Research Software Engineer & Data Scientist · Stanford Translational AI Lab (STAI) Biomedical Imaging · Neuroinformatics · Reproducible Research Software

I develop and maintain open-source research software infrastructure for large-scale biomedical imaging and neuroscience research at Stanford University. My work focuses on standards-aware preprocessing pipelines, automated quality control, and reproducible deployment for NIH-funded studies in Alzheimer's disease (AD/ADRD), Parkinson's disease, and related neurodegenerative disorders.

I work as the engineering counterpart to research projects spanning neuroimaging, clinical video analysis, and large-scale biomedical data integration.

End-to-end structural MRI preprocessing framework:

• SynthStrip-based brain extraction
• MNI152 registration
• N4 bias-field correction
• Intensity normalization
• Automated overlay-based QC reporting
• Containerized and cluster-ready execution

Lightweight, reproducible diffusion MRI preprocessing and QC framework:

• Eddy-current and motion correction
• Tensor fitting and diffusion feature extraction
• Automated quality-control visualization
• BIDS-compatible organization
• Containerized execution

BIDS-compatible functional MRI preprocessing pipeline:

• Motion correction
• Registration and normalization
• Automated QC reporting
• BIDS-compatible workflow structure
• Cluster-ready execution

All pipelines follow established research software engineering practices:

• Semantic versioning with tagged stable releases
• Pinned dependencies for reproducible execution
• Containerized deployment using Docker and Singularity
• Automated quality-control reports with overlay visualizations
• BIDS-compatible input/output organization
• Cluster-ready Slurm execution workflows
• MIT License for permissive academic reuse
• Zenodo DOI archiving for citable releases

• Reproducible neuroimaging preprocessing pipelines
• Automated MRI quality control
• Clinical video and motion-analysis infrastructure
• Standards-aware biomedical data engineering
• Privacy-preserving clinical data processing

Stanford Translational AI Lab (STAI) School of Medicine · Department of Psychiatry & Behavioral Sciences PI: Ehsan Adeli, PhD

The pipelines and infrastructure developed here support ongoing AD/ADRD and Parkinson's disease imaging analyses across NIH-funded studies in the Adeli lab.

• Open-source MRI preprocessing and QC frameworks: sMRI, DTI, and fMRI
• Scalable containerized neuroimaging workflows
• Standards-aligned biomedical data pipelines
• Automated validation and reporting systems
• NeuroQA: brain MRI reasoning benchmark under review (https://neuroqa.stanford.edu)

• 📧 mabbasi@stanford.edu
• 🌐 https://stanford.edu/~mabbasi/
• 🔗 Google Scholar
• 🔗 ORCID