My main research interest lies in the theory and numerical study of quantum many-body physics, particularly exotic phases of matter beyond the traditional Landau-Ginzburg paradigm. I am also focused on applying state-of-the-art large-scale scientific computing techniques to develop and optimise numerical simulation algorithms, including various Monte Carlo methods and machine learning model training and inference. One of my key areas of focus is quantum spin liquids (QSLs), an exciting field marked by rapidly evolving theoretical concepts, experimental discoveries of novel phenomena, and promising applications in quantum computing. In my previous research, I studied Raman spectroscopy, phonon dynamics, and the thermodynamics of Kitaev QSLs, and proposed several experimental observables for their detection. I also applied machine learning models to fit the energy landscape of the Kitaev QSL, leading to the development of a novel machine-learning-aided Monte Carlo algorithm—dubbed stratified Monte Carlo—which significantly reduces autocorrelation errors.