--- ### 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 |
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> ⚡ 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.
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## 📡 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 ]`**
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*`[ 🧬 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 ``` 
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``` "I work where genomics meets GPU clusters — making healthcare as intelligent as it deserves to be." — Mittal Kumar ``` 
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