Ph.D. student at KAIST
I study programming languages and compilers to make quantum algorithms easier to express and implement. My recent work focuses on quadratic speedups; I am also interested in high-level approaches to superquadratic speedups and QEC-aware compilation for the FTQC era.
I am part of the Programming Language Research Group, advised by Sukyoung Ryu and co-advised by Jeehoon Kang.
We designed QPPL to make quantum Monte Carlo estimation easier to use through probabilistic programming. The compiler generates the required quantum circuits automatically, so users do not need to manage quantum details.
We developed QPMC to make high-accuracy verification of large probabilistic systems more scalable. It avoids full state enumeration and uses quantum estimation for quadratically better scaling with accuracy.
We designed Memento to make crash recovery easier to build into concurrent data structures in persistent memory. Reusable components provide recovery guarantees without a custom recovery algorithm for each structure.
KAIST · Ph.D. student in AI Semiconductor
Advisor: Sukyoung Ryu. Co-advisor: Jeehoon Kang.
KAIST · M.S. in Computer Science
Advisor: Jeehoon Kang. Thesis: Quantum Probabilistic Model Checking.
Chungnam National University · B.S. in Computer Science and Engineering