My Nerdy Side

I tend to get distracted by origins. When a formula or algorithm appears in front of me, my instinct is not to memorize it, but to trace it back to first principles and ask whether it could be built differently. I am drawn to the moment when an idea feels inevitable rather than imposed—when structure emerges from reasoning instead of rules.

That instinct came alive during an engineering project on voice-controlled windshield wipers. While working on a control-system problem, I became fascinated by how a motor transitions smoothly rather than abruptly between states. I didn’t begin by searching for a known solution; instead, I tried to imagine what kind of mathematical shape would feel “right.” I ended up deriving a sigmoid-like function on my own, not because I knew it was widely used, but because it captured the gradual behavior I was observing. When it still didn’t fit perfectly, I modified it further to better model the motor’s nonlinear response. In that process, mathematics felt less like something I was learning and more like something I was inventing.

The same way of thinking shapes how I code. When implementing a complex loop, I often rewrite solutions not just to make them work, but to make them clearer. I enjoy compressing ideas, replacing procedural steps with more elegant structures, and optimizing not only for efficiency but also for readability. In one project, I replaced a long procedural structure with compact Python lambda functions, simply because I enjoyed seeing how much logic could be expressed cleanly.

What I hope readers understand is that my academic path has been guided less by acceleration and more by depth. I learn best when curiosity determines the structure—when I can revisit assumptions, test edge cases, and refine ideas long after a solution exists. This quiet, persistent curiosity shapes how I study, how I build, and how I approach problems with real consequences, and it is what matters most to me as a learner.