The National Science Foundation awarded a $515,999 Project Grant to the University of Notre Dame under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to develop comprehensive methods for learning to augment graph data through machine learning algorithms. Over a three-year period from March 2022 to February 2025, the University will deliver novel techniques to augment graph data by counterfactual inference on edges as treatment variables, forecasting...
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 $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 $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 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $160,338 to the Illinois Institute of Technology (IIT) to develop new methods for analyzing, generating, and optimizing graph-structured data. The 3-year project aims to advance graph neural network models and their applications in areas such as social network analysis, molecular design, and physical systems modeling. Key research thrusts...
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...
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 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 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $316,000 in funding to The Leland Stanford Junior University (Stanford University) to develop a general foundation model framework for scientific discovery using graph-structured data. The research aims to address key limitations in existing graph foundation models, such as the inability to handle complex graph structures or...
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 $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 discovery, and e-commerce. Key objectives include designing multi-task self-supervised graph learning, multi-graph co-training frameworks, and mixture-of-expert based meta-learning to enable strong task generalization, cross-graph learning, and cross-domain knowledge transfer for FGLMs. The project outcomes, including open-source code, benchmark data, and models, will be made publicly accessible to advance research and real-world applications leveraging graph-structured data.