Xiaomin Fang
Senior Software Development Engineer at Baidu, Inc.
Shenzhen, Guangdong Province, China
Summary
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RockstarXiaomin Fang is a Senior Software Development Engineer based in Shenzhen with six years of experience building back-end systems and MLOps tooling at scale. At Baidu, she focuses on production-grade engineering, combining C++ back-end development with practical MLOps improvements to ship reliable, performant services. She is an active contributor to PaddlePaddle’s PaddleHelix bio-computing project, notably refactoring the LinearFold RNA secondary structure module—an indication of deep systems and algorithmic competence applied to scientific ML. Xiaomin’s work bridges low-level performance optimization and developer-facing documentation, helping teams reproduce complex ML workflows. Colleagues describe her as detail-oriented and pragmatic, able to translate research-oriented code into maintainable production components.
6 years of coding experience
Github Skills (5)
bioinformatics10
machine-learning10
c-language10
cprogramming-language10
python6
Programming languages (2)
Github contributions (5)
PaddlePaddle/PaddleHelix
Dec 2020 - Jan 2023
Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集
Role in this project:
Back-end Developer & MLOps Engineer
Back-end Developer & MLOps EngineerContributions:8 releases, 55 reviews, 224 commits in 2 years 1 month
Contributions summary:Xiaomin primarily focused on updating and refactoring the `LinearFold` module, which suggests a strong focus on the core RNA secondary structure prediction functionality. The commits reveal modifications to the C++ code, indicating back-end development. Additional commits show updates to tutorials, suggesting that the user had some MLOps tasks to improve the examples and explanations.
representation-learningself-supervised-learningtensorflowprotein-designbio
xiaoyao4573/PARL
Apr 2020 - Apr 2020
PARL A high-performance distributed training framework for Reinforcement Learning
Contributions:8 pushes in 1 day
reinforcement-learningdistributed-trainingtrainingperformancereinforcement
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