Yuliang Guo
Principal Applied Scientist at Maven Robotics
Redwood City, California, United States
Summary
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Rockstar🎓
Top SchoolYuliang Guo is a Principal Applied Scientist with nine years of experience building 3D vision and deep learning systems for robotics and autonomous platforms. He has led research teams at Bosch and now develops general-purpose robotic AI at Maven Robotics, focusing on methods that generalize across cameras and hardware embodiments. His work spans large-scale neural reconstruction for auto-labeling, vision-only parking assistance, AR-driven industrial assembly, and real-time posture estimation for AR devices. An active contributor to Apollo, he improved traffic light and laneline perception in a widely used open autonomous driving stack, demonstrating a knack for production-grade C++ and model integration. Trained with a PhD from Brown, he combines academic rigor in visual representation and sparse 3D reconstruction with practical deployment experience. Colleagues describe him as someone who bridges foundational research and engineering to solve the hardest perception problems in physical AI.
9 years of coding experience
13 years of employment as a software developer
Doctor of Philosophy (PhD) Computer Engineering, Doctor of Philosophy (PhD) Computer Engineering at Brown UniversityB.S Materials Science and Engineering; Electrical Materials, B.S Materials Science and Engineering; Electrical Materials at Shanghai Jiao Tong University
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
computer-vision10
machine-learning10
c-language10
apollo10
cprogramming-language10
autonomous-driving10
tensorflow9
Programming languages (4)
Github contributions (5)
ApolloAuto/apollo
Oct 2018 - Jun 2019
An open autonomous driving platform
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:19 commits, 13 PRs, 5 pushes in 8 months
Contributions summary:Yuliang primarily worked on improving the traffic light perception component within the Apollo autonomous driving platform. Their contributions involved resolving issues related to traffic light detection, including format corrections, parameter adjustments, and algorithm fixes in the C++ code. They also updated and integrated a laneline detection model, further enhancing the perception capabilities of the system and adding vanishing point detection using a neural network. The user's work demonstrates a focus on computer vision, machine learning model integration, and optimization of autonomous driving software.
autonomousmachine-learningautonomyautonomous-drivingapollo
yuliangguo/3D_Lane_Synthetic_Dataset
Oct 2019 - Aug 2022
Contributions:16 commits, 7 pushes, 10 comments in 2 years 10 months
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