Chengming Li
Software Engineer at 微软
Suzhou City, Jiangsu, China
Summary
🤩
Rockstar🎓
Top SchoolChengming Li is a software engineer with nine years of experience specializing in high-performance vector search and database internals, currently at Microsoft after key contributions at Zilliz. He played a hands-on role in Milvus development—implementing and optimizing index features like IVFPQ delete, HNSW deletion/integration, and adding Annoy support—demonstrating deep familiarity with FAISS, HNSW, NGT, NSG, FALCONN and other ANNS libraries. With a master’s focus on SDN and experience with OpenFlow and Ryu, he combines networking research background with practical systems engineering for ANN search. Based in Suzhou, he brings competitive programming roots (ACM regional bronze) and a proven track record of performance tuning distributed components such as WAL and worker modules in large-scale vector systems.
9 years of coding experience
3 years of employment as a software developer
Master's degree, Computer Science and Technology, Master's degree, Computer Science and Technology at SouthEast UniversityBachelor's degree, Computer Science and Technology, Bachelor's degree, Computer Science and Technology at Wuhan University of Science and Technology
Chinese, English, Japanese
Github Skills (14)
nearest-neighbor-search10
vector-search10
hnswlib10
databases10
vector-database10
database-administration10
admin10
faiss10
nearest-neighbors10
database10
cprogramming-language9
golang9
go9
c-language9
Programming languages (7)
Github contributions (5)
milvus-io/milvus
Mar 2020 - Jan 2021
Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search
Role in this project:
Back-end Developer / Database Engineer
Back-end Developer / Database EngineerContributions:10 reviews, 46 commits, 67 PRs in 10 months
Contributions summary:Chengming primarily contributed to the development and optimization of the Milvus vector database, focusing on index implementations and related functionalities. Their work includes supporting and optimizing the IVFPQ CPU delete functionality, which likely involves interactions with the FAISS library. The user also implemented support for HNSW deletion and integration, and added new features like Annoy support. Overall, the user demonstrated proficiency in vector database internals and performance optimization.
approximate-nearest-neighbor-searchfaisshnswsimilarity-searchvector
op-hunter/milvus
Dec 2019 - Feb 2020
Milvus -- the world's fastest vector search engine.
Contributions:40 pushes, 1 branch in 1 month
pythonvector-search-engineapproximate-nearest-neighbor-searchvectorssimilarity-search
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