Project Grant 2531010
- 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 National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) program aims to develop software frameworks that can efficiently serve and deploy machine learning models for a variety of AI-powered applications. The $600,000 award, spanning from October 2024 to September 2027, tasks the prime awardee, Georgia Tech Research Corporation, with creating agile mechanisms and policies to serve a family of AI models across...
- 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 National Science Foundation (NSF) Technology, Innovation, and Partnerships (CFDA 47.084) Project Grant award of $1,499,999 to Georgia Tech Research Corp provides funding to develop an open-source ecosystem for secure authentication, authorization, and credential management in distributed computing systems. The project aims to enhance access to digital infrastructure and shared resources for researchers, educators, students, and small businesses, while strengthening U.S. leadership in...
- 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 National Science Foundation (NSF) Project Grant award under the STEM Education (CFDA 47.076) program provides $450,000 to Georgia TECH Research Corporation to develop fundamental algorithms and infrastructure that enable error-resilient operation of artificial intelligence (AI) systems. The key objectives are to: 1) Implement efficient post-manufacturing testing and tuning methods for deep neural networks implemented on energy-efficient analog crossbar arrays, to address vulnerability to...
- 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 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...
- This $117,451 federal Project Grant awarded by the National Science Foundation (NSF) under the NSF Technology, Innovation, and Partnerships (TIP) program (CFDA 47.084) supports research to develop novel approaches that can help Public Interest Technology (PIT) organizations deploy data safeguards when building AI systems. The project engages a multi-disciplinary team to conduct ethnographic and computational research on using disclosure limitation techniques, including differential privacy, to...
- 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 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 reproducibility. These extensions will incorporate optimized cryptographic techniques to ensure the IPA of data and AI models. Specific innovations include hash-chains for preventing data reordering, watermarking for statistically verifiable data provenance, and zero-knowledge proofs for certifying AI model ownership and privacy estimators for detecting model dependencies. The award period is from January 1, 2026 to December 31, 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $899.8k | 7/28/25 |