Andrew Davison
Research Group Leader at CNRS
France
Summary
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Rockstar🎓
Top SchoolAndrew Davison is a research group leader and senior neuroinformatics scientist with 19 years of experience at CNRS, leading the UNIC Lab/Paris-Saclay Institute of Neuroscience group and bridging computational neuroscience research with software engineering. He holds a PhD in Computational Neuroscience from Cambridge and combines deep academic rigor with practical engineering, exemplified by backend contributions to the widely used Neo Python library for electrophysiology data handling. His work focuses on robust data structures and reproducible analysis—fixing serialization, time-slicing, and parent-object handling to make neurophysiology tooling more reliable. Based in France, he has a long trajectory of postdoctoral and research roles including a stint at Yale, giving him broad collaborative networks and mentorship experience. Not obvious from titles alone, he regularly engages in low-level library maintenance that directly benefits experimental neuroscientists by ensuring data integrity across formats.
19 years of coding experience
Ph.D. Computational Neuroscience, Ph.D. Computational Neuroscience at University of CambridgeM.Sc. Medical Physics and Clinical Engineering, M.Sc. Medical Physics and Clinical Engineering at The University of Sheffield
French, English
Github Skills (6)
object-oriented-programming10
python10
scientific-computing9
numpy9
data-structure9
data-structures9
Programming languages (14)
Github contributions (5)
NeuralEnsemble/python-neo
Apr 2012 - Jan 2023
Neo is a package for representing electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats
Role in this project:
Back-end Developer
Back-end DeveloperContributions:4 releases, 94 reviews, 619 commits in 10 years 10 months
Contributions summary:Andrew's commits primarily focused on implementing and refining the `merge()` method for `AnalogSignalArray` objects, as well as fixing problems with pickling and ensuring proper handling of parent object references. They also addressed issues related to time slicing and ensured that all relevant parent objects are preserved. These changes are all related to core Neo object functionalities, indicating a focus on backend data structure development.
neurosciencepythonneofile-formatscomputational-neuroscience
apdavison/hbp-validation-framework
Sep 2017 - Feb 2025
Contributions:1 PR, 443 pushes, 53 branches in 7 years 6 months
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