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@@ -57,56 +57,66 @@
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\vspace{12pt}
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294 Squiggly red underlines. Nearly every line of my code had errors. Null
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pointers, incompatible types, undefined variables, Gradle sync errors---I had
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encountered them all. It was February 2024, my freshman year, and we had ten
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minutes to take the field for our First Tech Challenge (FTC) League Finals.
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My heart pounded as keys clattered beneath my flying fingers. My code was
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broken, and for the finals, it had to work. The merciless clock ticked away,
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and with seconds to go, I finally compiled the code. There was no time to
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test, hardly any to breathe. We took the field, and my finger hovered over
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the play button. Time paused. The buzzer sounded, and I pressed play. Success.
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In two minutes and thirty seconds, we won.
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% Essay content begins here
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294 squiggly red underlines. Nearly every line of my code had errors. Null pointers,
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incompatible types, undefined variables, Gradle sync errors---I had seen it all, and each error felt like a
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dissonant chord demanding resolution. It was February 2024, my freshman year, and we had ten minutes to
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take the field for our First Tech Challenge (FTC) final match. My heart raced as I thumped the keyboard in a
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frenzy. My code was broken, and for the final match, it had to work. Time raced faster than it ever used to, and
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I finally compiled the code.
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There was no time to test, hardly any to breathe, and before I knew it, we were on the field with my index
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finger hovering over the large play button. Time paused. I heard the buzzer and pressed play. Success.
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In two minutes and thirty seconds, we became league champions.
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Seven months earlier, I didn't know what a variable was. I was fully into
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music, and programming was yet to cross my radar. When my friend started a
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robotics team, I joined on a whim. It was that abrupt decision that started
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my journey into robotics. Progress was painstakingly slow; it took me two
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full months to make a motor turn. But gradually, I became hooked. Like a
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sponge, I absorbed everything: tutorials, documentation, and even Stack
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Overflow threads. Eventually, I taught myself enough Java to become a
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functional FTC programmer.
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It was almost hard to believe that seven months back, I didn't know what a variable was.
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I was fully into music, and programming was not even an afterthought.
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It was a mere coincidence that my neighbor (and good friend) decided to start a robotics team, and given the minimal investment, I
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joined. Like nearly all of my endeavors, my FTC journey began with a Google search. I was learning at a snail's pace, and it
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had taken me two months to simply make a motor move. Soon, I was hooked. Like a sponge, I was absorbing everything I had to learn,
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and I had eventually taught myself enough Java to become a functional FTC programmer.
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As the season progressed, we became a competitive team, and my knowledge was
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expanding in parallel. On that competition day, something just clicked. It
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wasn't the win that truly made me happy. It was the realization that my own
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code produced tangible outputs. At that very moment, I knew that I wanted to
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continue working in a STEM field, and I was ready to keep coding on.
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As the season progressed, my sponge was
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soaked, and we were a top competitive team by February. On competition day, the lightbulb within me finally clicked.
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The joy I experienced wasn't just from our robot picking up and scoring pixels, but from the fact that the code I had recently learn to write
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was resulting in a tangible output that I could witness. It was that moment when I decided to pursue a STEM career. I was
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no longer just a high school student; I was a STEM student, and I was ready to help change the world.
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But that readiness was tested in September 2024. Somewhat naively, I decided
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to build a machine learning model to predict gait patterns in Parkinson's
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Disease for my sophomore-year Science Fair project. The only problem is that
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I had no clue how. So I dove in: Python syntax, NumPy arrays, signal filtering,
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feature extraction, and model architectures. I had entered unfamiliar territory,
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and each concept I learned brought new confusion. After two months of relentless
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reading, coding, and debugging, I managed to transform raw sensor data into a
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working classification model. Somewhere between the first error message and
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the final 96\% accuracy, I had begun to absorb a new discipline.
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But that readiness was tested in September 2024. In a spur of ambitious insanity,
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I had committed to building a machine learning model to predict gait patterns in Parkinson's Disease for my sophomore-year
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Science Fair project. The problem: I had no clue how to. And so I learned. Python syntax, NumPy arrays, signal filtering,
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feature extraction, and model architectures. I had entered a brand new domain, and each concept seemed to confuse me in a different way.
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After two months of painfully laborious learning, coding, and debugging, I was finally able to transform raw sensor data into a
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functional and accurate classification model. Somewhere between the first error message and the final 96\% accuracy, I
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had managed to absorb a new discipline by pushing myself into unfamiliar waters.
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If it weren't for my ambition, I would have stopped there. Unfortunately, I realized that a working model on my laptop
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wasn't going to help any Parkinson's patients, and I needed to embed my model into a complete hardware device. This
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task was beyond daunting, as I had to venture into the foreign land of hardware and electrical engineering. With my
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engineering teacher guiding me, I slowly learned everything I needed. After dozens of 2 AM KiCAD tutorial binge sessions,
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I finally had a working
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design for a fully custom printed circuit board (PCB). Two weeks later, my PCB arrived, and after soldering all my
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components, it didn't work. My heart sank. I touched up all the joints with my soldering iron and tried again. Success.
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I wrote some quick software in C++, and I finally had a working end-to-end implementation for my final solution. After
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my project made it to the International Science and Engineering Fair (ISEF), the judges were impressed by the full
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end-to-end implementation, and my efforts were finally rewarded when I won 3rd in Robotics and Intelligent Machines at ISEF.
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Throughout my high-school life, I have strived to constantly learn new things, which is why I am so excited about the
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Wright Scholar opportunity. From tinkering in my bed, I can start working on relevant research problems this Summer,
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which would exponentially increase my learning and comprehension of these subjects. From cutting-edge
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biomedical computing to sensor processing to cybersecurity, AFRL offers exciting venues for me to apply my knowledge.
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For a sponge who lives to learn, AFRL can serve as a reservoir of knowledge, and I can't wait to absorb new information with
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domain experts.
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I could have stopped there, but I realized that a working model on my laptop
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wasn't going to help any Parkinson's patients, and I needed to embed my model
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into a complete hardware device. This task was beyond daunting, as I had to
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venture into the foreign territory of hardware and electrical engineering.
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With my engineering teacher guiding me, I eventually learned the basics. After
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countless 2 AM KiCAD tutorial sessions, I finally had a working design for a
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custom printed circuit board (PCB) housing a sensor and microcontroller. Two
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weeks later, my PCB arrived, and after soldering all my components, it didn't
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work. My heart sank. In desperation, I resoldered each joint carefully and
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tried again. Somehow, it worked. After writing some C++ software for the
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device, I had something that actually worked. The project eventually made it
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to the International Science and Engineering Fair (ISEF), placing 3rd in
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Robotics and Intelligent Machines. What struck me most wasn't the placement,
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but the fact that six months earlier, I wouldn't have understood any of it.
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Throughout high school, I've taught myself many disciplines, from FTC
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programming in Java to designing circuits. The Wright Scholar program provides
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an opportunity to apply my skills to current and critical research. I'm
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intrigued by AFRL's Sensors Directorate, where I hope to deepen my understanding
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of signal processing while contributing to sensor exploitation technologies.
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I'm equally drawn to Human Performance Wing's work with multimodal sensing to
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monitor and enhance human performance. Moreso, the chance to work alongside
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domain experts who can accelerate my growth as an engineer and developer is
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invaluable to me. Whether working with sensor fusion or biomedical sensing,
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as a sponge eager to learn, AFRL is exactly where I need to be.
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\end{document}
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