Project Grant 2227002

Award Date 1/1/22
Completion Date 12/31/22
Dollars Obligated $304K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Tempe, AZ 85281, USA
Similar Awards
Arizona State University was awarded a three-year $500,000 project grant from the National Science Foundation to develop neuro-symbolic learning and control tools for cyber-physical systems. The grant is funded through the NSF's Computer and Information Science and Engineering program, which supports research and education in all areas of computing, communications, and information science and engineering. Under this award, Arizona State University researchers will integrate machine learning,...
This Project Grant award of $353,369.00 was made by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program. The award will support research at Arizona State University (ASU) to address critical challenges in applying reinforcement learning to real-world urban environments. Key aspects of the research include developing actionable data analytics for urban decision-making, tackling issues like noisy/incomplete...
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,...
The National Science Foundation (NSF) has awarded a $850,000 Project Grant under its Computer and Information Science and Engineering (CFDA 47.070) program to the University of California, Los Angeles (UCLA) to develop an efficient human-in-the-loop learning framework for human-centric cyber-physical systems (CPS). The 3-year project aims to create a novel approach to integrate human oversight and intervention into the training of CPS agents, such as assistive driving and exoskeleton systems, to...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) award of $350,000 to North Carolina State University (NC State) will develop an efficient human-in-the-loop learning framework for human-centric cyber-physical systems (CPS). The project aims to create a learning approach that incorporates a human subject to monitor the learning agent and intervene when unsafe behavior occurs, demonstrating the correct actions....
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) award of $300,000 to Arizona State University (ASU) from August 2024 to July 2027 aims to enhance the performance of reinforcement learning (RL) systems in completing difficult tasks in complex environments. The project seeks to develop task and environment representations specifically for active design in RL, including: 1) Active Environment Design for RL to...
This $706,868 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports a collaborative research and education initiative between Arizona State University (ASU) and the AI Institute for Foundations in Machine Learning (IFML). The project aims to develop robust, interactive, and embedded machine learning algorithms for deploying AI-enabled pervasive systems in real-world settings, such as healthcare monitoring and...
This National Science Foundation award of $247,721 provides funding under the Computer and Information Science and Engineering program (CFDA 47.070) to Arizona State University for a project titled "CAREER: Autonomous Wearable Computing for Personalized Healthcare." The project aims to develop foundations for computational autonomy in wearable-based health monitoring and interventions. Specifically, the university will investigate methods for automatically and autonomously labeling...
This National Science Foundation (NSF) Division of Information and Intelligent Systems Project Grant aims to advance "few-round active learning" algorithms and enable more efficient training of supervised machine learning models. The $300,000 award to Virginia Polytechnic Institute & State University (Virginia Tech), running from August 1, 2023 to July 31, 2026, will support research to: 1) develop methods for quantifying the utility of unlabeled data for active learning tasks, and...
Arizona State University was awarded a $299,997 project grant from the National Science Foundation Office of International Science and Engineering to conduct research titled "IRES TRACK 1: SENSOR INFORMATION PROCESSING AND MACHINE LEARNING FOR WEARABLE DEVICES." The grant period runs from July 1, 2021 through June 30, 2024. Under this award, Arizona State University will develop techniques for sensor information processing and machine learning to enable new applications for wearable...

This National Science Foundation Project Grant award of $303,891 supports research at Arizona State University to advance machine learning for human-in-the-loop cyber-physical systems. Under the Computer and Information Science and Engineering program, the award funds development of mixed-initiative solutions to enable accurate learning of human behaviors in uncontrolled environments through mobile and wearable devices. Key objectives include investigating combinatorial approaches to maximize active learning performance given data informativeness, labeling burden, and label reliability. The university will also construct a vocabulary of complex behaviors using knowledge graph embedding and semi-supervised techniques, and develop network-graph algorithms to infer behaviors. Proposed work involves validating off-line and real-time active learning, behavior vocabulary construction, and behavior inference methods through both laboratory experiments and user studies.

Generated 1/6/24, 11:30 PM