Dan Liu

Beijing University of Posts and Telecommunications, Chinese University of Hong Kong, Northeastern University

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About

Dan Liu is a versatile researcher whose work spans robotics, computer vision, sensor technology, and signal processing. With contributions bridging theoretical modeling and practical engineering applications, Liu has established a notable presence across several high-impact domains. His 2018 work on deep learning-based smart radar vision systems for object recognition stands as his most influential contribution, accumulating 22 citations and demonstrating his early commitment to integrating artificial intelligence with sensing technologies. In robotics, Liu's 2019 paper introducing a complete relative pose error model for robot calibration — garnering 17 citations — addressed a critical challenge in industrial automation by accounting for both relative distance and rotational errors, meaningfully advancing calibration precision. His earlier research on compressed iterative particle filters for video-based target tracking reflects a sustained interest in intelligent perception systems dating back to at least 2011. More recently, Liu has expanded into flexible electronics, contributing to the growing field of wearable piezoresistive sensors through investigations of carbon nanofiber composites for health monitoring and human-machine interfaces. Collectively, his interdisciplinary body of work reflects a career dedicated to advancing intelligent, adaptive systems across robotics, sensing, and materials engineering.