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    pdftitle={Wright Scholar Essay},
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	\textbf{\large Wright Scholar Essay (Topics 1 and 3)}
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\vspace{12pt}
28 Squiggly red underlines. Nearly every line of my code had errors. Null
pointers, incompatible types, undefined variables, Gradle sync errors: I had
encountered them all. It was February 2024, my freshman year, and we had ten
minutes to take the field for our First Tech Challenge (FTC) League Finals.
My heart pounded as keys clattered beneath my flying fingers. My code was
broken, and for the finals, it had to work. The merciless clock ticked away,
and with seconds to go, I finally compiled the code. There was no time to
test, hardly any to breathe. We took the field, and my finger hovered over
the play button. Time paused. The buzzer sounded, and I pressed play. Success.
In two minutes and thirty seconds, we won.

Seven months earlier, I didn't know what a variable was. I was fully into
music, and programming was yet to cross my radar. When my friend started a
robotics team, I joined on a whim. It was that abrupt decision that started
my journey into robotics. Progress was painstakingly slow; it took me two
full months to make a motor turn. But gradually, I became hooked. Like a
sponge, I absorbed everything: tutorials, documentation, and even Stack
Overflow threads. Eventually, I taught myself enough Java to become a
functional FTC programmer.

As the season progressed, we became a competitive team, and my knowledge was
expanding in parallel. On that competition day, something just clicked. It
wasn't the win that truly made me happy. It was the realization that my own
code produced tangible outputs. At that very moment, I knew that I wanted to
continue working in a STEM field, and I was ready to keep coding on.

But that readiness was tested in September 2024. Somewhat naively, I decided
to build a machine learning model to predict gait patterns in Parkinson's
Disease for my sophomore-year Science Fair project. The only problem is that
I had no clue how. So I dove in: Python syntax, NumPy arrays, signal filtering,
feature extraction, and model architectures. I had entered unfamiliar territory,
and each concept I learned brought new confusion. After two months of relentless
reading, coding, and debugging, I managed to transform raw sensor data into a
working classification model. Somewhere between the first error message and
the final 96\% accuracy, I had begun to absorb a new discipline.

I could have stopped there, but I realized that a working model on my laptop
wasn't going to help any Parkinson's patients, and I needed to embed my model
into a complete hardware device. This task was beyond daunting, as I had to
venture into the foreign territory of hardware and electrical engineering.
With my engineering teacher guiding me, I eventually learned the basics. After
countless 2 AM KiCAD tutorial sessions, I finally had a working design for a
custom printed circuit board (PCB) housing a sensor and microcontroller. Two
weeks later, my PCB arrived, and after soldering all my components, it didn't
work. My heart sank. In desperation, I resoldered each joint carefully and
tried again. Somehow, it worked. After writing some C++ software for the
device, I had something that actually worked. The project eventually made it
to the International Science and Engineering Fair (ISEF), placing 3rd in
Robotics and Intelligent Machines. What struck me most wasn't the placement,
but the fact that six months earlier, I wouldn't have understood any of it.

Throughout high school, I've taught myself many disciplines, from FTC
programming in Java to designing circuits. The Wright Scholar program provides
an opportunity to apply my skills to current and critical research. I'm
intrigued by AFRL's Sensors Directorate, where I hope to deepen my understanding
of signal processing while contributing to sensor exploitation technologies.
I'm equally drawn to Human Performance Wing's work with multimodal sensing to
monitor and enhance human performance. Moreso, the chance to work alongside
domain experts who can accelerate my growth as an engineer and developer is
invaluable to me. Whether working with sensor fusion or biomedical sensing,
as a sponge eager to learn, AFRL is exactly where I need to be.

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