Paul Xiong
Founder Of Open Source Cervical AI at Cervical AI Open Source
San Ramon, California, United States
Summary
👤
Senior🎓
Top SchoolPaul Xiong is a seasoned software leader and founder with 14 years of experience building applied AI and embedded systems, currently leading the open-source Cervical AI initiative from San Ramon, California. He combines deep hands-on engineering—spanning lightweight vision models for iOS/macOS, AutoML, and embedded Linux—with product-first startup instincts honed over decades as a CTO. His teams have shipped commercial AI camera and router products, and he has driven projects that processed large-scale video streams, ReID/face recognition, and mesh Wi‑Fi optimizations. Motivated by real-world health impact, he pivoted to cervical cancer screening AI, achieving 98% recall/precision on a dataset of 9,000+ patient images while tackling hard problems like overlapping-cell segmentation and extreme class imbalance. Paul prefers small, high-velocity teams of 5–6 engineers and is more energized by coding and product building than by traditional executive duties. He brings rare cross-domain experience—from kernel and network drivers to mobile vision models—paired with a pragmatic, research-informed approach to shipping life-saving ML systems.
14 years of coding experience
23 years of employment as a software developer
B.S, Computer science & Engineering, B.S, Computer science & Engineering at Beihang University
Github Skills (59)
data-science9
ui-components9
python9
machine-learning9
gradio-interface9
deep-learning9
data-analysis9
data-visualization9
gradio9
programming-language8
golang8
nodejs7
browser7
go7
streaming7
Programming languages (4)
Github contributions (5)
paulxiong/SamSung-Galaxy-Tab-10.1-Model-GT-P7510
Sep 2011 - Sep 2011
Contributions:5 commits in 1 day
paulxiong/SimCLR-4
Mar 2020 - Mar 2020
PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations by T. Chen et al.
Contributions:7 commits, 7 pushes in 2 days
pytorchsimclrrepresentationssimple-frameworkdeep-learning
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