Project Grant 2524380
Award Date 8/1/25
Completion Date 7/31/28
Dollars Obligated $200K
Federal Agency
Federal Grant Program
Assistance Type
Project Grant
Place of Performance
Nashville, TN 37203, USA
Similar Awards
- 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...
- 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 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...
- This Project Grant award from the National Science Foundation's (NSF) Division of Mathematical Sciences (CFDA 47.049 - Mathematical and Physical Sciences) supports research to develop novel mathematical models and efficient algorithms for deep learning on large-scale graph-structured data. The $249,999 award, spanning September 2024 to August 2027, aims to produce innovations in areas like graph convolutional networks, graph matching, and graph clustering. The research will involve graduate...
- The National Science Foundation (NSF) awarded a $724,000 Project Grant under the Computer and Information Science and Engineering (CISE) Federal Grant Program to the University of Texas at Austin (UT Austin). The grant, awarded on July 15, 2025, will fund the development of a new highly parallel hardware and software system for graph-based data analytics to improve the reliability and efficiency of modern artificial intelligence (AI) techniques, including large language models (LLMs) like...
- This $550,000 award from the National Science Foundation's (NSF) Technology, Innovation, and Partnerships (CFDA 47.084) program supports the development of a hybrid, scalable data management system to improve access to scientific knowledge in data science. The project aims to create an intelligent, user-friendly interface that can extract, organize, and provide deep access to relevant concepts from peer-reviewed scientific literature. Key objectives include: Building a comprehensive knowledge...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering program (CFDA 47.070) supports a collaborative research effort to accelerate the execution of large-scale graph problems on distributed computing systems. The $2,656,268 award, active from August 1, 2023 to July 31, 2028, aims to develop new algorithms, software frameworks, and hardware accelerators to efficiently process large graph datasets in domains like computational...
- This $1,091,988 federal 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 to systematically design and optimize high-performance graph analytics algorithms. The researchers at the University of California, Davis (UC Davis) will create an open-source software platform that allows for automated exploration of implementation choices for graph computations using a...
- This federal Project Grant award from the National Science Foundation (CFDA 47.070 - Computer and Information Science and Engineering) provides $400,000 to Emory University to conduct collaborative research on empowering graph neural networks (GNNs) from a data-centric perspective. The project aims to address key challenges with GNNs related to data scale, distribution, and quality, which currently limit the widespread real-world application of these powerful AI models. The research will focus...