Research activities in the data science area are concerned with the development of machine learning and statistical methods, their theoretical foundations, and applications.
The group's expertise includes the development of scalable machine learning methods, computational aspects of statistical methods, large-scale statistical inference, nonparametric estimation, representation learning, functional data analysis, optimisation for machine learning, and advanced computational methods such as Markov Chain and sequential Monte Carlo for Bayesian inference.
The areas of applications include the design of novel methods for understanding user behaviour, analysis of social data, modelling and inference for information cascades and epidemic processes that arise in social networks and biomedical applications, as well as algorithms for development of next-generation artificial intelligence systems.
The group members are actively involved in teaching on the MSc Data Science degree programme which provides training in data science methods. The programme provides a thorough grounding in theory, much of it at a high mathematical level, as well as gaining practical skills of applied data science, enabling one to apply advanced data science methods to investigate real-world questions.
Moez Draief - Visiting Professor in Practice
Kostas Kalogeropoulos - Associate Professor
- Yirui Liu
- Filippo Pellegrino
- Yiliu Wang
- Tianlin Xu
- Jialin Yi
- Kaifang Zhou