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About

Xiangqing Li is a pioneering researcher at the intersection of biomedical sensing, soft robotics, and intelligent automation. Their work spans three transformative domains: high-throughput biosensing for pandemic response, deep learning-driven robotic manipulation, and wearable health monitoring. Li’s most impactful contribution is a high-throughput fully automatic biosensing platform for efficient COVID-19 detection (26 citations), which revolutionized rapid viral screening during the global health crisis. In robotics, Li developed UG-Net, a real-time deep convolutional encoder-decoder network that enables open-loop robotic grasping using only depth images (7 citations), and advanced this work with a 2.5D image-based grasping system that fuses depth and RGB data for enhanced robustness (2 citations). Most recently, Li created a highly sensitive and flexible piezoresistive sensor based on MXene/LM@PDMS sponge for sedentary healthcare monitoring (5 citations), demonstrating expertise in soft materials for continuous physiological tracking. This sensor represents a significant leap in wearable technology for aging populations. Li’s research consistently bridges cutting-edge machine learning with practical hardware solutions, earning recognition for its direct societal impact—from pandemic diagnostics to assistive robotics and preventive healthcare. Their interdisciplinary approach positions them as a key figure in the next generation of intelligent sensing and autonomous systems.