Charles Beauville
Machine Learning Engineer at Flower Labs
Lausanne, Vaud, France
Summary
🤩
Rockstar🎓
Top SchoolCharles Beauville is a Machine Learning Engineer with a decade of experience specializing in privacy-preserving and decentralized ML, currently building and supporting the open-source federated learning framework Flower at Flower Labs. He brings hands-on expertise in Python, PyTorch, distributed deep learning, and devops-focused backend work—having improved modularity, environment setup, and onboarding documentation for a project used by a community of over 100,000. Trained at EPFL and MIT (Data Science), he has a track record of reproducing academic results and moving research into production-grade distributed models. His background includes NLP for cybersecurity and a knack for smoothing developer experience in complex ML systems, reflecting both research rigor and pragmatic engineering.
10 years of coding experience
1 year of employment as a software developer
Master of Science - MS, Data Science, 6/6, Master of Science - MS, Data Science, 6/6 at Massachusetts Institute of TechnologyMaster of Science - MS, Data Science, Master of Science - MS, Data Science at EPFL (École polytechnique fédérale de Lausanne)
French, English, Spanish, German
Github Skills (8)
dependency-management10
sh10
script10
shell10
devops10
python10
scripting10
documentation8
Programming languages (7)
Github contributions (5)
adap/flower
Nov 2022 - Jan 2023
Flower: A Friendly Federated AI Framework
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:1 release, 335 reviews, 87 commits in 1 month
Contributions summary:Charles primarily focused on improving the setup and modularity of the project. Their contributions included adding Python version modularity to the virtual environment scripts, creating setup scripts for the baselines, and aligning scripts from the baselines with those on the root, indicating a focus on dependency management and environment configuration. They also addressed documentation issues, improving user experience by adding information on new install scripts and creating pages for first-time contributors.
federated-analyticsfederated-learning-frameworkmachine-learningkeras-federated-learningflower
charlesbvll/flower
Dec 2022 - Aug 2024
Flower - A Friendly Federated Learning Framework
Contributions:5 releases, 22 PRs, 151 pushes in 1 year 8 months
federated-learning-frameworkmachine-learningflowerframework-learningfederated-learning
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