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.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $500.0k | 7/10/25 |