Ziheng Huang is a software engineer in New York with nine years of experience building ML-driven products and high-performance back-end systems, currently contributing at Meta. He blends a strong statistical foundation (Columbia MS in Statistics, 4.0 GPA) with hands-on engineering—having optimized performance-critical Go code in the popular gin-gonic/gin project and added benchmarks to validate improvements. His background spans ads optimization, NLP and ranking models, ID graph matching, and fraud detection, reflecting a practical focus on production-ready pipelines from data collection to deployment. Prior roles in finance and research honed his skills in time-series, high-frequency trading models, and automated trading systems, giving him a unique edge in quantitative problem solving. Outside work he’s an open-source enthusiast, crypto/retail trader, and lifelong learner who seeks NLP and ranking challenges that marry algorithmic rigor with scalable systems.
9 years of coding experience
6 years of employment as a software developer
Master's degree, statistics, 4.0/4.0, Master's degree, statistics, 4.0/4.0 at Columbia University in the City of New York
Bachelor of Science (BS), Applied Mathematics, Bachelor of Science (BS), Applied Mathematics at Hohai University
Gin is a HTTP web framework written in Go (Golang). It features a Martini-like API with much better performance -- up to 40 times faster. If you need smashing performance, get yourself some Gin.
Role in this project:
Back-end Developer
Contributions:5 reviews, 7 commits, 9 PRs in 19 days
Contributions summary:Ziheng contributed to the `gin-gonic/gin` repository by addressing typos, improving code efficiency, and enhancing the code's performance. They focused on optimizing the code, by implementing changes such as using `buf.String()` instead of `string(buf.Bytes())`. Additionally, the user improved performance through implementing bit shift operations and rewriting parts of the validator. Moreover, they added tests and benchmark tests to evaluate the impacts of changes.
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