Robin Gutzen - Postdoctoral Researcher at New York University

Robin Gutzen

Postdoctoral Researcher at New York University

New York, New York, United States

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Summary

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Robin Gutzen is a computational neuroscientist and postdoctoral researcher with nine years of experience bridging physics, data-driven analysis, and neural network dynamics. He specializes in characterizing correlation structures and propagating activity in neural systems, and has led reproducible, open-source tooling for neurophysiology data handling—contributing backend fixes and format support to the widely used python-neo project. Based in New York and affiliated with Forschungszentrum Jülich and NYU, he combines rigorous quantitative comparison of models and data with practical engineering to make analysis code reusable across labs. Passionate about intuitive data visualization (and occasional sci-art), he brings an interdisciplinary perspective that spans biophysics, information theory, and software engineering to unravel how connectivity shapes neural function.

9 years of coding experience

5 years of employment as a software developer

Physics, Physics at University of Montpellier

PhD, Computational Neuroscience, PhD, Computational Neuroscience at RWTH Aachen University

Github Skills (8)

file-processing10

file-handling10

neo4j10

file-access10

python10

fileio10

data-processing10

numpy7

Programming languages (4)

C++Jupyter NotebookPythonDart

Github contributions (5)

NeuralEnsemble/python-neo

Oct 2019 - Oct 2020

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

Contributions:5 commits, 4 PRs, 13 comments in 11 months

Contributions summary:Robin primarily contributed to improving the `neo/io/nixio.py` file, suggesting expertise in data input/output operations. They fixed bugs related to annotation handling, specifically addressing duplication issues. Additionally, the user made enhancements by adding micro Volt conversion and importing a missing package related to base signal modifications. These changes indicate a focus on data processing, file format handling, and improving the robustness of the core functionalities within the `neo` package.

python

INM-6/networkunit

Nov 2017 - Aug 2022

A SciUnit library for validation testing of spiking neural network models.

Contributions:5 releases, 1 review, 145 commits in 4 years 9 months

neural-networktesting

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