Artificial intelligence and data science

Our research focuses on artificial intelligence and data science. This includes:

  • data mining
  • machine learning
  • deep learning
  • computational intelligence
  • information visualisation
  • biomedical informatics.

We are solving computational and data analysis problems of dynamic and significant importance to:

  • digital health
  • multimedia systems
  • bioinformatics
  • intelligent machines
  • digital forensics
  • transport systems
  • big data analysis
  • industry-based projects.

We also run a Graphics Processing Unit (GPU) Cluster Lab.

Our group members have been awarded and/or participated in many competitive and industry-based grants in the past few years. This includes:

  • ARC Discovery Projects
  • ARC Linkage Projects
  • ARC Centre of Excellence in Bioinformatics
  • projects in Dairy Futures CRC
  • Auto CRC.

We collaborate with external partners, including:

  • Department of Transport
  • Department of Economic Development, Jobs, Transport and Resources,
  • Department of Primary Industries
  • IBM
  • CSIRO/Data 61.

Our group members have held important roles nationally and internationally. Examples include being appointed as Editor-in-Chief, sitting on editorial boards and being guest editors for high-quality journals including:

  • IEEE Transactions on Multimedia
  • IEEE Transactions on Neural Networks and Learning Systems
  • IEEE Transactions on Cybernetics
  • IEEE/ACM Transactions on Computational Biology and Bioinformatics
  • Journal of Visualisation Language and Computing.

We've also been conference chairs and committee members for top conferences.

Group members

Research projects

Advanced analytics utilising conjoint mining of data and content with applications in business, bio-medicine and electrical power systems

This project provides techniques that enable effective analysis of unstructured content and related information from relational databases in a conjoint manner. These techniques will be applied in the business, bio-medicine and electrical power systems domains.

Deep learning in non-coding RNA classification in bioinformatics

Non-coding (nc) RNA plays a vital role in biological processes and has been associated with diseases such as cancer. Classification of ncRNAs is necessary for understanding the underlying mechanisms of the diseases and to design effective treatments.

Machine learning for cancer genomics

The aim of this research proposal is to improve the cancer detection and prediction so the patients may not go through the unhealthy and costly medical procedures and to improve the computational complexity of existing machine learning algorithms.

Privacy and security of genomic data and functionalities

In this project, we will provide the current trends and insights on the importance and challenges of privacy and security issues in the area of genomics.

Congestion management in key road networks of a major city through real time data collection, intelligent forecasting and real time routing

The project researches the issues for allowing Australian Road Traffic Authorities to automatically capture road traffic data, forecast traffic flows and smartly route traffic flows to avoid congestion on road networks.

Use of deep learning for analysis of video in sport

In this project, we are working with the Australian Institute of Sport (AIS) on action recognition and multi-person tracking in several different sports.

Deep mining neurological abnormalities from brain signal data

This project aims to develop a reliable, robust and real-time analysis system for automatic and accurate detection of neurological abnormalities, and the prediction of impending neurological problems from brain signal data.

Increasing data quality with group associations in outsourcing environment

This project aims to discover how tuples (data structures) in fragments can be grouped to increase the utility of queries executed over fragments.

Automatic annotation of semantic face features

Automatic annotation of local face features is a key step in design of face image retrieval systems. Data mining techniques such as classification and clustering will be used to achieve the goal. Benchmark data sets including face images with variations will be employed in this study. Evaluation metrics for system performance assessment are needed.