Yueqi Wang
Staff Research Engineer at Google DeepMind
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolYueqi Wang is a Staff Research Engineer in New York with nine years of experience building large-scale recommender systems and ML-driven discovery, most recently working on LLM-based generative recommender models at Google DeepMind after leading Shorts discovery and large recommender efforts at YouTube. With a PhD in Neuroscience and an MS in Computer Science, she brings a rare blend of experimental neuroscience, probabilistic modeling, and production ML engineering to problems in personalization. Yueqi has contributed to notable open-source ML work—implementing Neural Clustering Process components in TensorFlow’s Neural Structured Learning repo—illustrating both research rigor and practical demo-driven documentation. Colleagues rely on her ability to move ideas from academic prototypes to robust, user-facing systems at scale, and she explicitly avoids quant/trading roles.
9 years of coding experience
15 years of employment as a software developer
The University of UtahBachelor of Science - BS, Biological Sciences, 2nd Major in Psychology, Bachelor of Science - BS, Biological Sciences, 2nd Major in Psychology at Peking UniversityMaster of Science - MS, Computer Science, Master of Science - MS, Computer Science at Columbia University
Chinese, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (8)
neural-network10
keras10
machine-learning10
tensorflow10
python10
adversarial-learning9
graph8
regularization7
Programming languages (8)
Github contributions (5)
tensorflow/neural-structured-learning
Jan 2021 - Mar 2021
Training neural models with structured signals.
Role in this project:
ML Engineer
ML EngineerContributions:9 commits in 1 month
Contributions summary:Yueqi primarily contributed to the development and documentation of the Neural Clustering Process (NCP) models within the repository. Their work involved implementing the NCP model, fixing import statements, adding a setup file, and creating a demo Colab notebook using synthetic data. The user's commits also included fixing a warning message and modifying the Colab notebook to enhance user experience.
adversarial-learningautoencodergraph-learningstructureddeep-learning
yueqiw/ephys_analysis
Sep 2017 - Jan 2021
Processing and analysis of patch clamp electrophysiology data
Contributions:81 commits, 1 PR, 34 pushes in 3 years 4 months
connectivitypatchneurosciencepythonclamp
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