Xiangquan Xiao
Founder at Xpeng Inc.
Brookline, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top School
Top expert inAutonomous Robotics and Computer Vision Development
Xiangquan Xiao is a founder and seasoned software leader with 15 years building data, cloud, and ML infrastructure for autonomous systems and large-scale search. He led core data and hybrid cloud platforms for autonomous driving at XPENG and Baidu USA, and was a top contributor to the widely used Apollo open autonomous driving project. Prior experience at Google on web indexing and big data pipelines underpins his strength in reliable, production-grade distributed systems. Now based in Brookline, MA, he’s bootstrapping a stealth robotics startup while also authoring technical writing for broader audiences. His background blends hands-on backend engineering with strategic platform design, frequently migrating and hardening prediction and pipeline components to leverage core framework utilities. Trained at Peking University, he combines academic rigor with practical delivery across research-grade and product-focused environments.
14 years of coding experience
9 years of employment as a software developer
MS, Computer Science, MS, Computer Science at Peking University
Github Skills (7)
c-language10
cprogramming-language10
fileio9
file-access9
file-processing9
file-handling9
software-design8
Programming languages (6)
Github contributions (5)
ApolloAuto/apollo
Jul 2017 - Nov 2020
An open autonomous driving platform
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1 release, 44 reviews, 2983 commits in 3 years 4 months
Contributions summary:Xiangquan's commits primarily involve modifying code related to prediction tasks within the Apollo autonomous driving platform. The changes show migration of existing code, specifically around prediction components, to utilize "cyber file util". These changes are evident in the code diffs and messages, which showcase that the user has been working on the migration of modules such as those dealing with lane sequences and also modifications in the traffic light functionality. The code changes suggest a focus on utilizing core framework utilities and adapting existing code to changes within the project.
autonomousmachine-learningautonomyautonomous-drivingapollo
xiaoxq/toolkit
Jan 2015 - Sep 2019
Contributions:195 pushes in 4 years 9 months
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