Haifeng Wu
Algorithm Engineer at Xpeedic Technology
Wuhan, Hubei, China
Summary
👤
Senior🎓
Top SchoolHaifeng Wu is an algorithm engineer with 10 years of experience applying optimization and machine learning techniques to real-world problems in autonomous driving and EDA. He has led perception-algorithm optimizations to predict and track vehicle trajectories visually and developed routing algorithms for EDA including point-to-point, differential pair, and BUS routing under complex scene constraints. Comfortable across full-stack and back-end development, he has contributed to the AutoML Hypernets project by hardening data handling and feature-transformation pipelines, highlighting a pragmatic focus on data robustness. Based in Wuhan, he holds a master’s in computer science and a bachelor’s in electronic information engineering, both with excellent standing. Known for steadily shifting from web and software engineering toward deep algorithm work, he combines applied research skills with production-grade engineering discipline.
10 years of coding experience
Bachelor's degree, Electronic information engineering, Excellent, Bachelor's degree, Electronic information engineering, Excellent at 五邑大学Master's degree, Computer Science and Technology, Excellent, Master's degree, Computer Science and Technology, Excellent at CCNU
Chinese, Chinese
Github Skills (6)
pandas10
python10
data-processing10
numpy10
refactor9
refactoring9
Programming languages (9)
Github contributions (5)
DataCanvasIO/Hypernets
Aug 2020 - Jan 2023
A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:199 commits, 59 PRs, 229 pushes in 2 years 5 months
Contributions summary:Haifeng primarily focused on addressing issues related to data handling and type conversion within the hypernets library. They corrected type errors encountered with pandas Series objects and Numpy arrays. Additionally, the user worked on integrating the new features transformation step into the reporting module. The user appears to be involved in ensuring the robustness and accuracy of the library's data processing capabilities.
pythonneural-architecture-searchnasnettoolkitsframework-learning
DataCanvasIO/HyperBoard
Jan 2022 - Feb 2023
Contributions:1 release, 69 commits, 1 PR in 1 year
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