Jevri Hanna is a researcher and computational neuroscientist with nine years of experience applying deep learning and classical machine learning to EEG/MEG for sleep quality, tinnitus, and cognitive studies. He combines rigorous experimental design and statistics from an academic background (PhD, Cambridge) with practical software engineering, contributing notable improvements to the widely used MNE-Python library—especially ICA and PSD/plotting utilities. Skilled across CNNs, RNNs, transformers and generative models (GANs, VAEs, diffusion), he excels at finding hidden patterns in noisy biosignals and rapidly prototyping robust analysis pipelines. Based in Berlin, he has run labs, managed sensitive equipment and IT, and led multi-modal studies integrating EEG and eye-tracking, demonstrating both domain depth and operational leadership.
9 years of coding experience
12 years of employment as a software developer
Doctor of Philosophy - PhD, Neuroscience and Linguistics, Doctor of Philosophy - PhD, Neuroscience and Linguistics at University of Cambridge
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
Role in this project:
Data Scientist & Software Engineer
Contributions:6 commits, 9 PRs, 79 comments in 3 years 8 months
Contributions summary:Jevri contributed significantly to the MNE-Python project, primarily focusing on enhancing and refactoring the ICA (Independent Component Analysis) functionality for EEG/MEG data analysis. They implemented features for processing reference channels with ICA, improving the detection of bad components, and fixing backwards compatibility issues. They also contributed to the plotting utilities, including refactoring PSD plotting for Raw and Epochs objects, implementing a butterfly plot for epochs PSD.
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