Ruizi Yan grew up in Luzhou and began her undergraduate study in Computing Mathematics at City University of Hong Kong before transferring to the University of Edinburgh to pursue a joint bachelor’s degree in Mathematics and Statistics. She later completed her MSc in Statistical Science at St Peter’s College, Oxford. During her undergraduate studies, Ruizi focused on sequential experimental designs, Gaussian processes and nonparametric Bayesian models particularly Dirichlet Processes and Polya Trees which formed the basis of her dissertation.
Ruizi has gained practical experience through internships in data engineering and machine learning, applying models to real-world problems such as document analysis and financial risk assessment.
During her master’s studies, she developed an interest in graphical models and causal inference. At StatML, Ruizi aims to develop statistical methods for simulating a wide range of causal models, while remaining open to exploring new areas.
Outside of research, Ruizi enjoys sports, traveling, and is passionate about greenery and gardening.
