---

Education

	Institution	Credential	Year
🌲	Stanford University	Data Science in Medicine	2025
🦅	SUNY Stony Brook	M.S. Biomedical Informatics	2024–25
🏛️	Sapienza University, Rome	B.S. Bioinformatics	2020–23
🇪🇸	San Jorge University, Spain	Erasmus Exchange	2023

⚡ I operate at the intersection of genomics and GPU clusters, turning raw EHR signals into early clinical warnings. My work spans FHIR pipelines, HIPAA-grade governance, and deep learning systems validated on real patient outcomes across 3 continents. 📡   CLINICAL IMPACT METRICS ┌─────────────────────────────────────────────────────────────────────────────┐ │ OUTCOME DASHBOARD · Real Metrics · Real Patients · Real Stakes │ ├──────────────────────────────┬────────────────────┬─────────────────────────┤ │ PROJECT │ METRIC │ CLINICAL VALUE │ ├──────────────────────────────┼────────────────────┼─────────────────────────┤ │ ECG Fall Risk Detection │ 70.99% Accuracy │ Elderly inpatient │ │ (XAI / SHAP Interpretable) │ ████████████░░░░ │ safety & intervention │ ├──────────────────────────────┼────────────────────┼─────────────────────────┤ │ GNN 30-Day Readmission │ AUROC +11% │ Multi-modal EHR │ │ (Stony Brook Medicine) │ ████████████████ │ predictive precision │ ├──────────────────────────────┼────────────────────┼─────────────────────────┤ │ U-Net MRI Segmentation │ Dice Score +14% │ Surgical planning │ │ (Published · Sapienza 2023) │ ████████████████ │ boundary precision │ ├──────────────────────────────┼────────────────────┼─────────────────────────┤ │ Published Research │ 1 Paper · 2023 │ Peer-reviewed CV Lab │ │ (Sapienza / Prof. Pannone) │ ████ │ Computer Vision + MRI │ └──────────────────────────────┴────────────────────┴─────────────────────────┘ ⚙️   TECHNOLOGY MATRIX [ 🏥 HEALTHCARE SYSTEMS ] [ 🧠 AI / MACHINE LEARNING ] [ 🧬 BIOINFORMATICS ] [ 📊 VISUALIZATION & ANALYTICS ] 🔬   RESEARCH SYSTEMS 🧠   [2025]   Interpretable Deep Learning — ECG-Based Fall Risk Detection SYSTEM : XAI Clinical Framework · Fall Risk Stratification in Elderly Inpatients APPROACH : ECG time-series → PyTorch model → SHAP explainability layer OUTCOME : 70.99% accuracy — every prediction comes with a clinician-readable rationale STACK : Python · PyTorch · ECG Signal Processing · SHAP · Clinical Validation IMPACT : Clinicians can interrogate predictions — not just receive a black-box score 🕸️   [2024–2025]   Graph Neural Networks — 30-Day Hospital Readmission · Stony Brook Medicine SYSTEM : Heterogeneous Graph Neural Network on Multi-Modal EHR Data INPUT : Patient history · Lab results · Diagnoses · Procedures (as graph nodes) OUTCOME : AUROC +11% over clinical baseline scoring systems PROGRAM : Biomedical Informatics · SUNY Stony Brook · Built within Stony Brook Medicine STACK : Python · PyTorch Geometric · Epic EHR · Graph Construction Pipeline 🫁   [2023 · Published]   U-Net MRI Segmentation — Sapienza Computer Vision Lab SYSTEM : Enhanced U-Net for Clinical MRI Tumor Boundary Segmentation DATA : 1 TB of multi-modal imaging data · Processed via Snakemake on HPC Cluster OUTCOME : Dice Score +14% · Used for precision surgical planning PUBLISHED : Under Prof. Daniele Pannone · Sapienza University of Rome · October 2023 STACK : Python · PyTorch · Snakemake · HPC Cluster · Medical Imaging Pipeline 📊   GITHUB INTELLIGENCE    🏅   CERTIFICATIONS & CREDENTIALS             "I work where genomics meets GPU clusters — making healthcare as intelligent as it deserves to be." — Mittal Kumar