Yi Zhang
Principal Software Engineering Manager at Microsoft
Shanghai, Shanghai, China
Summary
🤩
Rockstar🎓
Top SchoolYi Zhang is a Principal Software Engineering Manager at Microsoft with 11 years of engineering leadership and a long tenure driving SharePoint platform work from Shanghai. He solves hard engineering problems by combining systems thinking with a willingness to take bold, outside-the-box approaches to technical and organizational challenges. Yi has deep experience across software, test, and ML infrastructure—contributing to notable open-source projects like Microsoft's Qlib and NNI, where he worked on backtesting, reinforcement-learning workflows, and robust tuner tests for AutoML. His background blends an MS in Computer Science and an MBA from Georgia Tech with early roots at Peking University, enabling him to bridge technical depth and product/business strategy. Known as a hands-on manager, he mentors teams to deliver production-ready ML and distributed systems features while still committing code to high-impact repos. A less obvious strength is his history in test engineering, which informs his emphasis on reliability and rigorous validation across the software lifecycle.
11 years of coding experience
19 years of employment as a software developer
MBA, MBA at Georgia Institute of TechnologyB.S., Computer Science, B.S., Computer Science at Peking UniversityBeijing No.4 High School
Github Skills (16)
hyperparameter-tuning10
pytorch10
machine-learning10
automated-machine-learning10
automl10
backtesting10
python10
nas10
algorithmic-trading10
reinforcement-learning10
quantitative-finance10
backtest10
neural-architecture-search10
deep-learning9
unit-testing9
Programming languages (6)
Github contributions (5)
microsoft/nni
Aug 2019 - Jan 2023
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Role in this project:
ML Engineer
ML EngineerContributions:3 releases, 1132 reviews, 742 commits in 3 years 6 months
Contributions summary:Yi's commits primarily focused on adding comprehensive tests for tuners within the NNI (Neural Network Intelligence) toolkit, which automates the machine learning lifecycle. These tests included checking the functionality of various built-in tuners, ensuring the correct generation of hyperparameters within defined search spaces and their ability to handle different parameter types. Moreover, the user addressed minor issues related to DARTS and introduced a CIFAR10 example using a random mutator, highlighting their work on neural architecture search tasks.
pythonneural-architecture-searchengineeringtensorflowbayesian-optimization
microsoft/qlib
May 2021 - Jun 2022
Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.
Role in this project:ML Engineer
ML EngineerContributions:52 reviews, 20 commits, 3 PRs in 1 year 1 month
Contributions summary:Yi primarily contributed to the development and support of backtesting functionalities within the Qlib platform. Their commits show modifications to the `examples/multi_level_trading/workflow.py` file, indicating work on a trading workflow. The user also updated the `rl_playground.py` file, suggesting involvement with reinforcement learning components of the platform. These changes, alongside the `qlib/strategy/base.py` and `qlib/strategy/__init__.py` updates, hint at enhancements to the strategy implementations.
auto-quantpythoninvestment-strategiesempowerquant
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