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I am an engineer with interests across robotics, control systems, autonomous systems, and AI. I received my M.S. degree in Systems, Control and Robotics from KTH Royal Institute of Technology and my B.S. degree in Electrical Engineering (Automation) from Zhejiang University. My work spans industrial control, verification, perception, planning, and machine learning. I have worked on powertrain actuator systems, vehicle software architecture, ROS-based perception pipelines, and autonomous navigation problems ranging from race cars to drones. Interests: Robotics, SLAM, path planning, reinforcement learning, computer vision, full-stack product building, and practical engineering for real-world systems. Miscellanea: Video editing and online content creation, magic and cardistry, bridge and Xiangqi, and films. Email: zihanqiao [AT] gmail [DOT] com |
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Developed and tuned MATLAB/Simulink control models for hybrid powertrain actuator systems across 16 functional units. I also tracked down parameter drift issues behind intermittent MiL/SiL failures and helped improve verification pass rates across multiple product variants. |
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Worked on vehicle software and integration problems for autonomous systems. I resolved signal timing and configuration issues across logs, tooling definitions, and DBC files, and also designed software architecture for an autonomous vehicle input/output module. |
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Built real-time ROS perception for stereo-camera cone detection and worked on MPC path tracking for an electric race car. This experience was a strong bridge between robotics research ideas and real-world autonomous system constraints. |
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Built a full-stack web app with Python/Flask and vanilla JavaScript, featuring a seed-based dealing system for reproducible shared sessions. The project also includes bilingual CN/EN support, mobile-first UI, and gameplay-oriented deck statistics. |
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Designed a DQN-based agent with rollout look-ahead in PyTorch for real-time traffic phase optimization. The method outperformed fixed-timing baselines and standard DQN in throughput and average vehicle delay across varying traffic densities. |
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Built an autonomous drone system in Python/ROS integrating landmark recognition, SLAM mapping, and A* path planning. The system completed a full pickup-and-return mission in a physical indoor obstacle environment. |
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Worked on SVM-based visual recognition and path generation on an autonomous car platform using Python and C. This project focused on perception-oriented autonomy in a compact research setting. |
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I enjoy bridge and Xiangqi, and I like the blend of probability, pattern recognition, memory, and decision making that strategy games bring. This part of my life also connects naturally with how I think about systems and problem solving. |
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I enjoy magic and cardistry as creative outlets. They are fun spaces for presentation, timing, rhythm, and audience connection, which feels very different from engineering but often exercises a similar attention to detail. |
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Beyond technical work, I enjoy video editing, online content creation, and films. I like projects that mix storytelling, visual communication, and technical craft, and I want this site to leave room for those parts of me too. |