Xiaodong Bi is a machine learning engineer with seven years of experience applying ML to high-frequency trading, recommendation systems, and finance-focused research. Currently at Optiver, he bridges HFT modeling and production-grade ML pipelines, previously contributing to recommendation and sequence modeling at ByteDance and finance AI research at Microsoft Research Asia. He holds an MS focused on machine learning and data mining from Nanjing University and a strong theoretical foundation from a top BE in computer science with a mathematics minor. An active contributor to open-source documentation, he has improved accessibility of the widely used microsoft/qlib quantitative investment platform, reflecting a pragmatic focus on making complex tools usable. Known for blending research rigor with hands-on engineering, he brings a taste for geek culture and unconventional problem-solving to applied ML challenges.
7 years of coding experience
Bachelor of Engineering - BE, Computer Science, 4.65/5.00, Bachelor of Engineering - BE, Computer Science, 4.65/5.00 at Tongji University
Master of Science - MS, Computer Science (Direction: Machine Learning and Data Mining), Master of Science - MS, Computer Science (Direction: Machine Learning and Data Mining) at Nanjing University
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:
Technical Writer
Contributions:13 reviews, 195 commits, 52 PRs in 1 year 5 months
Contributions summary:Xiaodong's commits primarily focused on updating documentation and README files within the `microsoft/qlib` repository. Their work involved modifying documentation links, API references, and code examples, indicating a focus on improving the clarity and accessibility of the project's information. The changes suggest a dedication to ensuring users can easily understand and utilize the Qlib platform and its features. These edits are focused on the documentation aspects of the project.
Contributions:87 commits, 49 pushes in 1 year 2 months
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Xiaodong Bi - Machine Learning Engineer at Optiver