious data behaviors without sharing sensitive raw data. Expected outcomes include open-source tools, curriculum materials for AI data integrity, and evaluations on real-world datasets, which will enhance secure collaboration, reproducible science, and public trust in data-intensive research.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $900.0k | 7/28/25 |