Statistics and Data Science
Developing and applying statistical inference, decision theory & optimisation, machine learning and visualisation techniques that will process data and draw new insights and knowledge from the data.
Bayesian Inference
- Time-series analysis, streaming data
- Robust statistics, inverse regression
- Scalable algorithms (Big Bayes)
Decision Theory & Optimisation
- Adaptive utility, sequential decision-making
- Lightweight and private optimisation
Machine Learning
- Linear & Logistic Regression
- Support Vector Machines & Kernel Methods
- k-Means Clustering and Mixture Models for Unsupervised Learning
- Neural Networks
- Deep Learning
Data Visualisation
- Visual Information – perception and understanding
- Graph, Spatial and Interactive data visualisation
Sample Application Areas
- Ecology, astronomy
- Vision/video
- Social networks
- Health
- Fintech
- Education
Faculty Members
Click on the staff member's name to view their full profile and publications.
Staff Name Research Group & Centres | Research Interests | Publications |
---|---|---|
Benavoli, Alessio | Bayesian statistics, probabilistic machine learning | |
Brophy, Caroline | Statistical modelling of non-standard situations, such as ecology, biodiversity, agronomy | |
D'Angelo, Silvia | Statistical Network Analysis, Applied Statistics, Latent Variable Models | |
Fritz, Cornelius |
Analysis of Dependent Data, Models for Large Networks, Computational Statistics, Computational Social Science |
|
Georgiadis, Athanasios |
Nonparametric statistics, Spatial statistics, Bayesian statistics, Mathematics. |
|
Howard, Emma | Statistics education, clustering analysis, applied statistics, and learning analytics. | |
Ng, Tin Lok James | Network Analysis, Mixture Model, Bayesian statistics, Spatial Statistics | |
Computational statistics, applied statistics, model-based clustering, pharmacoeconomics. | ||
Wilson, Simon INSIGHT, ADAPT |
Bayesian statistics, statistical reliability, interface of information and communications systems and statistical learning, computationally intensive statistics | |
Latent Gaussian models, Model-based clustering, Bayesian methods, Bayesian model determination, block modelling, changepoint models, application-based model development | ||
Zhang, Mimi | Stochastic Modelling, Markov Decision Process, Multivariate Modelling, Data Mining |