Recalibrated by Maria

Recalibrated by Maria - July 2026 Scholarship Essay

When I transferred to the University of Southern California as a Computer Science major, I believed success meant understanding concepts quickly. I had always enjoyed mathematics and technology, so I assumed that if I worked hard enough everything would eventually make sense. Instead I found myself struggling more than I ever had academically. Programming assignments that seemed manageable to others often took me days or weeks to complete, and I constantly questioned whether I belonged in one of USC's most rigorous programs.

There were weeks when I spent nearly sixteen hours at a time in the study rooms at Leavey Library, leaving only to grab something to eat or take a twenty-minute power nap before returning to my laptop. More than once, I walked out of the library at six o'clock in the morning as the sun was coming up, exhausted and frustrated because I still hadn't solved the problem I had spent the entire night working on. Those mornings were not moments I was proud of. They were moments when I genuinely wondered if I was capable of succeeding in computer science.

The hardest part wasn't the workload. It was the mindset I had developed. Every time my code failed or I couldn't solve a problem, I saw it as proof that I wasn't smart enough. I measured success by how quickly I could arrive at the correct answer instead of by how much I was learning throughout the process.

Over time computer science forced me to rethink that perspective. I realized that programming wasn't about writing perfect code on the first attempt. It was about breaking complex problems into smaller pieces, questioning my assumptions, testing different approaches, and learning from every mistake. A single misplaced line of code could produce dozens of errors, and the only way forward was to slow down, analyze my reasoning, and methodically work through each possibility. Debugging became less about fixing code and more about understanding how I thought.

Although I ultimately decided to switch my major to Economics and Data Science, that decision wasn't an ending. At first changing majors felt like admitting defeat. Looking back, it became the decision that helped me rediscover why I wanted to study technology in the first place and really help me recalibrate. I realized I didn't love programming simply for the sake of programming. I loved using computation, mathematics, and data to solve meaningful problems.

That realization became even clearer through my Artificial Intelligence specialization. Learning about machine learning, neural networks, and computer vision completely changed the way I viewed technology. Instead of asking whether I could simply build a model, I became fascinated by understanding why it worked, how it learned from data, and how it could be applied to solve meaningful problems. During one project, I built a neural network to recognize handwritten Chinese characters. Success did not come from training the model once; it came from analyzing incorrect predictions, questioning my process, refining the data, and improving the model through careful experimentation. The critical thinking skills I had struggled to develop in my early programming courses had gradually become second nature.

Today, with the encouragement of my professors, I am preparing to pursue a master's degree in Applied Data Science, where I hope to continue exploring artificial intelligence and data science, particularly their applications in healthcare. Looking back, I no longer see my time as a Computer Science major as a period of failure. Instead I see it as the experience that fundamentally changed how I learn. It taught me that critical thinking is not about finding the right answer immediately, it is about asking questions and remaining curious when the answer is not obvious. Those lessons continue to shape every problem I approach and have given me the confidence to pursue research at the intersection of AI and healthcare.