Vincent Quenneville-bélair
Machine Learning Scientist at Amazon
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolVincent Quenneville-Bélair is a Machine Learning Scientist with a decade of experience building production-grade ML systems at Amazon and Facebook AI, grounded in a PhD and multiple advanced degrees from the University of Minnesota. He bridges research and engineering—authoring core back-end audio processing code and tests for the widely used PyTorch audio ecosystem and contributing practical tutorials on audio signal processing and speech command recognition. His background includes academic teaching and leadership roles, a stint as Chief Data Scientist, and hands-on expertise in test automation and C++/torchaudio integrations. Based in New York, he combines deep signal-processing knowledge with a track record of shipping robust, well-tested audio ML components used by the broader open-source community.
10 years of coding experience
6 years of employment as a software developer
University of Minnesota Twin CitiesBachelor of Science (BSc), Bachelor of Science (BSc) at McGill University
Github Skills (22)
pytorch10
c-language10
preprocessing10
python10
preprocess10
signal-processing10
testing10
audio-processing10
sox10
digital-signal-processing10
data-preprocessing10
data-loading10
dataprep10
sound-processing10
cprogramming-language10
Programming languages (11)
Github contributions (5)
pytorch/audio
Jul 2019 - Jun 2021
Data manipulation and transformation for audio signal processing, powered by PyTorch
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:9 releases, 427 reviews, 112 commits in 1 year 11 months
Contributions summary:Vincent primarily contributed to the core functionality of the `pytorch/audio` repository, specifically the `torch_sox.cpp` file, indicating a focus on back-end audio processing implementations. They also addressed a version import issue, fixing the import of a specific library version. Moreover, the user added and modified tests, with a focus on testing transformations, functional, and general features across backends, showing the test automation engineer skill.
pytorchaudio-processingmanipulationpythonsignal
pytorch/tutorials
Jul 2019 - Apr 2021
PyTorch tutorials.
Role in this project:ML Engineer
ML EngineerContributions:9 reviews, 13 commits, 14 PRs in 1 year 9 months
Contributions summary:Vincent primarily contributed to a PyTorch tutorial related to audio signal processing. Their work involved loading, transforming, and visualizing audio data using the torchaudio library. They implemented and demonstrated various audio transformations, including spectrogram generation, resampling, and Mu-Law encoding, showcasing an understanding of signal processing techniques and their application within the PyTorch ecosystem. They also implemented a speech command recognition tutorial.
deep-learningpytorchpytorch-tutorials
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