Ph.D., Industrial and System Engineering, Ohio State University
Ph.D. Candidate (expected 2019)
Yuzhi’s dissertation focuses on the temporal attentional limitations of human operators, such as attentional blink and change blindness, and how these limitations interfere with the detection of multi-modal signals in temporal proximity. The result of his research will inform the design of alert systems in many complex domains. He is also actively involved in several research projects on multi-modal interfaces design for modern flight decks, autonomous vehicles and drone control stations.
Ph.D. Candidate (expected 2020)
Yidu Lu is a third-year PhD in ThinC lab. Over the past two years, she has worked on the system complexity project funded by Federal Aviation Administration. In this project, she focused on pilots’ interactions with complex flight deck technologies through reviewing regulatory materials, sending out surveys, organizing focus groups and conducting accident analysis. The goal of this project is to gain a better understanding of, and ultimately help minimize the detrimental effects of complexity. She is also working on the dissertation topic related to human-automation trust and eye tracking. Heer goal for this research is to develop a process-oriented method to infer trust by using eye tracking and apply this technique to better study trust calibration process.
Ph.D. Pre-Candidate (expected 2020)
My research focuses on trust and transparency in high tempo, high risk, human-robot systems. I am interested in how interfaces may be designed to support trust calibration and the development of a shared mental models in human-robot teams. My first study examined the efficacy of visual and auditory representations of system confidence for supporting trust calibration and performance in the context of a UAV-supported target detection task. It was found that while targets identified with high confidence led to faster response times, visual representations of confidence in the experiment led to slower response times as compared to auditory representations. Future work will research how a system may represent internal process to facilitate transparency, support trust calibration, and improve human-machine performance.
Derek Witcpalek, B.S.E., Computer Science (expected Winter 2020)