Project Grant 2524379
- This federal Project Grant award of $200,000.00 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports collaborative research towards developing well-rounded graph retrieval techniques for retrieval-augmented generation (RAG) systems. The research aims to advance solutions for real-world problems such as scientific document question-answering, cybersecurity diagnosis, and e-commerce personalization by enhancing generative AI...
- This $400,000 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports the development of a new framework and tools for advancing data-centric artificial intelligence (AI) through generative approaches to feature space reconstruction. The project aims to transform the traditional way of constructing feature spaces by using deep generative learning instead of manual or classical discrete search...
- This Project Grant award of $175,000 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program advances trustworthy artificial intelligence (AI) by developing methods to integrate large language models with structured knowledge graphs. The research, conducted by the University of California, Merced, aims to create more reliable and accurate AI-powered question answering systems. The key technical advances include synergistic knowledge...
- This $550,000 Project Grant award from the National Science Foundation (NSF) under the Technology, Innovation, and Partnerships (CFDA 47.084) program supports the development of generative artificial intelligence (AI)-powered products that enable humans to interact and converse with books and other documents. The key products being developed include: Technology to represent the informational content of books and document discussions as a knowledge graph, which will then be used to ground large...
- This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) is focused on enhancing machine learning with graph-structured data. The research aims to address the challenge of data distribution shifts in AI models when applied to real-world scenarios, particularly in fields like particle physics and biochemistry. The key activities under this 3-year award include: Developing methods to estimate and...
- This Project Grant award of $175,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program is funding research towards a "Continual Collaborative Framework for Knowledge Graph Reasoning". The key objectives are to develop methods that can reliably, sustainably, and extensibly reason over knowledge graphs to extract meaningful insights and uncover novel connections. This is crucial for addressing complex national challenges and...
- This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program provides $500,000 in funding to Louisiana State University to address challenges in analyzing large-scale graph data. The project aims to develop scalable and efficient techniques for graph representation learning (GRL), particularly for graph data stored in modern data lakes. Key objectives include creating a partitioning-based framework to enhance GRL...
- This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CFDA 47.070) program supports research at the Massachusetts Institute of Technology (MIT) to develop a mathematical foundation for leveraging graph data in machine learning systems. The project aims to characterize how the geometry of latent feature spaces affects graph structure, recover latent feature vectors from observed graphs, and devise efficient algorithms...
- The National Science Foundation (NSF) awarded a $300,000 Project Grant under the Computer and Information Science and Engineering (CISE) program to Michigan State University (MSU) for the "Collaborative Research: III: Medium: Empowering Graph Neural Networks from a Data Perspective" project. The project aims to overcome key limitations of Graph Neural Network (GNN) models by focusing on improving the quality, scalability, and adaptability of the underlying graph data. Specifically, the...
- This award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $360,000 to the University of Illinois to develop "HPCGPT", a question-answering service that leverages generative AI and integrates data sources to enhance the user support services at academic high-performance computing (HPC) centers. In collaboration with Princeton University and Rutgers University, the project aims to improve the quality,...
This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support a collaborative research project titled "Towards Well-Rounded Graph Retrieval for Retrieval-Augmented Generation." The $199,997 award to the University of Oregon aims to develop innovative retrieval techniques that can locate and infuse appropriate graph-structured knowledge to assist generative AI systems in solving real-world problems, such as scientific document question-answering, cybersecurity diagnosis, and e-commerce personalization. The research will advance three key dimensions of graph retrieval for retrieval-augmented generation: improving utility, enhancing trustworthiness, and enabling diversity. The project will run from August 1, 2025, through July 31, 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $200.0k | 7/29/25 |