Zhen Qin
Staff Research Scientist at Google DeepMind
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolZhen Qin is a Staff Research Scientist at Google DeepMind's GenAI unit in New York with 12 years of experience building large-scale machine learning and LLM systems whose outputs have been integrated across Google products like Search, YouTube, Workspace and Assistant. He blends research and engineering, having tech-led cross-functional teams and authored 30+ top-tier papers on LLMs, retrieval, ranking and personalization. Prior roles include managing data science at Ticketmaster and impactful internships where he contributed core features to Vowpal Wabbit—adding human-readable model outputs and online holdout/bootstrapping support—demonstrating a long-standing focus on interpretability and online learning. He holds a PhD in Computer Science from UC Riverside and a track record of shipping production ML systems at scale while maintaining deep research rigor.
12 years of coding experience
2 years of employment as a software developer
B.E. Information Engineering, B.E. Information Engineering at Beijing University of Posts and TelecommunicationsDoctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at University of California, Riverside
English, Chinese
Stackoverflow
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0reached
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Github Skills (8)
machine-learning10
c-language10
cprogramming-language10
elearning9
feature-engineering9
interpretation9
active-learning8
bandit7
Programming languages (1)
Github contributions (4)
VowpalWabbit/vowpal_wabbit
Jul 2013 - May 2016
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:64 commits, 1 PR in 2 years 10 months
Contributions summary:Zhen implemented features related to truly readable model output and feature name logging, demonstrating a focus on improving model interpretability and debugging capabilities. They modified core C++ code, particularly in `gd.cc` and `parse_args.cc`, to add support for human-readable model output and feature name mapping. Furthermore, the user incorporated the --mask and holdout features indicating they were also working on training enhancements.
hashingtechniquescpppythonactive-learning
pierce1987/pcim-dev
Feb 2015 - May 2015
Contributions:3 pushes in 2 months
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