I build reproducible applied AI systems across computer vision, medical AI, remote sensing, vision-language workflows, sustainability applications, and environmental machine learning.
My current portfolio is centered on five finalized Python-first projects. Each repository emphasizes clear documentation, reproducible structure, evidence tracking, limitation awareness, and responsible project framing.
Portfolio website: mohamad-sabbagh-ai-research-portfolio--za9699185.replit.app
| Order | Project | Focus | Repository |
|---|---|---|---|
| 1 | ECG Image-to-Signal Reconstruction | Medical AI, computer vision, signal reconstruction | medical-ecg-image-to-signal-reconstruction-pipeline |
| 2 | Satellite Land Classification with CNN/CNN-ViT | Remote sensing and comparative vision architectures | satellite-land-classification-cnn-vit |
| 3 | Waste Classification using VGG16 Transfer Learning | Sustainability-focused image classification and model release | waste-classification-transfer-learning |
| 4 | Aircraft Damage Classification + BLIP Reports | Inspection-support computer vision and vision-language workflow | aircraft_damage_vgg16_blip |
| 5 | Rainfall Prediction in Australia | Tabular ML, metric provenance, and environmental prediction | rainfall-prediction-classifier |
- Medical AI and signal-aware computer vision — ECG image-to-signal reconstruction with synthetic benchmarking, QC checks, failure-mode analysis, and pipeline compatibility tooling.
- Remote sensing and visual classification — CNN/CNN-ViT experimentation for agricultural vs non-agricultural land classification, with metric provenance and limitation tracking.
- Applied inspection AI — aircraft damage classification combined with BLIP-based caption/report generation, framed as inspection-support rather than certified maintenance tooling.
- Sustainability-focused ML — VGG16 waste classification with a bundled checkpoint, direct inference script, model-release documentation, and responsible use boundaries.
- Environmental tabular ML — rainfall prediction with classical ML models, leakage/split-risk awareness, temporal validation protocol, and calibration/interpretability planning.
- Python-first ML repository design
- Computer vision and transfer learning
- TensorFlow/Keras and PyTorch workflows
- Classical ML with scikit-learn and XGBoost
- Research evidence packs, metric provenance, and reproducibility checklists
- Responsible project framing: limitations, safe claims, and future-work separation
A detailed research dossier for the five projects is available here:
AI Research Portfolio — Research Dossier
It provides per-project briefs, evidence traceability for every claim, a structured reading guide for academic reviewers, and my research statement.
These repositories are research and portfolio projects. They do not claim clinical validation, production deployment, certified inspection readiness, state-of-the-art status, or operational forecasting service status unless explicitly supported in the relevant repository documentation.