Michiel Cottaar
Postdoc at University of Oxford
Oxford, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolMichiel Cottaar is a multidisciplinary postdoctoral researcher with 12 years of experience applying physics, Bayesian statistics, and Python to problems from solar physics and star cluster dynamics to advanced MRI sequence development and microstructure imaging. Based at the University of Oxford, he develops novel MRI acquisition and analysis methods to infer microscopic tissue changes and improve diffusion tractography-based connectivity, bridging hardware-aware sequence design with sophisticated data processing. His background includes PhD work on stellar kinematics and international research stints in Boulder, Oslo, and Zurich, and he contributes practical neuroimaging software—improving widely used tools like nibabel and HCP preprocessing pipelines. Curious about low-level data handling, he often focuses on robust file I/O and pipeline reliability, helping move research code toward reproducible, production-ready workflows.
12 years of coding experience
Master of Science (MSc), Astrophysics, Cum Laude, Master of Science (MSc), Astrophysics, Cum Laude at Utrecht University
Stackoverflow
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Github Skills (15)
data-preprocessing10
data-formats10
fs10
bash10
script10
shell10
nifti10
python10
data-format10
neuroimaging10
brain-imaging10
debug9
data-structures8
file-handling8
data-structure8
Programming languages (6)
Github contributions (5)
Washington-University/HCPpipelines
Apr 2018 - Jun 2022
Processing pipelines for the HCP
Role in this project:
Back-end Developer
Back-end DeveloperContributions:7 reviews, 110 commits, 13 PRs in 4 years 2 months
Contributions summary:Michiel primarily contributed to the processing pipelines for the HCP project, which includes shell scripting for various data preprocessing steps. Their work involved fixing bugs in existing scripts, such as correcting the merging of diffusion data and including missing files. The user also added features related to brain mask coverage and made code improvements based on feedback, suggesting a focus on optimizing and maintaining the pipeline's functionality.
hcp-pipelinespipelinesshellhcp
nipy/nibabel
Sep 2014 - Apr 2020
Python package to access a cacophony of neuro-imaging file formats
Role in this project:Back-end Developer & Bug Fixer
Back-end Developer & Bug FixerContributions:82 commits, 5 PRs, 34 comments in 5 years 8 months
Contributions summary:Michiel primarily focused on improving the `nibabel` package's functionality and maintainability. Their commits addressed bug fixes, including issues with string encoding and file handling related to compressed files. They also implemented enhancements like making the fileno transparent and ensuring the software could read in different CIFTI axis types. The user demonstrated skills in Python programming and working with various neuroimaging file formats.
pythontckimagingtrkstreamlines
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