Yang Xiang
President at Yunshan Networks
Beijing, China
Summary
🤩
Rockstar🎓
Top SchoolYang Xiang is a seasoned software leader with 10+ years building cloud-native observability and networking systems, currently serving as President of Yunshan Networks after a decade as VP of Engineering. He led development of DeepFlow, an eBPF-based observability project for distributed tracing and profiling, and has hands-on experience optimizing core backend data structures and flow processing logic. His background includes an NLP/recommendation internship at Google and a PhD focus in computer networking from Tsinghua University, blending rigorous research training with product-grade engineering. Known for diving into low-level telemetry and protocol/state-machine optimizations, he bridges academic depth and practical system performance improvements. Based in Beijing, he combines executive leadership with continued open-source contributions that materially improved DeepFlow’s performance and maintainability.
10 years of coding experience
12 years of employment as a software developer
PhD Computer Networking, PhD Computer Networking at Tsinghua UniversityB.E. Software Engineering, B.E. Software Engineering at Jilin University
Github Skills (10)
data-structures10
go10
ebpf10
data-structure10
tcp9
performance-optimization9
ip9
udp9
networking8
system-programming8
Programming languages (8)
Github contributions (5)
deepflowio/deepflow
Jul 2017 - Jan 2023
eBPF Observability - Distributed Tracing and Profiling
Role in this project:
Back-end Developer
Back-end DeveloperContributions:5250 reviews, 583 commits, 2077 PRs in 5 years 7 months
Contributions summary:Yang contributed to the development of the DeepFlow project, focusing on core backend features related to eBPF observability. They implemented default values for platform metadata in the Flow structure to simplify Poseidon code and modified the internal structures of the traffic data collection logic, which helped optimize the system's performance. The user also worked on improvements to the TCP/UDP flow processing and the flow state machine. Their work primarily involves modifications to data structures and core code logic within the project.
distributed-tracingebpfobservabilityprofilingopentelemetry
deepflowio/docs
May 2022 - Jan 2026
DeepFlow Docs
Contributions:487 reviews, 533 PRs, 559 pushes in 3 years 9 months
deep-learningmachine-learningtensorflow
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