One of my favorite things is the process of answering a difficult question or solving a challenging problem. Sometimes it involves methodical trial and error as I experiment with different approaches until I find the right tool to swiftly crack open the question to reveal the answer. Other times, I steadily piece together a solution over the course of hours, days, weeks, or even months of deep thought and consistent incremental work. And then there are the rare times where an answer just pops into my head, seemingly out of nowhere. Regardless of how I solve a problem, going through the process and continually learning new things en route to the ultimate solution is extremely rewarding.I first fell in love with the problem-solving process in the context of studying math at UCLA as I worked to understand highly abstract structures and prove intricate theorems, both on my own and with my fellow math majors. In March 2016, while I was continuing my pure math education as a graduate student at USC, I watched in awe when Google's AlphaGo program dominated Lee Sedol, one of the world's top Go players, with a stunning 4-1 victory in a five-game series that most people had expected to be a sweep in favor of Lee. Even though I didn't immediately act on it at the time, that week of late nights watching the power of cutting-edge deep neural networks tackle one of the world's most complex games sparked my interest in a new realm of problem-solving: data science and machine learning. Now, I am working to apply my blend of rigorous mathematical training, passion for problem-solving, and sharp communication skills toward commercial roles where I can use data to help answer difficult questions and solve challenging problems. When I am not thinking about math or data, I enjoy learning about Japanese music and culture. I also love running distances from 5K up through marathons and beyond on both roads and trails, as well as volunteering at trail running races in the San Gabriel Mountains.
