Mohammad Torabi
Graduate Researcher
Montreal, Quebec, Canada
Summary
🤩
Rockstar🎓
Top SchoolMohammad Torabi is a PhD-level graduate researcher and software engineer based in Montreal who specializes in turning messy, high-dimensional neuroimaging time-series into reliable, ML-ready graph features. Over five years he has engineered production-grade tools—like PydFC and a Nipoppy module—that standardized feature extraction across 22 heterogeneous datasets and are used as core dependencies in open analysis packages. He contributes to Nilearn as a core developer, improving large-scale feature computation, CI/CD robustness, and cross-team API design. Mohammad blends hands-on systems work with AI-assisted documentation—building LLM-guided, copy-ready tutorials to cut onboarding friction—and has automated benchmarking pipelines to surface representations that generalize across experiments. He mentors junior researchers and organizes lab-wide knowledge sharing, bridging research, software engineering, and reproducible science. An electrical engineering background combined with neuroscience training gives him a rare ability to translate theoretical biomarkers into scalable, auditable tooling for real-world noisy data.
5 years of coding experience
3 years of employment as a software developer
Electrical, Electronics and Communications Engineering, Electrical, Electronics and Communications Engineering at Sharif University of Technologyallame helliDoctor of Philosophy - PhD, Biological and Biomedical Engineering, Doctor of Philosophy - PhD, Biological and Biomedical Engineering at McGill University
Github Skills (12)
machine-learning10
python10
neuroimaging10
decoding10
brain-imaging10
bids8
connectivity7
imaging7
science6
data-structure5
deep-learning5
neuroscience2
Programming languages (4)
Github contributions (5)
neurodatascience/dFC
Aug 2021 - Apr 2026
An implementation of several well-known dynamic Functional Connectivity assessment methods.
Contributions:4 releases, 7 reviews, 50 PRs in 4 years 7 months
mtorabi59/dFC
Sep 2021 - Jun 2026
an implementation of several well-known dynamic Functional Connectivity assessment methods.
Contributions:1 PR, 591 pushes, 5 branches in 4 years 10 months
connectivityassessmentfunctional-connectivitywell-known
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