Paul Yang, Ph.D.

Yubo “Paul” Yang joined the Simons Foundation in 2021 as a Flatiron Research Fellow at the Center for Computational Quantum Physics (CCQ). In 2024, he began his faculty appointment as an Assistant Professor in the Department of Physics and Astronomy at Hofstra University. His research focuses on the computational modeling of quantum materials, with an emphasis on strongly correlated electron systems and the integration of machine learning into electronic structure methods.

During his time at CCQ, Paul developed correlated electronic structure methods to study strong-interaction physics in two-dimensional moiré materials, including generalized Wigner crystal phases. In collaboration with CCQ scientists Miguel Morales and Shiwei Zhang, he adapted and extended the capabilities of the open-source software packages Quantum ESPRESSO and QMCPACK to accurately model 2D moiré continuum Hamiltonians in monolayer and bilayer geometries. His work led to high-precision quantum Monte Carlo (QMC) simulations that revealed rich electronic phase behavior in these systems [1–3]. In addition to his work on moiré systems, Paul contributes to the ongoing development of machine learning-assisted QMC algorithms, including neural network-based wavefunction ansätze for strongly interacting systems [4]. He also works on the development of machine-learned interatomic potentials, especially for the accurate modeling of high-pressure hydrogen [5]. His work continues to integrate many-body physics, high-performance computing, and machine learning to address open problems in condensed matter and materials science.