Change. Out of necessity? When my father was badly injured, I frantically looked for ways that I, a humble high schooler, could endeavor to help him using my programming knowledge. Out of inspiration? When my swim teammates desperately searched for ways to improve, I was inspired to begin building a machine-learning-based pose-estimation program to deliver feedback based on video footage of our strokes. Out of sheer curiosity? I heard a rumor that my favorite video game company would begin hiring researchers to look into AI voice emulation for its characters. I had no experience in the natural language processing component of machine learning, and yet, I was curious. I envision a future where computer science drives change, where I am capable of joining—no, leading—this change. But as a burgeoning computer scientist, I have often had to sit on the sidelines of change, having been denied opportunities due to my age. That’s why I have learned to take responsibility to pursue my own endeavors. I learned how to build Apple apps using their native language, building neurorehabilitation apps in a desperate attempt to awaken my father from the fog that obscured his mind. Taking into account his nearly immobile arm’s range of motion while creating the user interface, I tinkered. Calibrating to his precise mathematical skill level to create problem sets, I coded. And incorporating aspects from other games that he seemed to like, I created. I scoured examples of rehabilitation apps, read one research paper on stroke patient rehabilitation after another, and frantically emailed professors from around the world requesting information on case studies. I walked the bridge between computer science and neuroscience. That’s why I self-studied machine learning through online resources, reaching out to Derrick, a graduate student at the nearby San Jose State University who became my mentor as I researched exercise recognition through pose-estimation models. Seeing Derrick and the other masters students demonstrate such passion for furthering the realm of machine learning pushed me to learn faster in hopes of catching up. Finally, after learning everything I needed to know about convolutional neural networks, pose-estimation algorithms, and deep-learning models, I committed hundreds of hours to creating a never-before-seen program that recognized and gave feedback on our swimming strokes. And in the process, I discovered my love for computer-vision research. That's why I dedicated myself to the elegant scientific works of computer science. The thousands of hours I have spent debugging code, seeking answers that don’t yet exist, and learning new programming languages to understand documentations were ultimately about becoming a person capable of bringing real change to the world—out of necessity, inspiration, and curiosity.

It's immensely frustrating to me. No matter how you spin it, there’s no single best combination of words that I can put together to present the “best” written essay possible. But unlike the English language, with machine learning, there is a certain technical mathematical level of perfection that can be achieved. The more hours I spent swimming in the depths of the fundamental calculus theorems powering machine learning, the more I came to this realization: even though artificial intelligence is meant to mimic the thought process of our human brains, the math behind such calculations force a limit of sorts. And as a perfectionist, my sheer curiosity drives me to search for these limits. For the first time in my life, computer science felt like an actual science, instead of some game of who can memorize the most syntax. Every algorithm, down to the bare fundamentals, was intuitively calculus-driven. Even the most complex of models revolved around the same fundamental math concepts as a simple one does. This consistency, accuracy, and ingenuity are all aspects that blend to create this work of mathematical art. And I for one, want to personally see just how far math can go to mimic our humanity.

Change. Out of necessity? When my father was badly injured, I frantically looked for ways that I, a humble high schooler, could endeavor to help him using my programming knowledge. Out of inspiration? When my swim teammates desperately searched for ways to improve, I was inspired to begin building a machine-learning-based pose-estimation program to deliver feedback based on video footage of our strokes. Out of sheer curiosity? I heard a rumor that my favorite video game company would begin hiring researchers to look into AI voice emulation for its characters. I had no experience in the natural language processing component of machine learning, and yet, I was curious. I envision a future where computer science drives change, where I am capable of joining—no, leading—this change. But as a burgeoning computer scientist, I have often had to sit on the sidelines of change, having been denied opportunities due to my age. That’s why I have learned to take responsibility to pursue my own endeavors. I learned how to build Apple apps using their native language, building neurorehabilitation apps in a desperate attempt to awaken my father from the fog that obscured his mind. Taking into account his nearly immobile arm’s range of motion while creating the user interface, I tinkered. Calibrating to his precise mathematical skill level to create problem sets, I coded. And incorporating aspects from other games that he seemed to like, I created. I scoured examples of rehabilitation apps, read one research paper on stroke patient rehabilitation after another, and frantically emailed professors from around the world requesting information on case studies. I walked the bridge between computer science and neuroscience. That’s why I self-studied machine learning through online resources, reaching out to Derrick, a graduate student at the nearby San Jose State University who became my mentor as I researched exercise recognition through pose-estimation models. Seeing Derrick and the other masters students demonstrate such passion for furthering the realm of machine learning pushed me to learn faster in hopes of catching up. Finally, after learning everything I needed to know about convolutional neural networks, pose-estimation algorithms, and deep-learning models, I committed hundreds of hours to creating a never-before-seen program that recognized and gave feedback on our swimming strokes. And in the process, I discovered my love for computer-vision research. That's why I dedicated myself to the elegant scientific works of computer science. The thousands of hours I have spent debugging code, seeking answers that don’t yet exist, and learning new programming languages to understand documentations were ultimately about becoming a person capable of bringing real change to the world—out of necessity, inspiration, and curiosity.

Change. Out of necessity? When my father was badly injured, I frantically looked for ways that I, a humble high schooler, could endeavor to help him using my programming knowledge. Out of inspiration? When my swim teammates desperately searched for ways to improve, I was inspired to begin building a machine-learning-based pose-estimation program to deliver feedback based on video footage of our strokes. Out of sheer curiosity? I heard a rumor that my favorite video game company would begin hiring researchers to look into AI voice emulation for its characters. I had no experience in the natural language processing component of machine learning, and yet, I was curious. I envision a future where computer science drives change, where I am capable of joining—no, leading—this change. But as a burgeoning computer scientist, I have often had to sit on the sidelines of change, having been denied opportunities due to my age. That’s why I have learned to take responsibility to pursue my own endeavors. I learned how to build Apple apps using their native language, building neurorehabilitation apps in a desperate attempt to awaken my father from the fog that obscured his mind. Taking into account his nearly immobile arm’s range of motion while creating the user interface, I tinkered. Calibrating to his precise mathematical skill level to create problem sets, I coded. And incorporating aspects from other games that he seemed to like, I created. I scoured examples of rehabilitation apps, read one research paper on stroke patient rehabilitation after another, and frantically emailed professors from around the world requesting information on case studies. I walked the bridge between computer science and neuroscience. That’s why I self-studied machine learning through online resources, reaching out to Derrick, a graduate student at the nearby San Jose State University who became my mentor as I researched exercise recognition through pose-estimation models. Seeing Derrick and the other masters students demonstrate such passion for furthering the realm of machine learning pushed me to learn faster in hopes of catching up. Finally, after learning everything I needed to know about convolutional neural networks, pose-estimation algorithms, and deep-learning models, I committed hundreds of hours to creating a never-before-seen program that recognized and gave feedback on our swimming strokes. And in the process, I discovered my love for computer-vision research. That's why I dedicated myself to the elegant scientific works of computer science. The thousands of hours I have spent debugging code, seeking answers that don’t yet exist, and learning new programming languages to understand documentations were ultimately about becoming a person capable of bringing real change to the world—out of necessity, inspiration, and curiosity.

Change. Out of necessity? When my father was badly injured, I frantically looked for ways that I, a humble high schooler, could endeavor to help him using my programming knowledge. Out of inspiration? When my swim teammates desperately searched for ways to improve, I was inspired to begin building a machine-learning-based pose-estimation program to deliver feedback based on video footage of our strokes. Out of sheer curiosity? I heard a rumor that my favorite video game company would begin hiring researchers to look into AI voice emulation for its characters. I had no experience in the natural language processing component of machine learning, and yet, I was curious. I envision a future where computer science drives change, where I am capable of joining—no, leading—this change. But as a burgeoning computer scientist, I have often had to sit on the sidelines of change, having been denied opportunities due to my age. That’s why I have learned to take responsibility to pursue my own endeavors. I learned how to build Apple apps using their native language, building neurorehabilitation apps in a desperate attempt to awaken my father from the fog that obscured his mind. Taking into account his nearly immobile arm’s range of motion while creating the user interface, I tinkered. Calibrating to his precise mathematical skill level to create problem sets, I coded. And incorporating aspects from other games that he seemed to like, I created. I scoured examples of rehabilitation apps, read one research paper on stroke patient rehabilitation after another, and frantically emailed professors from around the world requesting information on case studies. I walked the bridge between computer science and neuroscience. That’s why I self-studied machine learning through online resources, reaching out to Derrick, a graduate student at the nearby San Jose State University who became my mentor as I researched exercise recognition through pose-estimation models. Seeing Derrick and the other masters students demonstrate such passion for furthering the realm of machine learning pushed me to learn faster in hopes of catching up. Finally, after learning everything I needed to know about convolutional neural networks, pose-estimation algorithms, and deep-learning models, I committed hundreds of hours to creating a never-before-seen program that recognized and gave feedback on our swimming strokes. And in the process, I discovered my love for computer-vision research. That's why I am driven to pursue the depths of computer science. The thousands of hours I have spent debugging code, seeking answers that don’t yet exist, and learning new programming languages to understand documentations were ultimately about becoming a person capable of bringing real change to the world—out of necessity, inspiration, and curiosity.

Change. Out of necessity? When my father was badly injured, I frantically looked for ways that I, a humble high schooler, could endeavor to help him using my programming knowledge. Out of inspiration? When my swim teammates desperately searched for ways to improve, I was inspired to begin building a machine-learning-based pose-estimation program to deliver feedback based on video footage of our strokes. Out of sheer curiosity? I heard a rumor that my favorite video game company would begin hiring researchers to look into AI voice emulation for its characters. I had no experience in the natural language processing component of machine learning, and yet, I was curious. I envision a future where computer science drives change, where I am capable of joining—no, leading—this change. But as a burgeoning computer scientist, I have often had to sit on the sidelines of change, having been denied opportunities due to my age. That’s why I have learned to take responsibility to pursue my own endeavors. I learned how to build Apple apps using their native language, building neurorehabilitation apps in a desperate attempt to awaken my father from the fog that obscured his mind. Taking into account his nearly immobile arm’s range of motion while creating the user interface, I tinkered. Calibrating to his precise mathematical skill level to create problem sets, I coded. And incorporating aspects from other games that he seemed to like, I created. I scoured examples of rehabilitation apps, read one research paper on stroke patient rehabilitation after another, and frantically emailed professors from around the world requesting information on case studies. I walked the bridge between computer science and neuroscience. That’s why I self-studied machine learning through online resources, reaching out to Derrick, a graduate student at the nearby San Jose State University who became my mentor as I researched exercise recognition through pose-estimation models. Seeing Derrick and the other masters students demonstrate such passion for furthering the realm of machine learning pushed me to learn faster in hopes of catching up. Finally, after learning everything I needed to know about convolutional neural networks, pose-estimation algorithms, and deep-learning models, I committed hundreds of hours to creating a never-before-seen program that recognized and gave feedback on our swimming strokes. And in the process, I discovered my love for computer-vision research. These experiences drive me to pursue computer science. The thousands of hours I have spent debugging code, seeking answers that don’t yet exist, and learning new programming languages to understand documentations were ultimately about becoming a person capable of bringing real change to the world—out of necessity, inspiration, and curiosity.