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Mun-Taek Choi is a robotics researcher whose work spans human-robot interaction, service robotics, and deep learning-based perception systems for mobile robots. His research has made significant contributions to the development of intelligent service robots, particularly in enabling robots to interact meaningfully with humans in real-world environments. Choi's early work focused on structured software engineering methodologies for service robots, with his UML-based development frameworks (2006, 2009) establishing foundational approaches still referenced by engineers today. He gained wider recognition through the development of the Engkey tele-education robot (2011, 43 citations), a pioneering platform for remote teaching assistance. His research expanded into cognitive health applications, with a landmark 2015 study (98 citations) demonstrating measurable structural brain changes in elderly participants following robot-assisted cognitive training — a finding with profound implications for healthcare robotics. More recently, Choi has led advances in deep learning-driven person-following systems, producing highly cited work on robust target tracking under occlusion and illumination challenges (2020, 100 citations; 2021). His investigations into ADHD screening and human engagement modeling further underscore his commitment to socially beneficial robotics. With over 400 cumulative citations, Choi's contributions represent a comprehensive and enduring influence on intelligent service robot research.