📌 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:
- Statistical validity before scale
- Interpretability before optimization
- 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.
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.