Hye Sun Yun

Papers

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About

Hye Sun Yun is a rising researcher at the intersection of human-robot interaction and natural language processing. Her work focuses on enabling robots to navigate the complexities of multiparty, co-located conversations—a critical step toward making social robots effective in real-world settings like meetings, classrooms, and collaborative workspaces. Her most-cited paper, "Improving Multiparty Interactions with a Robot Using Large Language Models" (2023, 23 citations), addresses a fundamental challenge: speaker diarization, or identifying who said what in a group. By integrating large language models, Yun’s approach allows robots to track dialogue flow, moderate participation, and personalize responses in real time, moving beyond simple turn-taking to genuine group awareness. This work has quickly gained traction, establishing her as a key voice in socially aware robotics. Her contributions are particularly notable for bridging the gap between LLM capabilities and the physical, time-sensitive demands of embodied interaction. For students and researchers, Yun’s research offers a compelling vision of how robots can become not just tools, but attentive collaborators in human group dynamics.