Braindecode Code Sprint 2023

Get Ready!

The detailed Pull Requests / Issues tackled during the sprint are described on the Braindecode github project page. During the sprint, we'll chat on MNE discord.

Who?

• Alexandre Gramfort
  Research scientist at Meta, MNE-Python core developer, Scikit-Learn core developer and working on statistical machine learning and neuroscience data processing.
• Apolline Mellot
  I am a PhD candidate in the MIND team at Inria Saclay under the supervision of Alexandre Gramfort, Sylvain Chevallier and Denis Engemann. The subject of the PhD thesis I am preparing is to leverage machine learning and domain adaptation for enhancing the measure of brain health with magnetoencephalography (MEG) and electroencephalography (EEG) signals.
• Bruno Aristimunha
  PhD Student skilled in machine learning, deep learning and electrophysiological signal processing at the Federal University of ABC (Brazil) and Université Paris-Saclay.
• Denis Engemann
  I study the human brain using electrophysiology, imaging and machine learning to develop biomarker in next generation therapeutics and diagnostics.
• Joseph Paillard
  I am a master student at MVA Paris-Saclay, currently interning at Roche Basel with Denis Engemann. We work on machine learning methods for EEG data in the context of biomarker development.
• Lukas Alexander Wilhelm Gemein
  PhD Student at the University of Freiburg. Interested in the application of machine learning to healthcare data. Currently investigating brain aging based on electroencephalograic data.
• Philipp Bomatter
  MSc in Computational Science and Engineering (ETH Zürich) with focus on AI & Neuroscience. Now part of the Machine Learning team in the EEG biomarker group at Roche.
• Pierre Guetschel
  PhD on deep learning and electrophysiological signal processing at the Donders Institute for Brain, Cognition and Behaviour (Netherlands).
• Robin Tibor Schirrmeister
  Final-year PhD student at University of Freiburg, Germany working on deep learning for brain-signal decoding from EEG. Interested in applying deep learning to medicine and researching new deep learning methods, especially interpretability and automatic machine learning methods.
• Sara Sedlar
  Postdoctoral Researcher at the Laboratoire Interdisciplinaire des Sciences du Numérique (LISN). Interested in geometric machine and deep learning approaches for medical data analysis.
• Sylvain Chevallier
  I am full professor at LISN-CNRS, in INRIA team A&O/TAU that focuses on machine learning and stochastic optimization. I work mostly on timeseries analysis and manifold optimization. I teach at the Computer Science departement of IUT d’Orsay.
• Thomas Moreau
  I am a researcher in the MIND team @ INRIA Saclay, since autumn 2019, working on unsupervised learning for time series, bilevel optimization and on deep learning methods applied to solving inverse problems.