Systems Analytics Research Institute


Extracting exploitable knowledge from huge amounts of interconnected data

System modelling via data analysis, simulation and physical models

Semantic models and probabilistic inference to integrate multiple sources of information

Innovative methods of presenting uncertain information to end users

Empowering non-statistically trained users to understand and control their domain

Research methods

  • Machine learning and pattern analysis

  • Data visualisation/visual analytics

  • Statistical physics of complex systems

  • Natural-language processing

  • Visual information processing

  • Nonlinear and stochastic differential systems

  • Computational intelligence

  • Semantic web and cyber-physical social systems

  • Software engineering

  • Self-adaptive and autonomous systems

  • Cognitive science

Key applications

  • Prediction, classification, and clustering of data.  Non-linear models and Bayesian methods.  Advanced inference, time series analysis/forecasting

  • Projection of high-dimensional data for visual interpretation

  • Routing, network analysis, emergent behaviour in nonlinear and evolving systems, optimisation and scheduling

  • Text data (documents and social networks); topic and sentiment analysis

  • Analysis of image and video data; information retrieval

  • Physical and biological system modelling, fluid dynamics, econophysics

  • Agent-based system modelling based on micro-level modelling

  • Semantic modelling of software, hardware and physical systems and their interaction with human society.

  • Reliable software engineering, large-scale and non-SQL databases, parallel and cluster computing.
  • Software systems that react to data to improve their performance

  • Human factors in data analysis and knowledge interpretation; decision-support systems


Ian Nabney, Director of SARI