About Me

🎯 What I Do

I specialize in evolving traditional test automation into Intelligent Quality Engineering. My work, which began with published research into spatial data clustering, now focuses on merging Python and Local LLMs to solve the frustrations of manual testing and build tools for automation where traditional methods fail.

I build tools that don't just run scripts but understand them—from self-healing frameworks that adapt to UI changes, to offline AI coding assistants that secure data while boosting productivity.

💡 My Philosophy

I don't just automate workflows; I engineer the tools that make automation smarter, faster, and more resilient.

"Why do manual work when you can design systems that do it for you?"

📚 Research Foundations

My approach to engineering is rooted in a background in data mining and algorithmic research. Before moving into automation, I co-authored "Clustering Concepts and Techniques - For Big Spatial Data", focusing on the Triangle-Density Based Clustering Technique (TDCT).

Today, I apply those same principles of pattern recognition and data clustering to solve complex problems in test stability and self-healing automation frameworks. You can find a record of my academic research and citations on Google Scholar.

🛠️ Core Expertise

☕ Fun Fact

Powered by code, curiosity, and an unhealthy amount of coffee. When I'm not building automation tools, you'll find me exploring the latest in AI, contributing to open source, or debugging that one flaky test that only fails on Fridays.