Project Grant 2217076
- Kent State University received a $101,020 project grant from the National Science Foundation's Computer and Information Science and Engineering program to develop an efficient and scalable system for probabilistic graph learning, representation, aggregation, and analysis. The grant will support the design and implementation of formal probabilistic graph definitions, abstractions for graph manipulation, and a provable compiling process to enable distributed execution with quality guarantees....
- 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 $229,461 Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports the development and analysis of novel self-supervised probabilistic graph structure learning models. The goal is to uncover latent representations hidden within large datasets, which can provide valuable insights across diverse applications like cancer research and environmental analysis. The research will involve creating advanced mathematical models,...
- This Project Grant award of $160,000.00 from the National Science Foundation (NSF) Division of Mathematical Sciences, under the Mathematical and Physical Sciences (CFDA 47.049) grant program, will support a "Collaborative Research: Partial Priors, Regularization, and Valid & Efficient Probabilistic Structure Learning" project. The research aims to develop new statistical methods and frameworks for reliable uncertainty quantification in high-dimensional structure learning problems...
- This $875,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) is developing a probabilistic programming framework for modeling hybrid systems that combine continuous state evolution and discrete state changes. The project is applying this framework to domains such as epidemiology, medical devices, and autonomous systems, with the goal of enabling rigorous model-based decision-making. Key project...
- 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 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...
- Federal Project Grant Award Summary Louisiana State University received a $500,000 project grant from the National Science Foundation's Division of Information and Intelligent Systems under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), awarded July 15, 2025, with completion targeted for June 30, 2028. The project develops scalable and efficient techniques for graph representation learning (GRL) optimized for large-scale graph data stored in modern data lakes....
- 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 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 from the National Science Foundation's Division of Information and Intelligent Systems provides $148,749 to the University of Memphis for research titled "Collaborative Research: PPOSS: Planning: Efficient and Scalable Learning and Management of Distributed Probabilistic Graphs." The funding period is from October 1, 2022 to September 30, 2023. The award supports the exploration and development of an end-to-end system for efficient and scalable management of probabilistic graphs in distributed settings. Key activities include designing formal definitions and manipulation abstractions for probabilistic graphs, and developing a provable compiling process to guarantee correctness and efficiency of pipelined execution. Outcomes will include open-source software, publications, and workshop tutorials. The research framework aims to serve as a general-purpose probabilistic graph analysis tool benefiting multiple domains through comprehensive understanding of complex entity correlations. The work is part of the NSF's Computer and Information Science and Engineering program (CFDA 47.070) to advance computing and information science through investigator-initiated research and cyberinfrastructure development.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $148.7k | 7/12/22 |