Chuyang Ke
Data Scientist
Redmond, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolChuyang Ke is a data scientist with a decade of experience building gaming AI and recommendation systems, currently developing cutting-edge solutions at Xbox Emerging Technologies in Redmond. He holds a PhD in Computer Science from Purdue and has blended academic research with industry impact through roles at Microsoft (including an Azure Gaming Services internship) and research positions at Purdue and IQVIA. His technical expertise spans reinforcement learning, NLP, statistical modeling, and practical recommender engineering—evidenced by contributions to the widely used Microsoft Recommenders project where he improved SAR notebooks and implemented robust top-K evaluation utilities. Known for turning research ideas into production-ready components, he brings both rigorous experimentation and hands-on engineering to complex, real-world gaming problems.
10 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Purdue UniversityBachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at University of Rochester
Stackoverflow
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Github Skills (8)
pandas10
jupyter-notebook10
machine-learning10
recommendation-system10
python10
data-science10
pytest9
testing9
Programming languages (2)
Github contributions (5)
recommenders-team/recommenders
Jun 2022 - Aug 2022
Best Practices on Recommendation Systems
Role in this project:
Data Scientist
Data ScientistContributions:15 reviews, 44 commits, 10 PRs in 2 months
Contributions summary:Chuyang primarily contributed to the project by fixing and updating existing notebooks related to the recommendation system, specifically those involving the SAR (Session-based Association Rules) algorithm. Their work involved modifications to prediction time logging and prediction functions within the notebooks. In addition, the user implemented and tested the `get_top_k_items` function, which is a crucial component for evaluating the performance of recommendation algorithms. They also added further test cases, and comments to test the top K items function.
recommendation-systemspythonjupyter-notebookoperationalizationmicrosoft
Kasoyang/URwebpage
Sep 2016 - Dec 2016
Contributions:10 pushes, 1 branch in 2 months
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