Eric Bezzam
Audio ML Engineer at Hugging Face
France
Summary
🤩
Rockstar🎓
Top SchoolEric Bezzam is an Audio ML engineer with a decade of experience blending signal processing, physics-informed machine learning, and production-focused audio engineering. Currently at Hugging Face, he brings research-grade expertise from a PhD at EPFL and hands-on industry experience from Sonos and Snips, shipping real-world audio algorithms. He has a strong open-source footprint—contributing backend optimizations and STFT improvements to the well-known pyroomacoustics library—and a track record of turning algorithmic ideas into efficient implementations. As a co-founder and long-time organizer of a 250-student hackathon at EPFL, he combines technical depth with community building and mentorship. His work uniquely spans programmable optics and lensless imaging to beamforming and audio ML, reflecting an uncommon cross-disciplinary fluency in acoustics, optics, and ML systems.
10 years of coding experience
5 years of employment as a software developer
Master of Science (M.Sc.), Communication Systems, Master of Science (M.Sc.), Communication Systems at Ecole polytechnique fédérale de LausanneBachelor of Science (B.S.), Electrical and Computer Engineering, 1.27 (on 1.0 scale), Bachelor of Science (B.S.), Electrical and Computer Engineering, 1.27 (on 1.0 scale) at Jacobs University BremenInternational Baccalaureate Diploma, International Baccalaureate Diploma at UWC-USA
English, Spanish, French, German
Github Skills (7)
algorithm10
algorithms10
python10
implement10
signal-processing10
acoustics7
audio6
Programming languages (7)
Github contributions (5)
LCAV/pyroomacoustics
Jun 2017 - Sep 2021
Pyroomacoustics is a package for audio signal processing for indoor applications. It was developed as a fast prototyping platform for beamforming algorithms in indoor scenarios.
Role in this project:
Back-end Developer & Algorithm Implementer
Back-end Developer & Algorithm ImplementerContributions:11 reviews, 76 commits, 35 PRs in 4 years 3 months
Contributions summary:Eric primarily focused on improving the efficiency and functionality of the STFT processing module within the pyroomacoustics library. Their commits involved removing unnecessary variables and libraries, cleaning up print statements, and updating core components such as DFT, to use more optimized libraries like Intel's MKL. Furthermore, they added the option to pass a frequency domain signal for STFT synthesis. They also developed a new subband LMS class and unit test.
signaladaptive-filteringfast-prototypingprototypingsignal-processing
ebezzam/pyFFS
Oct 2020 - Sep 2021
Fast Fourier Series library.
Contributions:2 PRs, 119 pushes, 22 branches in 11 months
fourierfourier-series
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