Curiosity is the engine. In a field that reinvents itself every few months in regards of new architectures, new tooling and new paradigms, standing still is the only real way to fall behind. I chase understanding for its own sake, and that instinct is what keeps me learning, experimenting, and shipping through every shift in the landscape.
My edge: deep CS fundamentals + modern AI orchestration = moving from concept to production at unprecedented speed. I don't just solve problems: I lead through them, turning every challenge into a strategic data point for growth.
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Satellite-based crop classification using Sentinel-1 & Sentinel-2 imagery. End-to-end ML pipeline with spatial CV, vegetation-index feature engineering (NDVI, EVI, NDWI), and ensemble models (XGBoost, LightGBM, CatBoost) across two regions. 7.36M+ observations processed. |
Predicting hospital readmissions for diabetic patients. Covers data cleaning, feature engineering, class balancing with SMOTE, model evaluation, and feature-importance analysis for clinical interpretability. |
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End-to-end ML pipeline predicting customer churn for SyriaTel. Python + scikit-learn with feature engineering, model comparison, and actionable retention recommendations. |
Data-driven analysis of movie genres, ROI, directors, and language trends — translating rows of film data into strategic production decisions. |
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NTSB aviation-accident data (1962–2023) analyzed to assess airplane risk for a business-expansion decision. Full EDA workflow — cleaning, imputation, visualization, and strategic recommendations. |
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Open to conversations on ML, geospatial AI, FinTech data science, or anything where rigorous engineering meets data-driven impact. The best collaborations start with a message.
