Biomedical Engineer developing AI-powered clinical tools at the intersection of machine learning, medical imaging, and computational biology.
Open to collaborations in medical AI, computational neuroscience, and biomedical data science.
🔬 SlicerSEEG Complete 3D Slicer extension for automated SEEG electrode localization. Includes GUI, documentation, public demo and releases.
🤖 CT Threshold Models
ML ensemble for optimizing CT threshold selection in electrode segmentation. Integrates LightGBM, XGBoost, and Random Forest with clinical validation.
🧠 SEEG Automatic Segmentation
End-to-end deep learning pipeline for SEEG electrode localization in 3D CT scans.
🩺 CKD Biomarker Discovery
Machine learning pipeline for chronic kidney disease prediction with patient stratification and early detection models.
🧠 3D Brain Mask Segmentation
UNet-based model for brain mask extraction from post-surgical CTs. Integrated into clinical workflows.
🧬 Synthetic Patient Cohort Generation
Generative models (GANs) for creating synthetic patient cohorts in rare disease research.
📊 Chest X-Ray Pathology Classification
Multi-label deep learning model for detecting pathologies from chest X-rays using transfer learning.
🧪 Bioinformatics Pipelines
Genomics analysis, variant annotation, and sequence alignment pipelines for computational biology research.
AI/ML: PyTorch, TensorFlow, scikit-learn, MONAI, LightGBM, XGBoost
Medical Imaging: 3D Slicer, SimpleITK, NiBabel
Programming: Python, MATLAB, R, SQL
Biocomputing: Bioinformatics tools, HPC clusters, statistical modeling
Generative AI: GANs, VAEs, diffusion models, synthetic data augmentation
