Ye-Ji Mun

Papers

Total Citations

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About

Ye-Ji Mun is a rising researcher at the intersection of human-robot interaction and autonomous systems, with a focus on how robots can effectively influence human behavior over extended periods. Her most cited work, "Towards Robots that Influence Humans over Long-Term Interaction" (2023, 11 citations), tackles the critical challenge of designing robots that can shape human actions—such as an autonomous car adjusting its driving to influence a nearby human driver—while maintaining trust and cooperation. This paper, along with an earlier 2022 version (3 citations), establishes her as a key voice in understanding the dynamics of long-term robot influence, moving beyond short-term, one-off interactions. Mun also contributes to improving robot teleoperation through her work "Specifying Target Objects in Robot Teleoperation Using Speech and Natural Eye Gaze" (2023, 3 citations), which proposes an intent detection framework that reduces cognitive load by combining natural eye gaze and speech commands. Her research is particularly impactful for applications in autonomous driving, assistive robotics, and collaborative manufacturing, where seamless human-robot coordination is essential. With a growing citation footprint, Mun is shaping how robots become not just tools, but intelligent partners capable of adaptive, long-term influence.