Le Zhang
Senior Manager at AMD
Singapore
Summary
🤩
Rockstar🎓
Top SchoolLe Zhang is a Data Science Senior Manager based in Singapore with nine years of experience applying cutting-edge ML and AI to enterprise-grade systems across industries including recommendation, smart manufacturing, and financial services. He has led teams at AMD, Standard Chartered, and Microsoft to build scalable cloud-native AI platforms and production-ready solutions, and co-led Microsoft/Recommenders—one of the most popular open-source recommender-system hubs on GitHub. A PhD-trained engineer and former Purdue visiting scholar, he blends academic rigor with pragmatic engineering, publishing in top venues and chairing workshops on scalable recommender systems. Colleagues rely on him for end-to-end delivery from data engineering and splitter implementations for implicit feedback to model deployment and product integration.
9 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Electrical and Electronics Engineering, Doctor of Philosophy (Ph.D.), Electrical and Electronics Engineering at Nanyang Technological UniversityVisiting Scholar, Electrical and Computer Engineering, Visiting Scholar, Electrical and Computer Engineering at Purdue UniversityBachelor of Engineering (B.Eng.), Electrical and Electronics Engineering, Bachelor of Engineering (B.Eng.), Electrical and Electronics Engineering at Harbin Institute of Technology
English, Chinese
Github Skills (7)
splitting10
pandas10
python10
data-science10
splits10
recommender-system10
data-processing10
Programming languages (12)
Github contributions (5)
recommenders-team/recommenders
Oct 2018 - Feb 2021
Best Practices on Recommendation Systems
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:12 reviews, 1079 commits, 83 PRs in 2 years 4 months
Contributions summary:Le primarily worked on implementing splitting methods within the recommender system, focusing on various algorithms such as random and chronological splitting for datasets. They contributed code to add these splitter functions, which are essential for model training and evaluation. The user also focused on the handling and preparation of data, particularly for applications involving implicit feedback data, and provided tools to enhance it.
recommendation-systemspythonjupyter-notebookoperationalizationmicrosoft
microsoft/acceleratoRs
Feb 2017 - Jul 2019
Data science and AI solution accelerator suite that provides templates for prototyping, reporting, and presenting data science analytics of specific domains
Contributions:118 commits, 3 PRs, 70 pushes in 2 years 5 months
analyticssciencereportingdata-scienceprototyping
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