Bridging Retail Operations, Artificial Intelligence & Independent Research.
I am a Retail Ops Professional transitioning into AI Engineering. I build practical systems that solve real-world problems — from automating stocktake variances to detecting zero-day phishing attacks. I also pursue independent research at the intersection of physics, mathematics, and computation.
Configuration Space, Transcendental Structure, and π-Anomalies at Sub-Planck Scales
Published April 2026 · Zenodo (CERN) · Submitted to Foundations of Physics (Springer)
Key contributions:
- Time is a derived ratio between physical processes, not a fundamental axiom — Newton, Einstein, and Schrödinger recovered from five relational axioms without invoking time
- The three-body problem reduces to geodesic flow on a 2-sphere S² — quantum mechanics solves it exactly where classical mechanics cannot
- The Planck scale is a precision limit, not an ontological wall — π, which defines it, has no final digit
- First base-12 digit search for physical constants in π: electromagnetic constants appear 3.7–4.9× earlier in base-12 π than base-10
- Five original formulas including the Connectivity Force Law
F = −∇(ln κ)and the Geometric Relational HamiltonianĤ_Ω - Mohist relational physics (~400 BCE) and Daoist cosmogony formally mapped to the axiom system
"The universe is a circle. We have been writing its circumference in decimals."
📎 Full paper (open access, 31 pages): https://doi.org/10.5281/zenodo.19371442
- Languages: Python (Pandas, PyTorch, NumPy, Scikit-Learn)
- Engineering: Docker, GitHub Actions (CI/CD), FastAPI, Pytest
- Focus: MLOps, Process Automation, Deep Learning, Cybersecurity
Building robust, modular systems for real-world defense and search.
| Project | Description | Tech Stack |
|---|---|---|
| Phishing Detection System | 🚀 Production Security Engine. A hybrid 3-layer defense system (Whitelist → Heuristic Rules → XGBoost) achieving 99.32% F1-score. Features Real-time Link Expansion, Typosquatting Detection, and a robust CI/CD Pipeline. | Python, XGBoost, GitHub Actions, Pytest |
| Silver Retriever | Offline RAG System. A modular search engine designed for legacy hardware (No GPUs). Features a Plugin Architecture ("The Brain") to detect user intent (Deadlines, Tasks) using TF-IDF instead of heavy LLMs. Includes Smart Chunking. | Python, Streamlit, Scikit-Learn, GitHub Actions |
Engineering intelligent systems that run efficiently on constrained hardware.
| Project | Description | Tech Stack |
|---|---|---|
| Memory Bear (Legacy Edge) | 🐻 Cognitive Agent. A local AI agent running on a 2017 MacBook Air (Intel). Implements Ebbinghaus Forgetting Curves to dynamically manage context window limits. Features Quantized Inference and a biologically inspired Memory Graph. | Python, Llama.cpp, ChromaDB, NetworkX, Phi-3 |
My core focus: Bringing engineering rigor to supermarket logistics.
| Project | Description | Tech Stack |
|---|---|---|
| FreshGuard V2 (Retail Waste) | 🚀 Flagship. Production-grade forecasting engine reducing perishable waste. Features Docker, CI/CD, and Streamlit. The engineered evolution of V1. | Python, Docker, Pytest, Holt-Winters |
| Stocktake Variance Reporter | Automation Tool. A full-stack utility designed to cut stocktake reporting time by 99%. Includes "Theft Detection" logic and a web UI. | FastAPI, Docker, Pandas |
| Enterprise Retail Solution | Advanced R&D. A predictive analytics experiment utilizing Armstrong Cycle Transformers to forecast complex sales demand patterns. | Time-Series, PyTorch Transformers |
| Retail Waste System (V1) | Prototype. My initial menu-driven application for inventory tracking. Focuses on core CRUD operations and basic analytics. | Python, Matplotlib, Pandas |
Applying AI to decode complex genomic sequences.
| Project | Description | Tech Stack |
|---|---|---|
| Genomic Decoder V2 | Advanced Pipeline. Refined Deep Learning architecture for DNA sequencing. Focuses on modular code structure and improved inference performance over the original. | PyTorch, BioPython, CI/CD |
| Genomic Decoder (FlyOS) | Research Implementation. An end-to-end pipeline that reads raw DNA sequences to predict gene expression. Features O(1) lazy-loading for massive datasets. | PyTorch, Transformers |
| Project | Description | Tech Stack |
|---|---|---|
| O-Level Predictor | First App. My very first attempt to convert a Python calculation script into an interactive web app using Streamlit. | Python, Streamlit |