Fabien Cardinaux
Manager at Sony Europe
Stuttgart Region Germany
Summary
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Rockstar🎓
Top SchoolFabien Cardinaux is a manager leading a small R&D team in core AI research at Sony’s Stuttgart Technology Center, with a decade-plus track record as a scientist in industrial and academic settings. He holds a PhD from EPFL/IDIAP for work on face authentication and has focused his career on machine learning for healthcare, neural network compactification, on-device inference and neural architecture search. At Sony he progressed from principal research scientist and project leader to manager, contributing hands-on to open-source neural network libraries (nnabla) by refactoring layers and adding factorized and regularized components for efficient models. Based in the Stuttgart region, he combines deep academic roots with practical engineering to push research into deployable, resource-efficient AI. An understated strength is his ability to bridge long-term research (PhD-level biometrics) with immediate product constraints like model size and on-device performance.
8 years of coding experience
18 years of employment as a software developer
PhD, Machine Learning, PhD, Machine Learning at EPFL (École polytechnique fédérale de Lausanne)Master's degree, Instrumentation and Image Informatics, Master's degree, Instrumentation and Image Informatics at Universite de Bourgogne
English, French, German
Github Skills (5)
neural-network10
machine-learning10
convolutional-neural-networks10
python10
batch-normalization9
Programming languages (1)
Github contributions (4)
sony/nnabla
Jul 2017 - Oct 2019
Neural Network Libraries
Role in this project:
ML Engineer
ML EngineerContributions:16 commits, 5 PRs, 3 pushes in 2 years 3 months
Contributions summary:Fabien's commits primarily involve modifications and additions to the neural network library, specifically focused on enhancing and extending the functionality of various layers. Their work includes refactoring existing layers such as BatchNormalization, the inclusion of factorized layers like SVD and CP decomposition for convolutional and affine layers, and adding regularization capabilities in the CP optimization. The user also implemented changes in example implementations by updating activation layers, and also updating documentation.
deep-learningneural-networksmachine-learningneural-networktensorflow
sony/ai-research-code
Oct 2021 - Apr 2022
Contributions:1 release, 3 commits, 6 PRs in 6 months
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