Project Grant FA23862614005

This $300,000 Project Grant from the National Science Foundation Division of Civil, Mechanical, and Manufacturing Innovation, under the Engineering federal grant program (CFDA 47.041), will support research at Virginia Polytechnic Institute and State University to develop a cost-sensitive federated artificial intelligence framework called COFEDAI. The goal is to facilitate data sharing and aggregation across multiple manufacturers to improve AI and machine learning for smart manufacturing...

Carnegie Mellon University received a $148,819 project grant from the Air Force Research Laboratory (AFRL) under the Air Force Defense Research Sciences Program (CFDA 12.800) effective July 1, 2025, through June 30, 2028. The award supports research into secure and efficient federated learning in the foundation model era. Federated learning represents a distributed machine learning approach that enables model training across decentralized data sources while maintaining data privacy and...

This $348,573 project grant from the National Science Foundation's Division of Electrical, Communications and Cyber Systems, under the Engineering federal grant program (CFDA 47.041), will fund research at North Carolina State University from September 2022 through August 2025. The university will advance the frontiers of federated learning through exploring tradeoffs among learning performance, communication efficiency, privacy protection, and system robustness under a generalized...

This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Project Grant award, with a total funding of $174,770, supports the development of an adaptive, federated, continuous learning system that uses a novel federated, semi-supervised learning framework. This framework aims to retrain deep neural network models on distributed, unlabeled, heterogeneous data from edge devices, while leveraging explainable AI techniques to expedite local training. The...

The National Science Foundation awarded a $400,000 Project Grant to Stanford University under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The three-year award will support research and education activities to develop conceptual and mathematical understanding of large language models. Specifically, the university will conduct research analyzing simplified generative text models and language models trained on such data to gain insights into their inner...

This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program grant award of $588,040 to the University of Pittsburgh will support research on multilingual object detection methods that can better handle shifts in geographic domain and visual concepts. The 3-year project aims to develop techniques that leverage language models and diverse cultural knowledge to improve object detection performance in target domains with limited data, such as Africa. Key...

This Project Grant from the National Science Foundation's Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), provides $300,000 to Arizona State University to develop a machine learning framework for training models across hospitals on electronic health records without sharing patient data. The framework aims to address fairness and mitigate biases by training representation learning algorithms jointly across multiple...

The National Science Foundation awarded a $299,569 EAGER (EArly-concept Grants for Exploratory Research) Project Grant to the University of Rochester to develop an approach that leverages large language models and artificial intelligence to accelerate the discovery of earth-abundant, active, and selective catalysts for the reverse water-gas shift reaction. The project aims to demonstrate that language-based representations can be universally applied to materials discovery processes expressed...

The National Science Foundation awarded Auburn University a $498,762 project grant under the Engineering program (CFDA 47.041) to develop federated deep learning techniques for distributed additive manufacturing. The two-year award beginning September 1, 2021 will support Auburn University's work to create a federated deep learning framework that enables ubiquitous distributed additive manufacturing across networked nodes. Auburn University will collaborate with sub-awardees the University of...

This three-year National Science Foundation project grant of $599,974 will fund research to develop practical analyses and safe transformations for imperative deep learning programs. The awardee is the Research Foundation of the City University of New York on behalf of Hunter College. The project aims to increase the robustness, reliability, and scalability of deep learning systems through novel analyses and refactorings that will migrate legacy deferred execution code to more robust...