About
Dr. Luyang Zhao is an Assistant Professor in the Department of Electrical and Computer Engineering at Clemson University and directs the SMILE Robotics Lab. She received her Ph.D. in Computer Science from Dartmouth College in 2025. Dr. Zhao’s research focuses on soft robotics, embodied intelligence, modular robotic systems, and human-centered robotic interaction. Her work combines robotic design, sensing, perception, and learning to create systems that can safely and adaptively interact with people and complex environments. An emerging direction of her research centers on health-related applications, including soft wearable devices, robot-assisted training systems, and intelligent feedback mechanisms for medical education, assistive technologies, and rehabilitation-supportive applications. She is particularly interested in how soft interfaces, embodied AI, and data-driven assessment can improve procedural training, skill evaluation, and safe interaction between humans and robotic systems. Through Clemson-based and external interdisciplinary collaborations, Dr. Zhao is building a health research program at the intersection of robotics, medical devices, human factors, and health-oriented training technologies.
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How their research is transforming health care
My research aims to transform health and health care by creating robotic and intelligent systems that improve how people learn, perform, and receive physically interactive support. Many important healthcare activities, from procedural training and surgical education to rehabilitation and assistive support, depend on accurate movement, timely feedback, and safe interaction between people and technology. I address these needs by combining soft robotics, wearable sensing, embodied intelligence, and robot-assisted training to build systems that can monitor actions, detect deviations, and provide adaptive guidance. One emerging direction is the development of soft wearable interfaces and robotic teaching platforms for medical education, where trainees can receive real-time feedback on technique, consistency, and error patterns during skill acquisition. Over time, such technologies could help standardize training, reduce avoidable errors, improve learner confidence, and support more scalable instruction. More broadly, I aim to develop health technologies that are not only technically capable, but also usable, interpretable, and responsive to the needs of clinicians, trainees, and patients.
Health Research Expertise Keywords
Soft Robotics, Wearable Robotics, Medical Devices, Medical Education, Surgical Skill Training, Robot-Assisted Training, Procedural Guidance, Human Factors in Healthcare, Human Performance, Rehabilitation Technologies, Embodied AI, Health Data Analytics, Assistive Robotics, Intelligent Feedback Systems
