--- ### 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 ]`**   -a8a8a8?style=for-the-badge&labelColor=1a1a1a)   *`[ 🧬 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 ```