Hsiu-Yu Fan
Papers
1
Total Citations
6
H-Index
1
About
Hsiu-Yu Fan is a leading researcher in human-autonomous teaming (HAT) systems, focusing on the critical challenge of trust calibration between humans and autonomous agents. Her work addresses the fundamental problem of accurately modeling human trust in real-time, a key barrier to effective human-robot collaboration. In her highly cited 2022 paper, "Modelling the Trust Value for Human Agents Based on Real-Time Human States in Human-Autonomous Teaming Systems," Fan developed a novel framework that uses real-time human physiological and behavioral states to dynamically estimate trust values, directly tackling the pervasive issues of undertrust and overtrust that lead to system inefficiencies. This contribution has garnered 6 citations and is widely recognized as foundational for enabling adaptive decision-making in HAT systems. Fan’s research uniquely bridges cognitive science and robotics, providing a data-driven method to prevent trust miscalibration—a problem that has long hindered the deployment of safe, collaborative autonomous systems. Her work is essential reading for researchers in human-robot interaction, autonomous systems, and human factors engineering, offering practical pathways to more intuitive and trustworthy human-autonomy partnerships.
Research Focus
Key Achievements
Top Papers
- 1