Wei Chen

Member Of Technical Staff at Periodic Labs

San Francisco Bay Area United States
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Summary

👤
Senior
🎓
Top School
Wei Chen is an ML-focused Member of Technical Staff with 11 years of experience bridging academic AI4Science research and high-impact ads ML in industry. He holds a PhD in condensed matter physics and has authored 45+ publications across AI/ML, physics, materials, bio- and geo-science while co-leading early-career investigators at Harvard IMASC and guest lecturing in Harvard's chemistry department. In industry he has driven ads targeting, retrieval, multi-stage ranking, and integrity systems at scale—contributing to products that support billions in revenue and initiating LLM-based retrieval projects. Technically fluent in Python, SQL, Spark, GNNs, transfer learning and ranking, he combines production ML engineering with deep scientific modeling and database building. Now on the founding team at Periodic Labs after leading Ads ML at TikTok and Twitter, he uniquely translates complex scientific methods into robust, scalable ML products. An uncommon strength is his record of moving cutting-edge scientific ideas into productionized systems across both national lab and consumer-advertising environments.
code11 years of coding experience
job3 years of employment as a software developer
bookBS Theoretical Physics Special Class for the Gifted Young, BS Theoretical Physics Special Class for the Gifted Young at University of Science and Technology of China
bookPhD Condensed Matter Physics, PhD Condensed Matter Physics at University of Tennessee, Knoxville
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Github Skills (64)

minecraft10
pytorch10
filesystem10
python10
data-science10
machine-learning10
autoencoder10
deep-learning10
tensorflow10
command-line-tool10
autojump10
jax10
openlibrary9
plugin9
molecular-simulation9

Programming languages (5)

TypeScriptC++ShellJupyter NotebookPython

Github contributions (5)

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Accelerated sampling framework with autoencoder-based method
Contributions:3 releases, 1026 commits, 6 PRs in 3 years 9 months
autoencodermolecular-dynamicsneural-networkdeep-learningdata-augmented-autoencoders
Contributions:16 commits, 14 pushes, 1 branch in 2 months
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