Kevin Sitek
Research Assistant Professor at Northwestern University
Chicago, Illinois, United States
Summary
👤
Senior🎓
Top SchoolKevin Sitek is a Research Assistant Professor and neuroscientist in Chicago with 11 years of experience specializing in subcortical neuroimaging of speech and hearing. He combines rigorous PhD-level research from Harvard with practical software engineering skills, contributing to major open-source medical imaging projects like DIPY and the 3D rendering library FURY to improve performance and visualization. His work spans algorithmic backend improvements—adding parallel processing to image reconstruction workflows—and careful rendering tweaks that enhance scientific visualization and testing. Comfortable at the intersection of neuroscience and engineering, he translates complex brain-imaging problems into reproducible, high-performance code used by the community.
11 years of coding experience
BA, BA at University of California, BerkeleyDoctor of Philosophy - PhD, Doctor of Philosophy - PhD at Harvard University
Github Skills (13)
medical-imaging10
parallel-processing10
scientific-visualization10
3d-graphics10
diffusion-mri10
tractography10
python10
numpy10
shader9
neuroimaging9
machine-learning9
vtk9
game-engine8
Programming languages (4)
Github contributions (5)
fury-gl/fury
Mar 2019 - Mar 2019
FURY - Free Unified Rendering in pYthon.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:10 commits, 1 comment in 1 day
Contributions summary:Kevin primarily contributed to the `fury` project by modifying the `actor.py` file. They focused on enhancing the color representation of sphere vertices within the 3D rendering context. Their changes included modifying color definitions, updating documentation, and addressing syntax issues related to error handling, and they also added tests related to specific color output behavior. These commits indicate a focus on improving the visual aspects and internal logic of the rendering pipeline.
pythonshadersfurysimulationrendering
dipy/dipy
Mar 2019 - Mar 2019
DIPY is the paragon 3D/4D+ medical imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.
Role in this project:Backend Developer
Backend DeveloperContributions:6 commits, 1 PR, 5 comments in 11 days
Contributions summary:Kevin primarily contributed to the backend of the `dipy` library by adding parallel processing capabilities to CSA and CSD workflows. They modified the `reconst.py` file to include parameters for parallelization and adjusted default values. The user also added tests for the parallel flag and corrected the documentation regarding this flag. Their work focused on improving the performance of image reconstruction processes within the library.
signalpythonmicrostructurespatialtractography
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