Wenzhe Li
Seattle, Washington, United States
Summary
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SeniorWenzhe Li is a staff software engineer and tech lead manager at Google, based in Seattle, with over a decade of experience building and shipping cloud-scale AI and data platforms. He currently leads Vertex AI training and hyperparameter tuning and oversees Vizier, combining hands-on engineering with strategic platform leadership to accelerate machine learning workflows at scale. Previously, he contributed to Google's Payments Data Warehouse, and before that gained industry experience through an AWS internship that exposed him to distributed data systems and large-scale analytics. He brings a strong academic foundation with an MS in Information Networking from Carnegie Mellon and a BE in CS from Nankai University. An active open-source contributor, his ML-focused GitHub work includes practical demonstrations of common algorithms (KNN, Naive Bayes, K-Means, Decision Trees, Random Forests) using Iris and spam detection tasks, reflecting a pragmatic approach to education and tooling. Known for turning complex ML and cloud infrastructure concepts into reliable, production-ready systems, he blends research curiosity with execution discipline to deliver measurable impact.
12 years of coding experience
Github Skills (12)
scikit10
decision-tree10
machine-learning10
jupyter-notebook10
randomforest10
k-means10
naive-bayes10
random-forest10
python10
scikit-learn10
data-analysis9
nlp8
Programming languages (3)
Github contributions (5)
GreedyAIAcademy/Machine-Learning
Jun 2019 - Aug 2019
讲解常见的机器学习算法
Role in this project:
Data Scientist
Data ScientistContributions:23 commits, 21 pushes, 1 branch in 1 month
Contributions summary:Wenzhe uploaded code and slides related to common machine learning algorithms, including KNN, Naive Bayes, K-Means, Decision Trees and Random Forests. Their contributions focused on implementing and demonstrating these algorithms using the Iris dataset and a spam detection problem, showcasing practical applications. The work involves data loading, model training, and evaluation of model performance, reflecting a focus on applied machine learning principles.
machine-learning
wenzheli/DCMLDA--Java-
Nov 2013 - Nov 2013
Java implementation of DCMLDA model
Contributions:12 commits in 2 days
java
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