📌 AI Systems Engineer & Open Source Builder specializing in Interpretable ML, Vector Databases (Qdrant/Chroma), and Custom AI Agents for global startups & research teams.

"Models should not only predict well — they should explain well."

I approach modeling through three core principles:

1. Statistical validity before scale — Ensuring assumptions and tests are mathematically grounded before scaling computation.
2. Interpretability before optimization — Understanding feature importance and logic paths before squeezing marginal decimal gains.
3. Domain meaning before deployment — Ensuring features translate directly to clinical or business reality.

My research interests include:

• Permutation-based, resampling, and nonparametric inference
• Interpretable and explainable machine learning (post-hoc & intrinsic)
• Dimensionality reduction with geometric and statistical intuition
• Robustness, stability, and noise-aware modeling
• Translating statistical theory into clinically actionable insights

📄 papersearch-mcp Integrates arXiv and Semantic Scholar directly into AI IDEs with page-level PDF extraction and citation graph traversal.	🧬 nf-risk-stratification Nonparametric Combination (NPC) and bootstrap-based clinical risk stratification model for rare-disease clinical research.
🏥 clinical-nlp-ai-platform Clinical NLP platform for unstructured record mining, entity extraction, clinical sentiment analysis, and automated ICD coding.	📊 big-data-clustering-analytics Scalable clustering framework (KMeans++, DBSCAN, BIRCH, OPTICS) applied to NYC Taxi mobility (12M+) and fraud detection.
🔬 nonparam-comb General-purpose statistical library for Nonparametric Combination of permutation tests and multi-criteria severity ranking.	🔄 vector-sync-engine Containerized cross-engine vector database synchronization tool to migrate and replicate embeddings between Chroma and Qdrant.

I actively contribute to major AI/ML open-source projects with bug fixes, performance improvements, and new features:

• lancedb/lancedb#3661 — Workspace Manifest & Dependency Refactoring (Approved ✅)

  • System Impact: Resolved a blocking issue in the workspace linter (cargo deny) triggered by yanked upstream crates (like spin v0.10.0), and resolved duplicate developer dependency declarations.
  • Key Technologies: Rust, Cargo Workspace, CI/CD Linters.

• milvus-io/pymilvus#3686 — Environment Configuration Security & Type Safety (Under Review 🔍)

  • System Impact: Patched SDK settings parser to prevent unauthorized loading of .env configurations when the PYTHON_DOTENV_DISABLED flag is set, and resolved a runtime TypeError caused by uninitialized variables.
  • Key Technologies: Python, python-dotenv, Environment Management.

• chroma-core/chroma#7432 — Test Suite Isolation & Connection Refactoring (Under Review 🔍)

  • System Impact: Redesigned test fixtures for FastAPI SSL connections to run isolated database instances, resolving race conditions and connection leaks in the testing environment.
  • Key Technologies: Python, Pytest, FastAPI, SQLite.

C# / .NET AI Trading Agent & Portfolio Strategy Validator using SPRT, Sharpe/Sortino Ratios

Ruby AI Financial Fraud & Anomalous Transaction Auditor with consensus verification

Java Demographic Fairness Credit Risk Evaluator with bias metrics

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

• 🧬 High-Precision Biomedical AI: Designing mathematically rigorous, permutation-based nonparametric statistical engines for rare diseases and small-sample clinical datasets.
• 📈 Robust & Explainable ML (XAI): Developing invariant feature attribution and post-hoc explanation frameworks that maintain mathematical consistency under severe covariate and distribution shifts.
• 🛡️ Production AI Guardrails & Validation: Building automated evaluation suites, stability tests, and performance diagnostics to guarantee safety, reproducibility, and alignment in high-stakes clinical and financial ML deployments.
• 🔧 Developer Tooling & Agentic Integrations: Architecting FastMCP (Model Context Protocol) servers to seamlessly link scientific databases (arXiv, PubMed, gnomAD) with next-generation LLM agents and coding systems.