Science and Engineering

Data science



We focus on computational methods for the analysis and understanding of the relationships embedded within large-scale data sets.

Our research is grounded in theory and algorithm development. It ranges from machine learning to visualisation, with emphasis on:

  • information retrieval
  • data mining
  • web intelligence
  • bioinformatics.

We are experts in parallel computing, and run a dedicated HPC laboratory for realist experiments.

Real world applications

The application of our research in the real world includes:

  • environmental monitoring
  • XML-based search
  • spatial information
  • personalisation of search results
  • understanding the evolutionary and regulatory relationships across bacterial genomes.

We contribute to the development of open software projects that support our work, and maintain active collaborations with industry and university partners in Australia, Europe, India, China and the United States.


Academics in our discipline teach undergraduate degrees in engineering and information technology, and in the emerging computational science program.

A number of our staff have been recognised for excellence in undergraduate teaching and in postgraduate supervision in Queensland and nationally.


The Category 1 funded research projects we are currently leading are:

Visual analytics for next generation sequencing

Project leader
Associate Professor Jim Hogan
Project summary

Next-generation sequencing technologies have brought a revolution in biology and healthcare, while taxing the ability of scientists and clinicians to identify and process relevant data, to make sense of it all and communicate it to others in a concise and meaningful way.

This project aims to tackle this problem through fundamentally new approaches to data selection and visualisation at very large scale, actively encoding for insight into underlying biological and biomedical processes, bringing sustainable discovery of new relationships and variations within the data.

The project aims to support new approaches to medical diagnosis and treatment, and offer crucial lessons to address the broader challenge of understanding large, complex data sets.

Securing real-time control for critical infrastructure systems

Project leader
Professor Glen Tian
Project summary

This project investigates the challenging problem of securing real-time control of large-scale power systems.

Both industrial control systems and electricity power systems are a critical part of national infrastructures of industrial countries like Australia and Germany. The power systems are operated under real-time monitoring and control for system stability and efficiency, while the real-time control of power systems rely on 'domain separation' for the security and integrity of measurement and control data and data transmissions. Any 'air gaps' between the domains can be easily breached for a cyber attack, causing serious security threats worldwide in critical infrastructure systems including power networks.

The current practice is to monitor the integrity of the security domains through manual audits. However, manual checks are far slow to react to cyber attacks. System inspectors and maintainers may also cause unexpected and insecure incidents by accident.

Learning specific ontology for un-supervised text classification

Project leader
Professor Yuefeng Li
Project summary

The dramatic rise in massive text data has led to an increasing number of challenges in scalability and noisy information. Supervised classification has become expensive and time consuming as acquiring training sets for a large number of categories becomes more complex and classifiers are sensitive to data. Un-supervised classification has become an attractive alternative given it does not require training sets. However, un-supervised classification is still complex and there is a gap between understanding of concepts and features.

This project aims to exploit domain ontology to find specific ontology which can bridge the gap, leading to a breakthrough for un-supervised classification. It provides foundations for classifying big text data.

Interdisciplinary and inter-institution projects

Some of the projects we are contributing to with other disciplines and institutions are:

  • An integrated mathematical approach to synchronise and optimise hospital operations, 2014-2017
  • Human cues for robot navigation, 2014-2016.


School of Electrical Engineering and Computer Science

  • Level 12, S Block, Room 1221
    Gardens Point

  • Postal address:
    School of Electrical Engineering and Computer Science
    GPO Box 2434
    Brisbane QLD 4001