The National Science Foundation (NSF) awarded a $175,000 Project Grant under the Computer and Information Science and Engineering (CISE) program to the University of North Carolina at Charlotte. The project, titled "CRII: CSR: ENABLING ON-DEVICE CONTINUAL LEARNING THROUGH ENHANCING EFFICIENCY OF COMPUTING, MEMORY, AND DATA", aims to develop an efficient on-device continual learning framework that can incrementally learn new knowledge without forgetting prior learnt knowledge, while...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program grant award of $182,803 to the University of Connecticut (UConn) aims to develop a continual learning paradigm to address challenges in learning on evolving graphs. The project, titled "TOWARDS CONTINUAL LEARNING ON EVOLVING GRAPHS: FROM MEMORIZATION TO GENERALIZATION", seeks to enable intelligent agents to incrementally acquire, perceive, and exploit structural and temporal...
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...
This $112,067 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research by Northeastern University to strengthen the theoretical foundations of federated learning and enhance its resilience against data heterogeneity and system faults. The project aims to leverage the underlying statistical structures of federated datasets and design new algorithms to improve the reliability and performance of...
This $248,136 federal Project Grant, awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), will support the development of new AI methods for learning symbolic knowledge represented as computer code. The project aims to combine ideas from statistics, large language models, and program synthesis to create AI systems that can learn more abstract forms of knowledge from fewer examples and describe their knowledge in...
The National Science Foundation awarded a $245,043 Project Grant to Carnegie Mellon University under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The five-year award will support research towards theoretical foundations of neural network-based representation learning.
Specifically, the awardee will build a comprehensive theory for new neural network representation learning techniques. This includes characterizing statistical properties of...
This $197,997 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) will fund the development of a novel Safety-Centric Network Learning (SNL) framework at the University of Connecticut. The project aims to design reliable and stable network learning algorithms, representation models, and explainable generative methods to address safety challenges in AI systems deployed on real-world networks across...
This federal Project Grant award in the amount of $299,883 was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The project, titled "EAGER: INVESTIGATION OF DEVIATION AS THE SECOND OPTIMIZATION SIGNAL (SOS) FOR CLASSIFICATION IN ENSEMBLE LEARNING", aims to emulate the benefits of collaborative human learning by exploring ways for machine learning models to learn from each other's diverse...
This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) program aims to develop software frameworks that can efficiently serve and deploy machine learning models for a variety of AI-powered applications. The $600,000 award, spanning from October 2024 to September 2027, tasks the prime awardee, Georgia Tech Research Corporation, with creating agile mechanisms and policies to serve a family of AI models across...
The National Science Foundation (NSF) Engineering program (CFDA 47.041) awarded a 5-year, $544,381 Project Grant to the Regents of the University of Minnesota for the "CAREER: CONTINUAL LEARNING WITH EVOLVING MEMORY, SOFT SUPERVISION, AND CROSS-DOMAIN KNOWLEDGE" project. This research aims to develop a theoretical foundation and practical algorithms for continual learning, which enables artificial intelligence systems to maintain performance on previous tasks while seamlessly...