Lei Tai
Team Lead at Horizon Robotics
Shanghai, Shanghai, China
Summary
🤩
Rockstar🎓
Top School
Top expert inAutonomous Robotics and Simulation Technology
Lei Tai is a robotics and deep learning team lead with 11 years of experience, currently leading a team at Horizon Robotics in Shanghai after senior engineering and management roles at Huawei and Alibaba. He is a Ph.D. student in Electronic and Computer Engineering at HKUST with several top-tier publications, blending rigorous academic research with product-focused engineering. Proficient in Python and C++, he has hands-on expertise in localization, SLAM, path planning and control—demonstrated by contributions to the CppRobotics repository where he refined EKF-based localization and integrated OpenCV visualizations. His background spans both industry and international research stints (Universität Freiburg, CityU), enabling him to translate cutting-edge algorithms into deployable autonomous driving solutions. Known for bridging algorithmic depth with practical system design, he often surfaces research insights into production code and visualization tools that accelerate team delivery.
11 years of coding experience
4 years of employment as a software developer
Master's degree Materials Processing Engineering, Master's degree Materials Processing Engineering at Harbin Institute of TechnologyHong Kong University of Science and Technology (HKUST)
English, Chinese
Github Skills (6)
algorithms10
localization10
robotics10
c-language10
cprogramming-language10
opencv9
Programming languages (11)
Github contributions (5)
onlytailei/CppRobotics
Mar 2019 - Jun 2020
cpp implementation of robotics algorithms including localization, mapping, SLAM, path planning and control
Role in this project:
Back-end Developer
Back-end DeveloperContributions:109 commits, 4 PRs, 38 pushes in 1 year 3 months
Contributions summary:Lei primarily contributed to the implementation of robotics algorithms within the C++ codebase, specifically focusing on localization using an Extended Kalman Filter (EKF). Their work involved adding, updating, and refining the EKF implementation, including defining motion and observation models, and integrating visualization features using OpenCV. The contributions demonstrate a strong understanding of robotics concepts and the practical application of Kalman filtering techniques.
roboticscpppath-planningrobotics-algorithmsmapping
gaozhihan/weak-seg
Mar 2019 - Mar 2019
Contributions:37 commits in 1 day
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