Nil Goyette
Programmer Analyst at Imeka
Sherbrooke, Quebec, Canada
Summary
🤩
Rockstar🎓
Top SchoolNil Goyette is a Programmer Analyst with 12 years of experience specializing in computer vision and medical imaging, blending research-grade algorithms with production-ready software. Based in Sherbrooke, Quebec, Nil is the main developer of MI-Brain and a polyglot programmer (Python/Cython, C++, Rust) focused on performance optimization for heavy image processing workloads. He contributes to prominent open-source projects such as DIPY—where he implemented an unbiased line-based streamline filtering method—and has worked across both academic and industry settings, including Imeka and IBM. Comfortable at the intersection of development and research, Nil pairs algorithmic rigor from his MSc in computer vision with hands-on system engineering to deliver efficient, reproducible imaging tools.
12 years of coding experience
2 years of employment as a software developer
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at Université de Sherbrooke
French, English, Japanese
Github Skills (19)
python10
diffusion-mri10
cython10
medical-imaging10
data-structure9
algorithm9
algorithms9
numpy9
data-structures9
machine-learning8
tractography8
trac7
registers5
registrator5
segmentation5
Programming languages (8)
Github contributions (5)
dipy/dipy
Jun 2016 - Dec 2017
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:
Back-end Developer / Data Scientist
Back-end Developer / Data ScientistContributions:19 commits, 7 PRs, 129 comments in 1 year 5 months
Contributions summary:Nil primarily contributed to the `dipy/dipy` repository by implementing and testing a line-based target function for streamline filtering, a method that can avoid biased streamlines-based metrics. Their work focused on modifying and adding functionality within the `tracking` module, specifically the `vox2track` and `utils.py` files. The contributions include the implementation of the `target_line_based` function and its associated test, demonstrating a focus on algorithms and data processing within the domain of medical imaging.
signalpythonmicrostructurespatialtractography
nilgoyette/nifti-rs
Nov 2017 - Nov 2022
Rust implementation of the NIfTI-1 format
Contributions:33 pushes, 41 branches in 5 years 1 month
rustnifti
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