The National Science Foundation (NSF) awarded a $400,000 Project Grant under the Computer and Information Science and Engineering (CFDA #47.070) program to the University of Illinois for a 4-year collaborative research project on privacy-preserving machine learning on graph-structured data. The project aims to develop innovative, efficient algorithms for training and updating large-scale graph neural network models while preserving the privacy of sensitive graph data across applications in areas...
The National Science Foundation (NSF) awarded a $179,055 Project Grant to the University of Virginia under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant supports collaborative research to develop privacy-preserving algorithms for fundamental problems in graph mining and network science. The project aims to create scalable, accurate graph differential privacy algorithms for applications like healthcare, social networks, finance, and computational...
The University of California, San Diego (UCSD) was awarded a $400,000 Project Grant by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program. The 4-year grant, starting on May 1, 2024, will fund research to develop innovative privacy-preserving machine learning algorithms for graph-structured data, which has widespread applications in areas like communication theory, computational biology, and social sciences. The project aims to establish a...
The National Science Foundation awarded a $200,657 Project Grant under the Computer and Information Science and Engineering (CFDA #47.070) program to the University of Maryland, College Park (UMD). The grant supports collaborative research to develop private algorithms for fundamental problems in graph mining and network science, such as subgraph detection, node ranking, community detection, and studying properties of graph dynamical systems like epidemic spread. The research aims to create...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) provides $600,000 in funding to the Massachusetts Institute of Technology (MIT) to develop a mathematical foundation for leveraging graph data in machine learning systems. The key objectives are to: 1) characterize how the geometry of the underlying latent space affects structural/combinatorial properties of graphs, 2) derive optimal...
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...
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 $500,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research into information-theoretic privacy and security for personalized distributed learning systems at the University of California, Los Angeles from March 2022 through February 2025. The grant aims to design personalized learning models that leverage large-scale collaborative data while maintaining individuals' privacy and requiring trust only in one's own...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Project Grant (CFDA 47.070) award for $241,682 to the Regents of the University of Michigan aims to develop a consolidated framework for computational privacy and machine learning. The project will create tools to protect data privacy in real-world machine learning applications while maintaining utility, fairness, and enabling distributed learning. Key innovations include differential privacy...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) project grant, awarded to North Carolina State University, is focused on enhancing privacy in federated learning, which is an AI approach enabling knowledge sharing without compromising data privacy. The $220,258 grant, awarded on October 1, 2024, aims to address vulnerabilities in federated learning schemes that may leak sensitive information through improper privacy...