Xiaoyun Zhang
Senior Software Engineer at Microsoft
Greater Seattle Area United States
Summary
🤩
Rockstar🎓
Top SchoolXiaoyun Zhang is a Senior Software Engineer based in the Greater Seattle Area with a decade of experience building backend systems and machine learning tooling at Microsoft. She has been a core contributor to ML.NET and Model-Builder, adding AutoML recommendation capabilities and practical time-series forecasting examples that help C# developers integrate trained models into production. Her open-source work includes backend enhancements to Microsoft's autogen framework for agentic AI, demonstrating a blend of ML engineering and production-grade backend development. Xiaoyun’s strengths lie in turning research-grade models into developer-friendly tools, with a strong track record of rigorous testing and integration across large-scale projects. She brings an international academic background from Xi'an Jiaotong University and an exchange stint at the National University of Singapore, reflecting both technical depth and cross-cultural collaboration. An attention to validation and developer experience—rather than just model accuracy—sets her work apart.
10 years of coding experience
4 years of employment as a software developer
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at Xi'an Jiaotong UniversityExchange student, School of Compute, 4.5/5.0, Exchange student, School of Compute, 4.5/5.0 at 新加坡国立大学
Github Skills (24)
recommendation-system10
ai-agent10
python10
mldotnet10
machine-learning10
regression10
agent10
time-series-forecasting10
mlnet10
automl10
dotnet-core10
jupyter-notebook10
csharp10
nuget9
unit-testing9
Programming languages (14)
Github contributions (5)
dotnet/machinelearning
Aug 2019 - Jan 2023
ML.NET is an open source and cross-platform machine learning framework for .NET.
Role in this project:
ML Engineer
ML EngineerContributions:309 reviews, 105 commits, 251 PRs in 3 years 5 months
Contributions summary:Xiaoyun's contributions centered around adding a recommendation task to the AutoML feature of ML.NET. They implemented the ability to train recommendation models for predicting ratings. The changes involved validating user input, especially concerning label types, and the integration of recommendation-specific functionality within the automated machine learning framework. This work included extensive unit tests validating the robustness of the newly added recommendation task.
dotnetmachine-learningml
dotnet/csharp-notebooks
May 2022 - Oct 2022
Get started learning C# with C# notebooks powered by .NET Interactive and VS Code.
Role in this project:Data Scientist
Data ScientistContributions:9 reviews, 66 commits, 14 PRs in 5 months
Contributions summary:Xiaoyun's contributions revolve around updating and modifying a Jupyter Notebook focused on time series forecasting using a regression approach within the ML.NET framework. The updates involve integrating new NuGet packages for enhanced plotting and visualization capabilities and refining the data loading and preprocessing steps, converting a time series problem into a regression problem. The core focus is on demonstrating how to build, train, and evaluate a machine learning model, specifically for predicting load metrics within a time series context.
csharpdotnetvscode
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