Project Grant 2319511
- This $360,000 Project Grant from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports a collaborative research project between the Massachusetts Institute of Technology and the University of Washington. The project focuses on developing new machine learning-based approaches to quickly and accurately predict the performance of computer networks. The research aims to overcome the limitations of traditional network modeling...
- This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $720,000 from August 1, 2025 to July 31, 2028 to a collaborative research project between the Massachusetts Institute of Technology (MIT) and the University of Washington. The project aims to develop new machine learning models to quickly and accurately predict the performance of computer networks, which can help network operators...
- This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering program (CFDA 47.070) provides $600,000 in funding to The Trustees of Princeton University's Office of Research and Project Administration to develop innovative technologies for fine-grained network monitoring. The key objectives are to create a software component called a Telemetry Imputation Layer (TIL) that can recover detailed network performance data from limited sampling,...
- This $600,000 Project Grant award from the National Science Foundation's Division of Computer and Network Systems will support Purdue University's research to enable real-time, high-bandwidth network traffic analysis using machine learning on programmable switches. The project aims to develop novel methods for efficiently mapping popular ML models like decision trees and neural networks onto programmable switch hardware, as well as new switch primitives for computing flow statistics. This will...
- This National Science Foundation (NSF) award under the Computer and Information Science and Engineering (CFDA 47.070) program provides $500,000.00 to the University of Nebraska to develop an intelligent real-time traffic analysis framework and application-aware networking capabilities. The objectives are to: Integrate online-offline machine learning approaches for real-time network traffic analysis and prediction. Implement scalable techniques for analyzing Internet-scale network flow data in...
- The University of Chicago received a $250,000 Project Grant award from the National Science Foundation Division of Computer and Network Systems. The grant is part of the NSF's Computer and Information Science and Engineering program (CFDA 47.070), which supports investigator-initiated research and education in computing, communications, and information science engineering. Under this award, the University will conduct research modeling modern network traffic from October 2021 through September...
- The National Science Foundation (NSF) Division of Computer and Network Systems awarded Duke University a $300,000 Project Grant under the Computer and Information Science and Engineering program (CFDA 47.070). The goal of this 2-year EAGER (Early-concept Grants for Exploratory Research) project is to leverage an existing testbed in the Duke-Durham Research Triangle to investigate fiber sensing-based heterogeneous traffic monitoring and its integration with data communication networks. The...
- This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program totaling $600,000 will fund the development of learning-driven models and methodologies for next-generation cellular network internet measurements from October 1, 2022 to September 30, 2025. The Regents of the University of Minnesota, doing business as the Office of Sponsored Projects Administration, will utilize novel ensembles of Gaussian processes to model rich cross-layer...
- This $400,000 Project Grant, awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070), will support the University of North Carolina at Chapel Hill (UNC-CH) in implementing a prototype system for performing scientific data analysis using in-network and near-network computing resources. The SCIWIT (Scientific Data Analysis in Transit) project aims to leverage programmable network devices to perform...
- This Project Grant award, valued at $300,000.00 and spanning from June 2025 to May 2028, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070). The funding supports collaborative research led by Duke University to develop novel distributed processing algorithms that can efficiently analyze massive datasets while providing performance guarantees. The project focuses on expanding the Approximate...
This Project Grant award of $400,000 from the National Science Foundation's Division of Computer and Network Systems supports research at the University of North Carolina at Chapel Hill (UNC) to develop techniques for monitoring network traffic and estimating Internet performance experienced by users. The project aims to design deep learning frameworks for classifying end-user segments based on access networks, client platforms, and application usage, as well as online sampling, hashing, and sketching techniques to enable lightweight, passive analysis of network performance. The research also seeks to passively estimate whether network bandwidth constrains Internet transfers. This work is expected to transform understanding of how Internet properties differ across user segments, aid in network systems management, and provide training opportunities for students in data science and analysis of large datasets. The project will run from October 2023 through September 2026.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $400.0k | 8/15/23 |