📌 I work at the intersection of statistical theory, interpretable machine learning, and real-world clinical data.

Focus: Interpretable ML · Nonparametric Statistics · Clinical & Scientific AI

“Models should not only predict well — they should explain well.”

I approach modeling through three principles:

1. Statistical validity before scale
2. Interpretability before optimization
3. Domain meaning before deployment

My research interests include:

• interpretable and explainable machine learning (post-hoc & intrinsic)
• permutation-based, resampling, and nonparametric inference
• dimensionality reduction with geometric and statistical intuition
• robustness, stability, and noise-aware modeling
• translating statistical theory into clinically actionable insights

Used primarily for statistical modeling, interpretability research, and reproducible scientific workflows.

An Model Context Protocol (MCP) server for searching, analyzing, and retrieving academic papers.

• Purpose: Integrates arXiv and Semantic Scholar directly into AI coding assistants (like Claude Code/Desktop).
• Features: Page-level text extraction from PDFs using PyMuPDF (fitz), citation graph traversal, and advanced search filters.
• Tech: FastMCP, Python, HTTPX, PyMuPDF.

Nonparametric Combination (NPC) and bootstrap-based risk stratification model.

• Purpose: Reproducible statistical analysis framework for our peer-reviewed research in Necrotizing Fasciitis.
• Tech: Python, NumPy, Pandas, Scipy.

End-to-end clinical NLP platform for medical entity extraction, clinical sentiment analysis, topic modeling, and automated ICD coding.

• Purpose: Privacy-preserving processing and deep learning pipelines for unstructured health records.
• Tech: Python, PyTorch, Transformers, FastAPI.

I actively participate in bug triaging, Q&A, and technical discussion forums across key scientific Python and developer libraries:

• scikit-learn: Contributing technical solutions for community questions on discussions (e.g. customized Gower's distance implementations, tree routing diagnostics).
• FastAPI / Next.js: Supporting developers in resolving configuration and execution issues in production settings.

Permutation-Based Analysis of Clinical Variables in Necrotizing Fasciitis Using NPC and Bootstrap
Mathematics, MDPI (2025)

This work introduces a permutation-based, nonparametric framework for analyzing clinical variables in necrotizing fasciitis. By combining Nonparametric Combination (NPC) methodology with bootstrap techniques, the study enables robust inference under small-sample and distribution-free conditions, with an emphasis on interpretability and clinical relevance.

The study demonstrates how permutation-based inference can outperform classical parametric approaches in rare-disease clinical settings.

🔗 https://www.mdpi.com/2227-7390/13/17/2869

• permutation-based inference for small-sample biomedical studies
• interpretability under distribution shift
• robustness diagnostics for clinical ML models
• statistical foundations of explainable AI

• 📘 math and statistics-first explanations of ML & AI
• 🧪 reproducible experiments with robust inference
• 📊 real-world clinical and analytical datasets
• 🧠 research-oriented notebooks focused on why, not just how

⭐ Thoughtful questions and rigorous discussions are always welcome.