Who is Bruno?
I am a Research Scientist at Yneuro (France 🇫🇷) and (Honorary) Research Associate at the University of California, San Diego (USA 🇺🇸). I obtained my PhD in Computer Science from Paris-Saclay University (France 🇫🇷) and Federal University of ABC (Brazil 🇧🇷), advised by Professors Sylvain Chevallier, Marie-Constance Corsi and Raphael Y. de Camargo.
Research Interests
My current research interests include Learning Representation from the time series (Decoding, Generating and Transferring knowledge), Brain-Computer Interfaces, Machine Learning for brain decoding, Benchmark, and Riemannian Geometry (mostly Symmetric Positive Definite Neural Networks - SPDNets).
Open Source Projects
I advocate for open source as the basis of reproducible science, while occasionally working with closed code. I lead the widely used Python libraries Braindecode and MOABB, and I collaborate with related open-source projects like MNE-Python, MONAI, MONAI Generative, and SpeechBrain.
From libraries to open datasets — a few of the things I build, organize, and maintain (full activity on GitHub):
Libraries & infrastructure
Mother of All BCI Benchmarks: reproducible evaluation of BCI pipelines across open datasets.
Geometric (Riemannian/SPD) deep learning library for neural decoding through trivialization.
Open data, tools, and compute resource for machine learning on neuroelectromagnetic data.
Community Involvement
During my PhD, I collaborated with research groups across the US (San Diego, San Francisco, Washington), UK, Ireland, Germany, Italy, Netherlands, Canada (Waterloo, MILA), Brazil (Sao Paulo), and France. Those collaborations turned into more than 16 publications (full/short papers, reports, abstracts), all listed on Google Scholar. I particularly enjoyed the self-contained, code-oriented projects 🧠⚙️. For academic cooperation, contact me via email or LinkedIn.
I organized the Braindecode Code-Sprint in the European summer of 2023, co-organized the workshop Designing Brain-Computer Interfaces from Theory to Real-Life Scenarios at the Graz BCI 2024 conference, and led the Special Session on Decoding the Brain Time Series at IEEE MLSP 2025.
I review for machine learning conferences and journals: NeurIPS (twice), ICLR, ICML, NeuroImage, Imaging Neuroscience, the Journal of Machine Learning Research (JMLR), and the Learning from Time Series for Health workshop at ICLR.