Eelke Spaak
Director, Research MSc Cognitive Neuroscience at Donders Institute for Brain, Cognition, and Behaviour
Nijmegen, Gelderland, Netherlands
Summary
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Rockstar🎓
Top SchoolEelke Spaak is a cognitive neuroscientist and AI-trained researcher with 15+ years of experience probing the neural mechanisms of perception and oscillations, now directing research for the MSc Cognitive Neuroscience program at the Donders Institute. He bridges rigorous human neuroscience (PhD, postdoc at Oxford) with hands-on computational skills—contributing to prominent open-source projects like PyMC and Pingouin and developing analysis tools for large intracranial datasets. His work spans theory and application: from multivariate methods for working memory to using Bayesian and machine-learning techniques to find structure in complex neural and behavioral data. As co-chair of the Radboud Young Academy he actively shapes academic policy and scientific culture, while long-standing involvement in FieldTrip and web development highlights a rare combination of deep experimental expertise and practical software engineering.
15 years of coding experience
13 years of employment as a software developer
MSc, Philosophy, MSc, Philosophy at The University of EdinburghDoctor of Philosophy - PhD, Doctor of Philosophy - PhD at Radboud University Nijmegen
Dutch, English
Stackoverflow
2,767reputation
288kreached
41answers
12questions
numpy
top-5%
python
top-5%
Github Skills (24)
unit-testing10
variational-inference10
probabilistic-programming10
python10
pandas10
statistics10
mcmc10
bayesian10
bayesian-inference10
data-analysis10
py9
statistic9
numpy9
theano9
anova7
Programming languages (10)
Github contributions (5)
pymc-devs/pymc
Sep 2016 - Aug 2021
Bayesian Modeling and Probabilistic Programming in Python
Role in this project:
Data Scientist
Data ScientistContributions:2 releases, 29 reviews, 27 commits in 5 years
Contributions summary:Eelke contributed to the PyMC3 repository by modifying and improving the library's functionality related to Bayesian modeling and probabilistic programming. Their work involved applying transformations to distributions, making changes to the variational inference (ADVI) component for better Theano compatibility and GPU support, and refactoring the _repr_latex functionality. They also addressed issues in the statistical functions and graph visualization, demonstrating a focus on enhancing the core features of the library.
pythonbayesian-inferencestatistical-inferencemachine-learningprobabilistic-programming
raphaelvallat/pingouin
Aug 2020 - Sep 2020
Statistical package in Python based on Pandas
Role in this project:Back-end Developer / Data Scientist
Back-end Developer / Data ScientistContributions:1 review, 22 commits, 4 PRs in 17 days
Contributions summary:Eelke primarily contributed to the statistical package pingouin by implementing and refining the options mechanism for dataframe post-processing, specifically focusing on rounding behavior. They added functionality for custom rounding using callables and extended the rounding options to handle various cell types, including those with ndarray type. Their work also involved adding unit tests to ensure the correct behavior of the implemented features and addressed a bug related to confidence intervals. Furthermore, they updated the tests and changelog to reflect these changes.
multiple-comparisonsstatisticspythoncircular-statisticscorrelations
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