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About

Danni Chang is a researcher specializing in human-robot interaction, robot appearance design, and user experience evaluation, with a particular focus on how visual design elements shape human perception of social and humanoid robots. Her most widely cited work, "The Trusted Listener" (2022, 16 citations), investigates how anthropomorphic eye design in social robots influences users' perceptions of trustworthiness — a critical factor in fostering natural and effective human-robot partnerships. This research highlights the outsized role that subtle visual cues, particularly eye design, play in shaping emotional and cognitive responses to robotic companions. Building on this foundation, Chang has pioneered the application of eye-tracking methodologies to robot design evaluation, as demonstrated in her 2020 study using global eye-tracking metrics to assess humanoid robot appearances. Her more recent work (2024) advances this further by integrating autoencoder-based machine learning with eye-tracking data analytics, enabling more objective and scalable assessments of user preference in robot design. Across her growing body of work, Chang bridges cognitive psychology, HRI design, and data-driven evaluation methods, offering researchers and designers valuable frameworks for creating robots that feel both trustworthy and visually intuitive to human users.