Zhen Wu
Course Assistant at Stanford University Department of Computer Science
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolZheng Wu is a Senior Staff Machine Learning Engineer in Sunnyvale with nine years of experience building production ML systems and data infrastructure across Meta, GoFundMe, TAL Education, and Stanford. He combines hands-on engineering—Spark/Scala pipelines, Redshift/Airflow orchestration, and back-end model training—with people leadership, having grown a Silicon Valley R&D team from 0 to 18 and mentored engineering managers. At Meta he focuses on feed modeling at scale, while earlier roles show a track record of driving product adoption, building company-wide experiment frameworks, and delivering executive-facing analytics. He’s an active contributor to DeepChem, enhancing dataset processing and model evaluation for drug discovery and computational chemistry, reflecting a strong interest in open-source scientific ML. Trained at Stanford (MS in Management Science & Engineering and CS), he blends research rigor with pragmatic product delivery and a knack for translating complex models into measurable business impact.
9 years of coding experience
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Peking UniversityMaster of Science - MS, Computer Science, Master of Science - MS, Computer Science at Stanford University
Github Skills (6)
machine-learning10
tensorflow10
python10
data-processing10
data-set10
datasets10
Programming languages (3)
Github contributions (5)
deepchem/deepchem
Nov 2016 - Oct 2020
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:394 commits, 62 PRs, 1 push in 4 years
Contributions summary:Zhen primarily contributed to the implementation of features for the DeepChem library, focusing on dataset processing and machine learning model training. They added functionality for creating new datasets and enhancing existing ones by incorporating features and labels. Additionally, the user incorporated different splitting and evaluation methods. The commits also show the user was adapting the codebase for a variety of machine learning models.
deep-learningquantum-chemistrydrug-discoverybiologymaterials-science
miaecle/deepchem
Oct 2016 - Jul 2020
Deep-learning models for Drug Discovery and Quantum Chemistry
Contributions:458 pushes, 155 branches in 3 years 9 months
deep-learningquantum-chemistry
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