About

My career has followed a somewhat unconventional path. I started in molecular biology and laboratory science, where I developed a strong appreciation for the practical realities behind scientific data—how experiments are designed, how samples move through a workflow, and how decisions made in the laboratory ultimately affect the data produced downstream.

Over time, I became increasingly interested in what happens after the data is generated. That led me toward programming, bioinformatics, data analysis, and eventually machine learning and AI. Learning to work computationally gave me another way to approach problems I encountered in biology: building tools to automate repetitive work, analyzing larger datasets, identifying patterns, and making complex information easier to understand.

Today, my work sits at the intersection of molecular biology, genomics, data science, and technology. I enjoy projects where understanding the underlying science is just as important as writing the code, and where a well-designed computational solution can make a real laboratory or analytical workflow better.

I’m particularly interested in genomics, NGS, bioinformatics, workflow automation, and AI-driven approaches to biological data. My goal is to continue building at that intersection—using both scientific understanding and computational tools to solve problems that neither discipline could address as effectively on its own.