Hao Chen
Staff Software Engineer at Meta
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolHao Chen is a Staff Software Engineer at Meta with a decade of experience building scalable, privacy-enhancing systems used by millions. He blends deep academic rigor—a PhD in Mathematics from the University of Washington—with hands-on research and engineering roles at Microsoft and Facebook, specializing in graph and data mining. Hao authored signaldemo.live to make end-to-end encryption concepts accessible, and contributes to prominent open-source graph learning projects like DeepWalk, where he modernized compatibility and tooling. Based in Seattle, he navigates both research and production engineering, turning complex cryptography and graph algorithms into deployable, user-facing systems.
10 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD Mathematics, Doctor of Philosophy - PhD Mathematics at University of WashingtonBachelor of Science (BS) Mathematics, Bachelor of Science (BS) Mathematics at Peking University
Github Skills (13)
scikit10
graph-algorithms10
machine-learning10
gensim10
python10
data-science10
scikit-learn10
develop9
deep-learning9
documentation8
nlp7
natural-language-processing7
algorithms7
Programming languages (2)
Github contributions (5)
phanein/deepwalk
Mar 2016 - Apr 2020
DeepWalk - Deep Learning for Graphs
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:15 commits, 12 PRs, 14 pushes in 4 years
Contributions summary:Hao contributed to the DeepWalk project by addressing compatibility issues with newer versions of Gensim and Scikit-learn, updating dependencies and code to accommodate the changes. They fixed an issue related to the psutil API usage and also added arguments to the scoring script to enhance functionality. Furthermore, they updated the README documentation, providing usage instructions and highlighting the compatibility requirements with different Gensim versions.
pytorchdeep-learning-for-graphsdeep-learningmachine-learninggraph-neural-networks
GTmac/HARP
Jan 2018 - May 2020
Code for the AAAI 2018 Paper "HARP: Hierarchical Representation Learning for Networks"
Contributions:13 commits, 3 PRs, 11 pushes in 2 years 3 months
pytorchrepresentationdeep-learningrepresentation-learninghierarchical
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