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 $249,991 Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CFDA 47.070) program supports the development of an edge-friendly cyberinfrastructure that enables federated learning (FL) to be deployed for intelligent transportation system (ITS) applications in an efficient, secure, and privacy-preserving manner. The research aims to bring advances to transportation applications like naturalistic driving studies and traffic conflict...
This Project Grant award of $175,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the "UNITED LEARNING: DATA-SHAREABLE HETEROGENEOUS DISTRIBUTED LEARNING" research project at Southern Illinois University (SIU). The project aims to develop new methods for integrating low-resource computing devices, such as personal computers and IoT devices, into the training of complex deep learning models. The key...
This Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) provides $500,000 to Wayne State University to conduct research on trustworthy artificial intelligence (AI) and machine learning (ML) systems. The project aims to address critical challenges of privacy, robustness, and fairness in reinforcement learning (RL) algorithms, which are increasingly used in high-impact applications like healthcare, education, and large language models. The research focuses...
This $240,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) aims to develop computationally efficient algorithms for data removal in high-dimensional machine learning and AI models. The research will investigate methods to approximate model behavior when subsets of training data are removed, without full model retraining. This work supports improving the robustness, interpretability, and accountability of complex AI/ML...
This $271,343 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research into developing robust machine learning and inference methods that can withstand data corruption and distribution shifts. The project aims to explore new techniques for structured learning, supervised learning, and reinforcement learning that are resilient to these challenges, with potential applications in healthcare,...
This $500,000 project grant, awarded on January 1, 2024 by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), aims to address the urgent need for end-to-end safety in learning-enabled autonomous systems across various application scenarios, such as self-driving cars and urban air mobility. The project, titled "COLLABORATIVE RESEARCH: SLES: GUARANTEED TUBES FOR SAFE LEARNING ACROSS AUTONOMY ARCHITECTURES,"...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $174,998 to The Trustees of the Stevens Institute of Technology to investigate timing side channels in adaptive neural networks. The project aims to: (1) define a threat model for exploiting timing channels to gain sensitive user information, (2) develop a machine learning-based pipeline to utilize these timing channels, (3) create an...
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 $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...