Science and Engineering

Data science



We develop and apply data mining, machine learning and artificial intelligence methods to analyse and understand the relationships embedded within large-scale and complex data exhibiting heterogeneity and semantic/structural diversity.

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

  • machine learning
  • data mining
  • text mining
  • web intelligence
  • bioinformatics
  • sensor and Internet of Things (IoT) analytics.

We are experts in parallel computing, and run a dedicate High Performance Computing (HPC) laboratory for realistic experiments.

Real world applications

We are actively engaged in trans-disciplinary research in areas including:

  • enterprise data analytics such as Education, Transport and Main Roads.
  • smart services and social media mining for example detecting hierarchical communities in social networks, finding people perception on senior living.
  • agriculture applications such as emerging trend prediction in Agriculture, building sustainability
  • environmental monitoring
  • bacterial genomes analytics.

We maintain active collaborations with industry and university partners in Australia, Europe, India, China and the United States.

We contribute to the development of open software projects that support our work.

Featured research

Our researchers collaborate on projects in specialised research groups and facilities across disciplines and institutions:


We teach undergraduate and postgraduate degrees in information technology and engineering, and in the emerging data analytics program. We are engaged in teaching across a range of topics including Data Science, Networking and Computer Science specializations.


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

Searching when the stakes are high: better health decisions from search engines

Project leader
Dr Guido Zuccon
Project summary

This project aims to help people make better health decisions from search engines by improving the information that search queries return. Google is utilised by 80 per cent of Australians to search health symptoms, despite evidence showing that many often find incorrect and unreliable health information.

Expected outcomes include new models and methods for evaluating high-stakes search and new search technologies to help people find and recognise high quality information to make better health decisions.

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.

Student topics

Are you looking to further your career by pursuing study at a higher and more detailed level? We are currently looking for students to research with us. Contact our staff to find out more about research opportunities, or take a look at our student topics.


School of Electrical Engineering and Computer Science

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