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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

Faculty Members

Staff Name Research Group & Centres Research Interests Link to Partial List of Publications
Keaney, Aideen
User perceptions of social networking sites. Trust and privacy as they relate to technology. Online identity theft
Nipoti, Bernardo
Bayesian nonparametric statistics, Markov chain Monte Carlo methods, survival analysis, species sampling
O'Regan, Myra
 
White, Arthur
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
Wyse, Jason
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

Associated Research Centres