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About

Baijian Yang is a researcher at the intersection of computer vision, robotics, and human-robot interaction, whose work addresses both environmental challenges and autonomous systems. He first gained significant recognition with his 2018 paper on algae detection using deep learning—his most cited work with 21 citations—which tackled the growing global crisis of harmful algal blooms by applying advanced image recognition techniques to environmental monitoring. Building on this, his 2019 follow-up introduced a multi-robot task planner for autonomous algae removal, demonstrating his commitment to translating detection capabilities into real-world remediation systems. Yang's research has since expanded into mobile robotics and localization, with his triangulation-based visual localization framework for field robots (2022, 16 citations) offering a practical GPS-alternative solution for autonomous navigation in complex environments. More recently, he has pursued human-multi-robot teaming, developing cognitive load-based workload allocation frameworks that enable more intelligent and adaptive collaboration between humans and robot teams. His 2024 work on hypergraph-augmented trajectory prediction further reflects his growing interest in socially aware autonomous systems. Across his career, Yang demonstrates a consistent drive to deploy AI and robotics in solving tangible, real-world problems spanning environmental science, autonomous navigation, and collaborative human-robot systems.