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School of Electronic Engineering and Computer Science

Bayesian Machine Learning for Causal Discovery

Supervisor: Dr Anthony Constantinou

Research group(s): Risk & Information Management

Are you interested in the theory of causality? Do you want to improve the algorithms we use to discover cause-and-effect relationships from data? This project will focus on algorithms that combine machine learning with search techniques, probability, and statistics, to discover causal Bayesian Networks from data. A project in causal discovery can focus on different learning challenges, such as learning from time-series data, missing data, limited data, big data, data with latent confounders, and any other noisy data. For some examples, refer to our research work here: Applicants must demonstrate strong interest in Bayesian inference and have strong mathematical and machine learning skills.