Xinping Fan
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolXinping Fan is a materials engineer-in-training based in London with nine years of hands-on experience bridging academic research and technical communication. Currently pursuing a PhD on titanium alloys after completing an MEng at Imperial College London, Xinping has practical research experience in composite development for radioactive waste immobilisation. She combines lab expertise with a knack for making complex tools accessible, contributing documentation fixes and Sphinx tooling to the popular TensorLayer deep learning library. Resilient and solution-focused, Xinping is actively seeking graduate roles while keeping an eye on a potential PhD path or chartered engineering status in the UK.
9 years of coding experience
Master of Engineering - MEng, Material Science and Engineering, Master of Engineering - MEng, Material Science and Engineering at Imperial College LondonDoctor of Philosophy - PhD, Ti Alloys, Doctor of Philosophy - PhD, Ti Alloys at 英国帝国理工学院
English, Chinese
Github Skills (4)
tensorflow10
sphinx10
documentation10
python10
Programming languages (2)
Github contributions (5)
tensorlayer/TensorLayer
Aug 2016 - Aug 2017
Deep Learning and Reinforcement Learning Library for Scientists and Engineers
Role in this project:
Technical Writer
Technical WriterContributions:11 commits, 11 pushes, 2 branches in 1 year
Contributions summary:Xinping's contributions primarily involve documentation updates and corrections within the TensorLayer repository. They fixed function name typos, added and managed Sphinx extensions for documentation generation, and corrected example titles. Their work focuses on improving the clarity and accuracy of the documentation, enhancing its usability. This suggests a focus on making the library accessible to users.
tensorflow-tutorialscientistspythongoogledeep-reinforcement-learning
shorxp/tensorlayer
Aug 2016 - Aug 2016
Contributions:4 commits, 2 PRs, 3 pushes in 3 days
tensorlayerdeep-learningreinforcement-learningdeep-reinforcement-learningmachine-learning
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