From a young age, I was naturally drawn to math and problem solving — the thrill of cracking a tough problem always excited me.

When I started college at Vanderbilt University, my path seemed certain: pre-med. Yet, life has a funny way of redirecting us. To fulfill a basic liberal arts requirement, I enrolled in CS 1101, an introductory programming course in Java.

What I expected to be a simple box-checking exercise turned into something much more. Programming came incredibly naturally to me — a surprising discovery, especially since growing up, I never imagined myself in tech. In my mind, software engineering was reserved for “geeks,” and I wasn’t sure I fit that mold. But as the semester unfolded, I realized this wasn’t about stereotypes or labels — it was about problem-solving, creativity, and building something from scratch, all things I loved.

Alongside my coursework, I worked in several research labs focused on the intersection of medicine and technology. One of the projects that shaped my interests most was a virtual reality-based community designed to help individuals manage substance addiction. Our goal was to create immersive environments to curb cravings and promote recovery.

I initially joined the lab through my pre-med track, but the work I did was heavily technical — I used MATLAB to scrape data from Reddit, building a machine learning model that could predict withdrawal struggles based on user posts. It was my first real taste of applying computational methods to solve real-world problems, and it lit a spark. It showed me that technology wasn’t just a tool — it was a bridge between disciplines, a way to innovate in fields I deeply cared about.

Encouraged by that experience, I pivoted my academic focus, eventually double majoring in Computer Science and Applied Mathematics while completing a minor in Neuroscience. Even as I transitioned away from pre-med, my passion for understanding the brain never faded — a passion that continues to fuel my curiosity today.

In fact, it was my background in neuroscience that initially sparked my fascination with artificial intelligence. Neural networks, the cornerstone of modern AI, felt like a natural extension of my academic interests. To this day, I still enjoy diving into neuroscience podcasts and staying curious about how biological intelligence informs machine learning.