During my service in the Republic of Korea Air Force as an interpretation officer, I had time to reflect deeply on my career path. I decided to build expertise at the intersection of computer science and finance, combining analytical rigor with work that can make a tangible difference.
I then joined a university lab focused on finance and AI, where I have taken on projects closely connected to that goal. At Think Pool, I developed AI-driven factors for finance. With PFCT, I worked on a default-prediction model using tabular transformers. At the Agency for Defense Development, I contributed to interpretable-model research. Together, these experiences have strengthened my interest in building reliable, effective, and understandable AI for financial and tabular data.