Project Grant 2243850
- This National Science Foundation (NSF) Engineering (CFDA 47.041) Project Grant award to Rensselaer Polytechnic Institute (RPI) provides $439,539 over 3 years (2025-2027) to support research on computationally efficient graph neural networks (GNNs) with theoretical guarantees. The key objectives are to systematically analyze how graph topology and network architecture influence GNN performance, optimize computational and memory resources through techniques like graph data aggregation and...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program provides $203,796 to Texas A&M Engineering Experiment Station (Tees) for the project "COLLABORATIVE RESEARCH: PPOSS: LARGE: GENERAL-PURPOSE SCALABLE TECHNOLOGIES FOR FUNDAMENTAL GRAPH PROBLEMS". The project aims to accelerate the execution of large graph problems on distributed computing systems, with applications in computational biology, social...
- 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 three-year, $532,241 Project Grant from the National Science Foundation's Division of Computer and Network Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), will support the development of scalable algorithms, systems, and infrastructures for graph neural network training. The University of Massachusetts will develop a novel "split parallelism" training paradigm to transparently scale graph neural network training to large-scale graphs...
- 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...
- This federal Project Grant award for $300,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to empower Graph Neural Networks (GNNs), a powerful class of artificial intelligence models, by addressing key limitations related to data scale, distribution, and quality. The primary objectives are to: 1) develop graph condensation methods to significantly reduce data size while preserving critical information for efficient and accurate...
- This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $187,000.00 to the Massachusetts Institute of Technology (MIT) to develop mathematical foundations for understanding and improving graph neural networks (GNNs), which are widely used machine learning models for analyzing graph-structured data. The project aims to address key theoretical challenges with GNNs, including limited expressivity, suboptimal performance...
- 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...
- This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering program (CFDA 47.070) is for $291,261 over 3 years from January 1, 2024 to December 31, 2026. The grant is funding collaborative research by the Illinois Institute of Technology (IIT) to develop efficient graph neural network (GNN) algorithms and computation systems for both static and dynamic graphs. The key products and services to be delivered include: Design of novel,...
- This three-year Project Grant from the National Science Foundation's Integrative Activities program, totaling $535,066, will fund research at Tennessee State University from September 2022 through August 2025. The university will develop new algorithms using geometric graph theory and machine learning to analyze cryo-electron microscopy images of protein macrostructures. The goal is to automate the identification of secondary structure elements in proteins, which could advance modeling...
This Project Grant award from the National Science Foundation (NSF) Division of Information and Intelligent Systems (CFDA #47.070 - Computer and Information Science and Engineering) provides $599,939 to Texas A&M Engineering Experiment Station (Tees) to develop novel 3D graph neural network algorithms and architectures. The key objectives are to (1) create 3D graph neural networks that can efficiently and accurately capture the geometric properties of small molecules and proteins to generate informative representations, and (2) extend these 3D graph neural network models to enable hierarchical representation learning of the complex structures of proteins. The research aims to advance the state-of-the-art in geometric graph analysis and boost the performance of various real-world applications leveraging molecular and protein data. The project also includes efforts to broaden participation in AI and molecular analysis research and education through engagement with K-12 and underrepresented students.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $599.9k | 7/19/23 |