Xīan Zhāng
Distinguished Engineer at LinkedIn
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolXīan Zhāng is a Distinguished Engineer with 11 years of experience building data- and ML-driven products and scaling teams that bring research talent into production at companies including LinkedIn, Uber, and Pinterest. He holds a PhD in Machine Learning from Duke and combines deep academic rigor with hands-on engineering — from contributing to Photon-ML on Apache Spark to leading cross-functional applied science teams. Xīan excels at closing the gap between business objectives and state-of-the-art models, growing engineers and scientists into lasting product builders. He also serves on program committees and reviews for top ML conferences, signaling active engagement with the research community beyond product delivery. Based in San Francisco, he is known for pragmatic code stewardship and for fostering career growth in colleagues while shipping measurable business impact.
11 years of coding experience
16 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Machine Learning, Doctor of Philosophy (Ph.D.) Machine Learning at Duke UniversityBachelor's Degree Computer Science, Bachelor's Degree Computer Science at Nanjing University
Stackoverflow
1reputation
2kreached
0answers
2questions
Github Skills (15)
apache-spark10
machine-learning10
scala10
performance-optimization9
back-end-development9
algorithms8
algorithm8
implement8
data-structure7
data-structures7
xcode6
htaccess6
ios6
php6
webhosting6
Programming languages (4)
Github contributions (5)
linkedin/photon-ml
Mar 2015 - Nov 2016
A scalable machine learning library on Apache Spark
Role in this project:
Back-end Developer
Back-end DeveloperContributions:137 commits, 86 PRs, 26 pushes in 1 year 9 months
Contributions summary:Xīan's commits primarily focused on refining and improving the machine learning library Photon-ML, specifically within the context of the GAME project. They removed unnecessary print statements, which suggests code cleanup and optimization. The user also addressed issues related to warm-start training and made improvements to core components like data loading and model implementations by reverting code changes impacting warm-start training in Photon and ensuring correct behaviour.
scalabledata-sciencemachine-learningapachespark
XianXing/photon-ml
Apr 2016 - Apr 2016
Contributions:2 pushes in 11 days
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