Sylvain Chevallier
Full Professor at Université Paris-Saclay
Gif-sur-Yvette, Ile-de-France
Summary
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SeniorSylvain Chevallier is a Full Professor and researcher with 11+ years focused on signal processing and machine learning for multivariate time series, specializing in brain–computer interfaces, industrial anomaly detection, and assistive technologies. Based at Université Paris-Saclay, he blends academic leadership and hands-on engineering, teaching DevOps, Python, and ML while contributing actively to open science and open source. His practical contributions to prominent BCI and geometry-aware ML libraries—such as pyRiemann, MOABB, and pymanopt—include SSVEP dataset integrations, numerical-stability fixes, testing and automation, reflecting deep expertise in Riemannian methods for positive definite matrices. Comfortable across research, teaching, and production code, he brings a pragmatic focus on reproducibility and robustness that benefits both neuroscience benchmarks and industrial deployments.
11 years of coding experience
Github Skills (17)
unit-testing10
scipy10
python10
scikit10
machine-learning10
manifold10
numpy10
scikit-learn10
linear-algebra10
optimization10
signal-processing9
data-analysis9
test-automation9
neuroscience8
data-processing8
Programming languages (5)
Github contributions (5)
NeuroTechX/moabb
Jan 2019 - Jan 2023
Mother of All BCI Benchmarks
Role in this project:
ML Engineer
ML EngineerContributions:3 releases, 242 reviews, 188 commits in 4 years
Contributions summary:Sylvain contributed significantly to the development and integration of SSVEP (Steady-State Visual Evoked Potential) paradigms and datasets within the MOABB framework. Their work included adding the SSVEPExo dataset, implementing a BaseSSVEP paradigm, and providing examples for cross-subject evaluation using the CCA classifier. The user also added tests for the SSVEP paradigm and made various code adjustments, demonstrating expertise in BCI research and implementation within the context of the MOABB framework. The commits also cover testing of the new functions and datasets, ensuring that the integration is functional.
neurosciencebrain-computer-interfacebcimothermachine-learning
pyRiemann/pyRiemann
May 2020 - Aug 2022
Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python
Role in this project:Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:115 reviews, 17 commits, 31 PRs in 2 years 4 months
Contributions summary:Sylvain significantly contributed to the development of an example for SSVEP-based BCI multiclass prediction. Their work involved implementing an offline SSVEP classification example, including data loading, filtering, and visualization. The user also implemented a data loader function, refined data processing techniques to use mne.Epochs, incorporated visualizations of the processed data, and added expected results.
signalneurosciencepythonprincipal-component-analysissklearn
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