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About

Zengxiang Zhou is a researcher whose work sits at the intersection of astronomical instrumentation, robotics, and precision engineering. Best known for his contributions to fiber positioning systems in large-scale spectroscopic telescopes, Zhou has played a significant role in advancing the technology underpinning major observational facilities, including LAMOST, BigBOSS, and the Thirty Meter Telescope (TMT). His research addresses critical challenges in robotic fiber positioner accuracy, calibration, and motion planning — work that directly enables the simultaneous observation of thousands of celestial objects, dramatically improving survey efficiency. Notable contributions include the application of deep learning for fiber positioning unit detection (2021), the development of multi-CCD camera measurement systems (2014), and the use of the Differential Evolution Algorithm for positioner calibration (2024), each garnering meaningful citation counts within the specialized field. Zhou has also explored parallel and hybrid robotic mechanisms for TMT's mask exchange system, demonstrating versatility across serial and parallel robotic architectures. More recently, his research has expanded into human-computer interaction, applying GMM-HMM models to eye-tracking systems. With a consistently interdisciplinary approach, Zhou's body of work represents a sustained commitment to precision robotics in both scientific and assistive technology contexts.