Paul Brossier is a Lead Software Engineer with 20 years of experience specializing in low-level audio and music analysis, research-driven development, and robust C/C++ back-end systems. He is the author and maintainer of the widely used open-source aubio library and has contributed performance and stability fixes to notable projects like hnswlib, demonstrating strong attention to memory management and cross-language bindings. Based in Greater Paris, his career bridges academic research (PhD in Computer Music) and industry roles across audio tech companies, where he consistently ships reliable core libraries rather than flashy front-ends. Colleagues rely on him for deep domain expertise in signal processing and for making complex native codebases more maintainable and performant.
20 years of coding experience
8 years of employment as a software developer
Master of Science (M.Sc.), Physics, Master of Science (M.Sc.), Physics at McGill University
Master of Philosophy (M.Phil.), ATIAM, Master of Philosophy (M.Phil.), ATIAM at IRCAM
Ph.D, Computer Music, Ph.D, Computer Music at Queen Mary, U. of London
Contributions:8 releases, 3862 commits, 42 PRs in 15 years 3 months
Contributions summary:Paul primarily contributes to the C codebase of the aubio library, focusing on low-level audio and music analysis tasks. Their commits involve fixing bugs and improving the error messages for various core functionalities. The user has been actively making improvements to multiple modules and the core structure of the library.
Header-only C++/python library for fast approximate nearest neighbors
Role in this project:
Backend Developer
Contributions:6 commits, 7 PRs, 3 comments in 1 year 1 month
Contributions summary:Paul focused on improving the efficiency and stability of the `hnswlib` library, a fast approximate nearest neighbor search implementation. Their commits addressed memory management issues, such as matching `new[]` with `delete[]`. They also made performance optimizations by avoiding signed/unsigned integer comparisons and using `size_t` for address storage. These changes improved the library's robustness and performance.
python-libraryneighborspythonheadernearest
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