Jun Duan
Research Staff Member at IBM
Town of Yorktown, New York, United States
Summary
👤
Senior🎓
Top SchoolJun Duan is a Research Staff Member at IBM with a decade of experience building cloud- and edge-focused infrastructure, specializing in Kubernetes and PaaS systems. He holds a PhD in Computer Engineering from Stony Brook and earlier degrees from Peking University, blending deep academic rigor with practical systems engineering. At IBM he translates research into production-ready solutions, and his open-source contributions include backend performance and observability improvements to vllm, a high-throughput LLM serving engine. Jun’s work often targets operational efficiency—instrumenting model load and sleep/wake behaviors to improve monitoring and runtime reliability—reflecting a talent for making complex distributed systems more observable and resilient. Based in Yorktown, NY, he combines research sensibilities with hands-on coding to bridge cutting-edge ML infrastructure and real-world deployment needs.
10 years of coding experience
6 years of employment as a software developer
Master of Science - MS, School of Electronics Engineering and Computer Science, Master of Science - MS, School of Electronics Engineering and Computer Science at Peking UniversityDoctor of Philosophy - PhD, Computer Engineering, 4.0/4.0, Doctor of Philosophy - PhD, Computer Engineering, 4.0/4.0 at Stony Brook University
Github Skills (15)
monitoring10
performance-tuning10
performance-analytics10
performance-monitor10
performance-analysis10
performance-monitoring10
python10
performance-measurement10
apidoc9
api9
inference9
pytorch9
model-management8
llm8
transformer7
Programming languages (6)
Github contributions (5)
vllm-project/vllm
Jan 2025 - Mar 2025
A high-throughput and memory-efficient inference and serving engine for LLMs
Role in this project:
Backend Developer
Backend DeveloperContributions:2 reviews, 7 PRs, 9 comments in 1 month
Contributions summary:Jun primarily contributed to the backend logic of the VLLM project, specifically focusing on improving the performance and monitoring of model loading, inference, and sleeping/waking up processes. They added logging capabilities to track the time taken for weight downloads, model loading, and executor sleep/wake-up operations. The user also implemented an API endpoint to check the engine's sleep status. These modifications enhanced the project's monitoring capabilities and operational efficiency.
amdcudadeepseekgpthpu
IBM-edge-platform-research/kyst-configurations
Apr 2022 - Sep 2022
Gitops source for kyst
Contributions:55 PRs, 249 pushes, 60 branches in 5 months
gitopskubernetes
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