Hyunwoo Oh

I am a data scientist at Lam Research. I use machine learning techniques and AI to reduce the cost of semiconductor manufacturing processes. I received my Ph.D. in physics from University of Maryland. Under the guidance of Paulo Bedaque and Tom Cohen, my research has sat at the intersection of nuclear theory and computational innovation. Before my doctoral studies, I served as a process integration engineer at Samsung Electronics, bringing a background in industrial-scale engineering to my foundational work in physics.

selected publications

  1. Signal-to-noise problems in fault-tolerant quantum computation
    Hyunwoo Oh
    Oct 2026
  2. Classical and Quantum Algorithmic Developments in Lattice Field Theory
    Hyunwoo Oh
    University of Maryland, May 2026
  3. Training neural control variates using correlated configurations
    Hyunwoo Oh
    Phys. Rev. D, Oct 2025
  4. Corrections to adiabatic behavior for long paths
    Thomas D. Cohen, and Hyunwoo Oh
    Phys. Rev. A, Dec 2024
  5. Leveraging neural control variates for enhanced precision in lattice field theory
    Paulo F. Bedaque, and Hyunwoo Oh
    Phys. Rev. D, May 2024
  6. Efficient vacuum-state preparation for quantum simulation of strongly interacting local quantum field theories
    Thomas D. Cohen, and Hyunwoo Oh
    Phys. Rev. A, Feb 2024
  7. Infinite variance problem in fermion models
    Andrei Alexandru, Paulo F. Bedaque, Andrea Carosso, and Hyunwoo Oh
    Phys. Rev. D, May 2023
  8. Lattice scalar field theory at complex coupling
    Scott Lawrence, Hyunwoo Oh, and Yukari Yamauchi
    Phys. Rev. D, Dec 2022