My journey into computer science began with the thrill of translating ideas into code — the satisfaction of turning abstract logic into something that works. This passion naturally drew me toward AI, where algorithms don’t just solve problems, but begin to mirror the very processes of the human brain. To explore this, I pursued a B.Sc. in Data Science at Korea University, where I developed the analytical and technical foundations for understanding complex systems — both biological and artificial.

My research centers on bridging human and machine vision. I am particularly interested in how human and machine vision diverge, and how incorporating biological factors and structures can make artificial vision more human-like. My long-term goal is to build computer vision systems that do not merely process images, but genuinely “see” and behave the way humans do. Currently, I am working on research related to topographic neural networks and foveated and peripheral vision — drawing inspiration from the brain’s own visual architecture to inform better AI models. Through this work, I hope to contribute to a future where the boundary between human and artificial vision becomes meaningfully closer.