The National Science Foundation (NSF) awarded a $375,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of Virginia to develop a new method for designing human-machine interfaces (HMIs) that minimize human error. The project aims to develop a formal foundation of "fuzzy mental models" that can better capture the vagueness inherent in how human operators understand system states and functions. Specific objectives include:...
The National Science Foundation (NSF) awarded a $550,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program to the Rector & Visitors of the University of Virginia (University of Virginia) to conduct research on developing methods to improve the safety and reliability of robotic and autonomous systems (RAS) operating in complex real-world environments. The key objectives of the 3-year project are to: 1) create techniques to extract...
The University of Virginia received a $374,876 project grant award from the National Science Foundation to conduct research titled "COLLABORATIVE RESEARCH: DASS: ACCOUNTABLE SOFTWARE SYSTEMS FOR SAFETY-CRITICAL APPLICATIONS" from October 1, 2021 through September 30, 2024. The grant was awarded under the NSF's Computer and Information Science and Engineering program (CFDA #47.070), which supports investigator-initiated research and education in all areas of computing, communications,...
This $149,680 National Science Foundation project grant supports the development of technologies to improve human-human and human-machine teaming in clinical environments at Virginia Polytechnic Institute and State University from October 2022 through September 2023. Funded under the Computer and Information Science and Engineering program, the research aims to build understanding of team dynamics in intensive care units where data-powered technologies are increasingly utilized. Through...
This $115,440 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to develop new machine learning techniques to improve user interface (UI) engineering processes. The key goals are to: 1) create multimodal screen embeddings to better understand UI semantics, 2) develop automated documentation methods that summarize code in relation to app features, 3) leverage UI-related fault patterns to automate...
The National Science Foundation (NSF) awarded a $188,410 Project Grant under the Computer and Information Science and Engineering (CISE) program to the University of California, Berkeley. This 18-month grant, which began on January 1, 2024, supports the development of risk-aware interactive control and planning algorithms to achieve safe cyber-physical-human (CPS-H) systems. The key objectives of this research project are: (i) creating computationally-tractable risk-aware trajectory planning...
This $270,913 federal Project Grant award, funded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program, supports research to develop qualitative and quantitative methodologies for assessing the safety of learning-enabled autonomous systems. The project, led by the Augusta University Research Institute, Inc. (AURI), will target foundational challenges in capturing uncertainties from environments and providing timely, comprehensive, and...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) Project Grant award of $375,000 will fund research to develop tools to design provably safe autonomous systems. Key activities include: Developing inverse reinforcement learning algorithms to align an RL agent's norms with those of its designers, constrained by deontic logic. Systematically exploring the agent's norms to uncover unknowns and generate surprising...
This Project Grant award, totaling $659,809.00, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program. The award will fund research to develop a novel system that leverages artificial intelligence (AI) techniques to identify cognitive and affective states, behavioral patterns, and contextual factors contributing to medical errors. The goal is to provide transparent risk assessment and preventative...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award (CFDA 47.070) provides $395,927 to the University of California, Santa Cruz to develop theoretical and algorithmic foundations for building a safe and robust human-AI ecosystem. The 3-year project aims to: Develop an analytical framework to characterize the complex interactions between humans and AI systems, embedding safety components for both the AI learner and human agents....
This National Science Foundation Project Grant of $221,846 supported research at the University of Virginia from January 2022 through July 2023 under the Computer and Information Science and Engineering program.
The grant funded research to develop formal methods for evaluating human-machine interaction reliability in safety-critical systems. Researchers aimed to create a theoretically grounded method for scoring likelihood of human error in design interfaces. They also sought to introduce a theory of formal model repair for interactive systems to enable removal of problematic human-machine interface errors. The research team planned to validate advances through human subject experiments and formal proofs.
The work focused on improving methods for assessing human aspects of interfaces and applying formal methods to new contexts. Researchers intended to provide resources to help other scholars, designers, and engineers enhance reliability in cyber-human systems. The project's outcomes could help prevent injuries and save lives across various safety-critical domains.