About Me

By the age of 13, I left the comfort of my family and moved to a new country to grow both as a person and as a tennis player. Learning a new language, adapting to a different culture, and managing life on my own—down to the smallest things like figuring out how to do laundry—forced me to grow up quickly. Balancing 20+ hours a week of training, traveling for tournaments, and keeping up with school wasn't easy, but it taught me resilience, discipline, and how to thrive under pressure. Today, I'm a D1 tennis student-athlete on scholarship, pursuing my Computer Science degree with a Mathematics minor and Big Data Analytics concentration.

This early independence gave me a strong sense of purpose, especially for someone my age. The experience instilled in me a confidence in my own abilities, a deep appreciation for others, and a natural ability to empathize with them. My parents are both judges, so dinner conversations often revolved around complex cases and the human stories behind them. Those experiences sparked my fascination with uncovering patterns others might overlook and finding solutions that truly matter. That's what drew me to data science—the chance to use AI and analytics to address social justice challenges and help people in meaningful ways.

I love long hours and high stress—this is where I thrive. Give me an impossible job, a laptop, and an energy drink, and I'm where I do my best work. Whether it's a long night coding, an intense hackathon, or fighting my way through a tough tennis match, challenges don't drain me—they give me purpose. I like pushing myself and staying busy, always searching for ways to grow and contribute.

What excites me most about data science isn't just uncovering answers—it's discovering the questions no one thought to ask. Looking ahead, I'm especially drawn to studying how bias shows up in human judgment, shaping outcomes in law, opportunity, and everyday decision-making. Since current models often mirror human tendencies, the challenge lies not only in developing the algorithms themselves but also in the very data we use to train them. By applying supervised learning and natural language processing, I aim to classify and analyze these biases, making them visible in ways that support greater fairness and transparency. This vision drives my plan to pursue a Master's in Data Science, where I can deepen my expertise and continue building technologies that serve people with integrity and purpose by shedding light on patterns of injustice.