Jona Sassenhagen
Senior ML Research Engineer at Neo Cybernetica
Andernach, Rhineland-Palatinate, Germany
Summary
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Rockstar🎓
Top SchoolJona Sassenhagen is a Senior ML Research Engineer with 13 years of experience bridging academic neuroscience and production-grade machine learning systems. He moves fluidly between hands-on engineering and leadership, having led engineering teams at DataRobot to build model monitoring, explainability, and time-resolved drift analytics before returning to research-focused engineering at Neo Cybernetica. His background includes a PhD in Linguistics and postdoctoral work analyzing neural time-series, which informs rigorous approaches to ML for signal and text data. An active open-source contributor, he has improved test coverage and data-format support in widely used neuroimaging projects such as nilearn and MNE-python, reflecting a practical commitment to reliability in scientific software. Colleagues describe him as equally comfortable designing experiments and shipping robust production features that make complex models auditable and maintainable.
13 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD, Linguistics, Doctor of Philosophy - PhD, Linguistics at Marburg University
German, English
Github Skills (13)
megaparsec10
pytest10
python10
test-automation10
testing10
filter9
data-analysis9
matplotlib9
plot9
filtering9
neuroimaging8
brain-imaging8
eeglab7
Programming languages (13)
Github contributions (5)
mne-tools/mne-python
Nov 2014 - May 2022
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
Role in this project:
Back-end Developer
Back-end DeveloperContributions:14 reviews, 528 commits, 227 PRs in 7 years 7 months
Contributions summary:Jona primarily contributed to the MNE-python repository by fixing bugs and adding support for new data formats within the EEG/MEG analysis framework. Their work included resolving issues with the reading and processing of BrainVision data, particularly handling events and channel locations. Furthermore, they added functionality to handle missing values for the lowpass filter in the epochs image, and extended the plotting capabilities of the software.
visualizationneuroimagingneurosciencepythonstatistics
nilearn/nilearn
Jun 2017 - Jul 2017
Machine learning for NeuroImaging in Python
Role in this project:QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:9 commits, 1 PR, 16 comments in 1 month
Contributions summary:Jona primarily focused on adding and modifying tests within the `nilearn` repository. Their contributions involved writing tests for plotting functions, particularly for surface plotting. The user implemented new test cases, integrated existing tests with axes, and introduced the use of temporary files for output. This work aimed to improve code coverage and ensure the reliability of the plotting functionalities.
neuroimagingpythondecodingbrain-connectivitymachine-learning
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