About
I am a physics Ph.D. candidate at Cornell University, advised by Prof. Eun-Ah Kim. My research sits at the interface of quantum information, many-body dynamics, and machine learning.
I design learning architectures around the physical structure of a problem. In my work, attention acts across measurement snapshots and monitored trajectories, sparse computation follows the geometry of quantum error-correction circuits, and reinforcement-learning policies operate within the motion constraints of reconfigurable atom arrays. The recurring goal is to extract useful quantum information without requiring complete state reconstruction or exact classical optimization.
My collaborations have connected theory and machine learning with experimental quantum platforms, including work with Google Quantum AI, MIT, and Quantinuum. Looking ahead, I am interested in measurement-native discovery, adaptive and device-aware quantum error correction, and hardware-software co-design.
Education
Cornell University, Ithaca, NY
Ph.D. candidate in Physics, expected August 2027
M.S. in Physics, 2025
Advisor: Prof. Eun-Ah Kim
Gwangju Institute of Science and Technology, Gwangju, South Korea
B.S. in Physics, minor in Mathematics, 2022