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About

Dewen Hu is a multidisciplinary robotics and computational intelligence researcher whose work spans brain-computer interfaces (BCIs), autonomous robotics, and reinforcement learning. Based at a leading Chinese research institution, Hu has built a distinguished career bridging neuroscience-inspired systems with practical engineering applications. His most impactful contribution lies in BCI-driven assistive technology, particularly his work on EEG-based smart wheelchair systems (118 citations), which opens transformative possibilities for individuals with motor disabilities. Complementing this, he has pioneered novel BCI paradigms including Morse code-inspired motor imagery classification and tactile ERP-based communication interfaces, broadening accessibility beyond visually dependent systems. Hu's early work on bionic neural networks for fish-robot locomotion demonstrated his talent for translating biological principles into robotic control, a theme that persists throughout his career. His research has since expanded into visual navigation, pose-only 3D scene reconstruction, symmetry-aware robotic grasping, and scalable multi-robot coordination through distributed model predictive control — reflecting an impressive breadth of expertise. With contributions spanning reinforcement learning, mobile robot tracking control, and human-machine interaction, Hu's cumulative body of work represents a sustained effort to develop intelligent, adaptive systems that operate effectively in complex real-world environments.