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...
The National Science Foundation awarded a $584,714 project grant to the George Washington University under the Computer and Information Science and Engineering program (CFDA 47.070) to support research on expeditious computing from October 1, 2022 to September 30, 2025. The project aims to develop a holistic framework called "expedient computing" to balance the timeliness, accuracy, and freshness of results against the energy consumed in processing large volumes of data from sensor...
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...
The National Science Foundation (NSF) awarded a $484,822 Project Grant through its Computer and Information Science and Engineering (CFDA #47.070) program to the University of Chicago. This 5-year grant, effective July 1, 2023, supports research into characterizing the properties, reliability, and sensitivity of graph neural networks (GNNs) and advancing the theoretical understanding of statistical properties in graph estimators. The goal is to transform GNNs from black-box models into...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) grant (CFDA 47.070) awarded to George Washington University (GWU) provides $424,291 over 4 years to research the development of a novel dual-purpose photonic fabric that can enable both power-efficient on-chip data communications and high-performance neural network acceleration for future heterogeneous chiplet-based computing architectures. The key objectives are to: (1) provide high-bandwidth and...
The National Science Foundation (NSF) awarded a $469,787 Project Grant to Trustees of Boston University under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant funds the design and development of GNNSuite, a novel unified framework for scaling graph machine learning workloads on modern storage technology. Key project objectives include methods and tools for training and serving large graph neural network (GNN) models on larger-than-memory graphs without...
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 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,...
The National Science Foundation (NSF) awarded a $375,000 Project Grant to George Washington University (The) under the Computer and Information Science and Engineering program (CFDA 47.070) to conduct collaborative research on developing a cross-layer, machine learning-based, energy-efficient and resilient Network-on-Chip (NoC) design for multicore systems. The 3-year project aims to advance the understanding of the interactions between the NoC and other chip components, as well as optimize...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program grant of $599,951 awarded to Washington State University aims to develop a novel computing framework for accelerating graph neural network (GNN) computations using processing-in-memory (PIM) architectures. The key objectives are to: 1) establish an interdisciplinary research-based curriculum integrating PIM, machine learning, and data-driven design optimization, 2) motivate and engage...