Chengxi Zhong
Papers
8
Total Citations
64
H-Index
4
About
Chengxi Zhong is a pioneering researcher at the intersection of deep learning, acoustics, and micro-robotics, whose work is redefining the possibilities of non-contact manipulation. His primary research areas include acoustic holography, physics-based deep learning, and automated micro/nano-robotic systems. Zhong’s major contribution is the development of AcousNet, a deep learning framework for generating dynamic 3D holographic acoustic fields from phased transducer arrays, enabling precise, contactless control of millimeter and sub-millimeter objects. His work on real-time acoustic holography, reinforced by physics-based and contrastive learning, has achieved high-fidelity acoustic field reconstruction for robotic manipulation, with his most cited paper garnering 36 citations. Notably, Zhong has extended these techniques to biomedical applications, including automated surgical knot tying on mini-incisions using dual-arm nanorobots—a breakthrough for minimally invasive surgery. His innovations in vision-based closed-loop control and spatiotemporal multiplexing for trapping multiple micro-particles further demonstrate his impact on advanced manufacturing and biomedical engineering. With a growing citation record and a focus on real-time, intelligent systems, Zhong is shaping the future of acoustic robotics and micro-scale automation.
Research Focus
Key Achievements
Top Papers
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