Project Grant W911NF261A184

This three-year, $1.1 million project grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop highly scalable and sample-efficient spectral methods for learning graph topologies from high-dimensional data samples. The grantee, the Trustees of the Stevens Institute of Technology, will investigate spectral graph densification frameworks to efficiently estimate attractive Gaussian Markov random fields. A unique feature of the learned...

This two-year, $174,693 National Science Foundation project grant funds the development of a parallel and distributed framework for graph mining on graphics processing units (GPUs) at Rowan University from June 2023 to May 2025. The Computer and Information Science and Engineering program aims to advance computing and communications research and education. The project will create a new framework to accelerate fundamental graph computations across multiple GPUs for applications such as...

The National Science Foundation (NSF) awarded a $175,000 Computer and Information Science and Engineering (CISE) Program grant to The Trustees of the Stevens Institute of Technology to develop a compressor-assisted collective communication framework for large-scale deep learning on GPU-based systems. The two-year project aims to address challenges with the communication overhead of training massive deep learning models by investigating efficient lossy compression techniques for gradient data and...

This four-year Project Grant, awarded August 1, 2025, by the National Science Foundation's Division of Information and Intelligent Systems under the Computer and Information Science and Engineering program (CFDA 47.070), provides $400,000 in funding to Emory University to advance Graph Neural Network (GNN) technology through data-centric optimization strategies. The project will deliver research outcomes and methodologies addressing three critical GNN limitations: scalability, generalization...

Geometric Data Analytics Inc. was awarded a $199,898 Project Grant from the Department of Energy Office of Science on February 14, 2022 to develop distributed topological fingerprints for automating anomaly search and testing (DTFAAST). The grant was awarded under the Office of Science Financial Assistance Program (CFDA #81.049), which supports basic research to deliver scientific discoveries and tools that transform understanding of nature and advance U.S. energy security and economic...

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 $540,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to develop a comprehensive framework called FZ to enable scientific users to intuitively research, compose, implement, and test specialized lossy data compression techniques. The project will build on existing capabilities from various open-source data compression tools to create an intuitive cyberinfrastructure for the composition of specialized...

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 $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) Computer and Information Science and Engineering (CISE) program, CFDA 47.070, provides $568,008 to The Johns Hopkins University to explore novel graph machine learning approaches with non-canonical symmetries. The primary research activities include: Developing a scalable non-canonical graph representation based on intersecting cliques/communities, with applications to large graphs like social networks. Designing non-canonical...