Jianxiong Xiao
Founder And CEO at AutoX
San Jose, California, United States
Summary
👤
Senior🎓
Top SchoolJianxiong Xiao is the founder and CEO of AutoX, the leading RoboTaxi company in China, driving deployment of fully driverless autonomy across five global R&D centers. With a PhD in Computer Science from MIT and a decade of experience bridging academia and industry, he previously founded Princeton’s Computer Vision and Robotics Labs as an assistant professor. He combines deep technical expertise in computer vision and robotics with hands-on product leadership, scaling teams and operations to hire hundreds of engineers across software, hardware, and sensors. Jianxiong remains an active contributor to open-source ML tooling—his enhancements to PrincetonVision’s minimalist GPU ConvNet framework improved image handling, async prefetching, and CUDA compatibility. Based in San Jose, he runs a globally distributed engineering organization that supports remote work for U.S. hires while continuing to push real-world autonomous vehicle adoption. His career blends rigorous research, practical system-level optimizations, and bold commercialization of autonomy technology.
9 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Massachusetts Institute of TechnologyHong Kong University of Science and Technology (HKUST)
Github Skills (9)
cuda10
mask-rcnn10
computer-vision10
faster-rcnn10
opencv10
cprogramming-language9
machine-learning9
c-language9
gpu-programming9
Programming languages (2)
Github contributions (4)
PrincetonVision/marvin
Jun 2016 - Jan 2017
Marvin: A Minimalist GPU-only N-Dimensional ConvNets Framework
Role in this project:
ML Engineer
ML EngineerContributions:25 commits, 24 pushes, 3 comments in 7 months
Contributions summary:Jianxiong focused on enhancing the Marvin framework, a GPU-only N-Dimensional ConvNets Framework. They introduced an `ImageDataLayer` for image loading using OpenCV, enabling image processing capabilities. The user also optimized the framework by implementing asynchronous image prefetching and made adjustments for improved CUDA compatibility. Further work involved integrating a webcam demo and addressing bug fixes related to image handling and CUDA support.
pytorchcudagpuminimalistmarvin
jianxiongxiao/ProfXkit
Jul 2016 - Sep 2016
Contributions:5 commits, 4 pushes, 1 branch in 1 month
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