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About

Jingzheng Yao is a researcher specializing in computer vision, autonomous systems, and robotic perception, with a particular focus on underwater environments and intelligent navigation. His most impactful contributions lie in applying deep convolutional neural networks (CNNs) to the challenging domain of underwater image processing, where poor visibility, high pressure, and complex environmental conditions make automation both difficult and critical. His 2020 paper on underwater image processing and object detection has accumulated 171 citations, establishing him as a notable voice in deep-sea autonomous operation research. A companion work on marine organism detection and classification — targeting commercially valuable species such as sea cucumber, sea urchin, and scallop — further demonstrated the practical utility of his vision-based approaches, earning 79 citations. Beyond underwater systems, Yao has broadened his research into multi-modal sensor fusion, proposing a solid-state LiDAR-inertial-visual odometry framework for robust robotic mapping. His most recent work ventures into pedestrian trajectory prediction using latent bidirectional cooperative diffusion models, addressing safety-critical edge cases in autonomous driving. Together, these contributions reflect a researcher consistently pushing the boundaries of intelligent perception across diverse and demanding real-world environments.