My main research interest lies in the theory and numeric of quantum many-body physics, especially the exotic phases of matter beyond the traditional Landau-Ginzburg paradigm, as well as applying SOTA large-scale scientific computing techniques to develop and optimize numerical simulation algorithms, including various Monte Carlo simulations and machine learning model training and inference. One of my focuses is quantum spin liquids (QSL), which is an exciting research area, with rapidly evolving fundamental concepts, experimental discoveries of novel phenomenon, and promising applications in quantum computing. In my previous research, I studied the Raman spectroscopy, phonon dynamics and thermodynamics of Kitaev QSL, and propose several experimental observables for QSL detection. I also applied machine learning model to fit the energy landscape of Kitaev quantum spin liquid, based on which a novel machine-learning-aided Monte Carlo algorithm is developed, dubbed stratified Monte Carlo, which significantly reduces the autocorrelation error.