Kexin Feng is a researcher specialising in the theoretical and numerical study of quantum many-body physics, with particular emphasis on exotic phases of matter beyond the conventional Landau–Ginzburg paradigm. His work integrates state-of-the-art large-scale scientific computing to develop and optimise simulation algorithms, including a variety of Monte Carlo methods and machine learning–based training and inference techniques. A key focus of Feng’s research is quantum spin liquids (QSLs), a rapidly evolving field featuring novel theoretical constructs, emerging experimental phenomena, and promising prospects for quantum computing applications. In previous projects, he investigated Raman spectroscopy, phonon dynamics, and the thermodynamic properties of Kitaev QSLs, proposing several experimental observables for their characterisation. Furthermore, he applied machine learning models to fit the energy landscape of the Kitaev QSL, leading to the development of a novel machine-learning-assisted Monte Carlo algorithm—termed stratified Monte Carlo—that significantly reduces autocorrelation errors.