Ariel Rokem
Scientific Committee Member at University of Washington eScience Institute
Seattle, Washington, United States
Summary
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Rockstar🎓
Top SchoolAriel Rokem is a senior scientist and data-driven neuroinformatics researcher with 17 years of experience building open, reproducible tools for brain imaging and analysis. Based in Seattle, he leads a research group at CAMH and holds affiliations with the Vector Institute, combining academic leadership with hands-on software development. His contributions span core scientific Python projects—from SciPy and nibabel to nipype and visualization library FURY—where he has improved testing, interfaces, and reproducible workflows for neuroimaging. He has a strong track record of integrating statistical methods with practical software (e.g., tools for coherence analysis, visualization of spherical signals, and a Python–MATLAB bridge) that make advanced analyses accessible. Known for clear technical documentation and QA rigor, he elevates usability in complex distributed and Kubernetes-based deployments such as JupyterHub. Trained as a neuroscientist (PhD, UC Berkeley), he blends deep domain knowledge with robust engineering to turn large, messy neuroscience datasets into reproducible science.
17 years of coding experience
6 years of employment as a software developer
PhD Neuroscience, PhD Neuroscience at University of California, BerkeleyMA Cognitive Psychology, MA Cognitive Psychology at The Hebrew University of Jerusalem
German, Swedish, Hebrew
Github Skills (51)
mri10
data-visualizations10
algorithms10
kubernetes10
nipype10
scipy10
registrar10
visualization10
3d-graphics10
jupyterhub10
python10
experimental-design10
brain-imaging10
matplotlib10
testing10
Programming languages (24)
Github contributions (5)
uwescience/shablona
May 2015 - Jun 2019
A template for small scientific python projects
Role in this project:
Data Scientist
Data ScientistContributions:1 review, 119 commits, 71 PRs in 4 years 1 month
Contributions summary:Ariel contributed significantly to the project by implementing core data analysis and modeling functionalities. They added a function to transform experimental data, and introduced functions for cumulative Gaussian calculations and error function optimization. They also integrated Pandas for data handling, enhanced the testing suite, and created a notebook, demonstrating a focus on building a robust and functional scientific Python project.
templatepythonsciencepython-projectsscientific
arokem/python-matlab-bridge
Oct 2012 - Sep 2021
A simple Python => MATLAB(R) interface and a matlab_magic for ipython
Role in this project:Back-end Developer
Back-end DeveloperContributions:4 releases, 244 commits, 78 PRs in 9 years 1 month
Contributions summary:Ariel's contributions primarily involve changes to the Python-MATLAB-bridge's core functionality. They made several documentation improvements, fixing typos and clarifying code comments within the MATLAB files. Further, the user refactored existing code by implementing and enhancing the calling of code on the server by implementing web_eval functionality. Finally, the user implemented features, such as getting the output of figures and stdout, and the related methods.
pythonmatlabmagicoctaveipython
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