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About

Huaizu Jiang is a computer vision and robotics researcher whose work sits at the intersection of deep learning, real-time perception, and edge computing. His research focuses on two critical challenges in autonomous systems: obstacle detection and optical flow estimation — both foundational components for safe and efficient robot navigation. Jiang's most recognized contribution is StereoVoxelNet, a deep learning-based system for real-time obstacle detection using stereo cameras and occupancy voxels. By moving beyond traditional stereo matching techniques, this work addresses a safety-critical gap in robot navigation and has accumulated over 20 citations since its publication. His NeuFlow series represents another significant thread of innovation, tackling the longstanding tradeoff between accuracy and computational efficiency in optical flow estimation. NeuFlow demonstrated that high-accuracy optical flow is achievable on edge devices in real-time — earning 12 citations — while its successor, NeuFlow-V2, pushes these efficiency boundaries even further. Collectively, Jiang's body of work reflects a consistent drive to make sophisticated vision algorithms practical for deployment in real-world robotic environments. With a growing citation record across multiple recent publications, his research is gaining meaningful traction within the robotics and computer vision communities.