Edwin Fong is currently an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Hong Kong. He previously worked as a data scientist at Novo Nordisk, where he developed innovative statistical methods for healthcare applications. Edwin completed his PhD at the University of Oxford and the Alan Turing Institute, under the supervision of Professor Chris Holmes. His current research focuses on Bayesian methods driven by prediction, covering topics such as Bayesian nonparametrics, scalable inference, and model selection. He is particularly interested in generalized Bayesian procedures to address challenges like scalability and model misspecification, including methods like general Bayesian updating and the Bayesian bootstrap. Additionally, his work intersects with machine learning, conformal prediction, and recently, causal inference in clinical trials, exploring its connections with Bayesian techniques.