The National Science Foundation (NSF) awarded a $290,000 project grant under its Engineering program (CFDA 47.041) to the University of California, Irvine (UC Irvine) for the period of September 1, 2024 to August 31, 2027. The project, titled "COLLABORATIVE RESEARCH: BLOG: A BI-LEVEL OPTIMIZATION FRAMEWORK FOR LEARNING OVER GRAPHS", aims to develop a unified bi-level optimization-based training framework for machine learning over graphs (LOGS) with automatic selection of...
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...
The National Science Foundation (NSF) awarded a $1,182,881 Project Grant to the University of California, San Diego (UCSD) under the NSF's Computer and Information Science and Engineering program (CFDA 47.070). The grant supports the development of new methods to ensure that machine learning models assigned to decisions such as lending and hiring can be changed through individual actions, protecting the right to access these services. The project will create techniques for (1) detecting...
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...
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 $184,208 Project Grant under its Computer and Information Science and Engineering (CFDA 47.070) program to the University of California, Santa Barbara (UCSB) to develop a "data preparation framework for end-to-end equitable machine learning." The project aims to investigate how biases can be mitigated when handling prevalent dataset issues such as missing values, heterogeneity, and data imbalance, and devise fairness-aware data...
The University of California, Santa Barbara (UCSB) was awarded a $439,984 Project Grant by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) to conduct research on mitigating bias in machine learning algorithms. The goal of the 3-year project is to develop theoretical frameworks and principled algorithms to enhance fairness in both static and dynamic machine learning-based decision-making systems. The research will investigate...
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 University of Virginia (UVA) received a $600,000 Project Grant from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) to advance federated graph machine learning (FGML) techniques. The project aims to 1) address data heterogeneity challenges in FGML, 2) develop novel algorithms to tackle label deficiency issues, and 3) strengthen data privacy protection for node attributes and graph structures. The research will produce...
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...