Leo Yan is a Ph.D. candidate in computer science based in Hangzhou with six years of experience blending academic research in LLM/agentic reasoning and hands-on backend engineering. He has a strong systems and database background—evidenced by contributions to the Apache HertzBeat monitoring project where he added PostgreSQL storage, metadata support, and Prometheus-related fixes—bridging research ideas to production-quality infrastructure. At Westlake University he has taught practical machine learning and worked in a deep learning lab, pairing pedagogical clarity with experimental rigor. His academic record includes top-ranked master's honors and national scholarships, signaling both technical excellence and consistent high performance. He brings a pragmatic researcher’s mindset to engineering problems, able to translate novel ML approaches into reliable, scalable backend systems.
6 years of coding experience
1 year of employment as a software developer
Master of Science in Engineering, Software Engineering, Ranked 1/39, Awarded First prize(2018) / China National Scholarship(2019, 2020), Master of Science in Engineering, Software Engineering, Ranked 1/39, Awarded First prize(2018) / China National Scholarship(2019, 2020) at Chongqing University of Post and Telecommunications
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Westlake University
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Zhejiang University
Apache HertzBeat(incubating) is a real-time monitoring system with agentless, performance cluster, prometheus-compatible, custom monitoring and status page building capabilities.
Role in this project:
Back-end Developer & Database Engineer
Contributions:2 reviews, 12 PRs, 20 pushes in 2 years
Contributions summary:Leo primarily contributed to enhancing the data storage capabilities of the HertzBeat monitoring system. They focused on implementing PostgreSQL as a data storage option, adding support for storing metadata and integrating it into the existing system. Additionally, the user worked on bug fixes related to Prometheus integrations and added a push-style collector. The contributions also include refactoring and updates to the monitoring system's components.
Contributions:8 commits, 5 pushes, 1 branch in 3 months
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