Project Grant 2444247

Award Date 7/15/25
Completion Date 6/30/28
Dollars Obligated $500K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Baton Rouge, LA 70803, USA
Similar Awards
This $150,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports collaborative research to develop new methods for analyzing, generating, and optimizing graph-structured data. The project aims to create more expressive and efficient graph neural network models, improved generative models for graphs, and apply graph learning techniques to optimization problems and physical systems modeling. The...
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'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 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 awarded a $499,979 project grant to the George Washington University under the Computer and Information Science and Engineering program (CFDA 47.070). The grant will support research towards developing high-performance machine learning techniques on graphs from October 1, 2021 to September 30, 2024. The Computer and Information Science and Engineering program aims to advance computing and informatics research and education. This award will further those goals by...
This $599,573 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program supports the University of Notre Dame's research to develop a new machine learning paradigm for effective yet efficient foundation graph learning models (FGLMs). The project aims to create techniques, methods, and models for FGLMs that can be widely applied in areas like scientific research, social network analysis, anomaly detection, drug...
This federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $631,953 to Yale University to develop a general foundation model framework for graph-structured data in scientific discovery. The researchers will address key limitations in existing graph foundation models by incorporating novel approaches such as multi-level graph neural networks, graph signal processing, multimodal graph...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) project grant awards $600,000 to the Rector & Visitors of the University of Virginia (University of Virginia) to develop innovative approaches for efficient training of Dynamic Graph Neural Network (DGNN) models on large-scale, time-varying graphs. The 3-year project, from October 2024 to September 2027, aims to create novel methods for graph partitioning, sampling, caching,...
This Project Grant award of $600,000 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program supports research at the Massachusetts Institute of Technology (MIT) to develop a mathematical foundation for using graph data in machine learning tasks. The goal is to create principled methods for exploiting the latent geometry and structure underlying graph data, such as from social networks or protein interaction networks, to improve machine learning...
This National Science Foundation (NSF) Project Grant award, under the Mathematical and Physical Sciences program (CFDA 47.049), provides $379,999 to Michigan State University to develop novel graph-based semi-supervised learning techniques for machine learning tasks that require minimal labeled data. The research aims to address the challenge of limited labeled data availability, which is a key limitation of most machine learning methods. The proposed work involves developing three key...

This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award, with a total funding of $500,000, aims to design and develop scalable and efficient techniques for graph representation learning (GRL), particularly tailored for graph data stored in modern data lakes. The project has three primary objectives: 1) creating a partitioning-based framework to enhance the scalability of GRL, 2) developing methods to optimize the reading and partitioning of graph data from data lakes to improve the computational efficiency of GRL, and 3) implementing predictive optimization techniques to automatically select suitable GRL models, computational resources, and data lake configurations based on specific workloads. The research, conducted by Louisiana State University, has the potential to benefit fields such as AI, data management, biology, sociology, and cybersecurity by improving the processing and learning from large-scale graph data. The project will also support education by providing students with opportunities to engage in cutting-edge research and contribute to the field. This award, which runs from July 2025 to June 2028, reflects NSF's mission and has been deemed worthy of support through the Foundation's intellectual merit and broader impacts review criteria.

Generated 8/5/25, 5:58 AM