Study level

  • Vacation research experience scheme


Topic status

We're looking for students to study this topic.

Research centre


Associate Professor Helen Thompson
Associate Professor in Statistics
Division / Faculty
Faculty of Science
Dr Gentry White
Associate Professor in Data Science and Government Statistics Chair
Division / Faculty
Faculty of Science

External supervisors

  • Bernadette Giuffrida, ABS


Machine learning can be very powerful but its black box reputation can be an obstacle for industry.

In this project we'll look at creative ways to visually convey how methods like deep neural nets, random forest, XGBoost algoritms, and support vector machines work for different audiences.

Research activities

You can expect to:

  • learn about a range of machine learning methods
  • engage with relevant industry partners about the barriers to understanding
  • get to exercise creativity in your presentation of methods
  • communicate your work in an interactive manner
  • meet regularly with your supervisor(s) to discuss ideas and research direction, as well as to receive feedback.


A report on the available methods and 'state of the art' for 'explainable' machine learning methods, including examples of applications and methods. These results will be used internally by the industry partner for reference and future research proposals and projects.

Skills and experience

To be considered for this project, you'’'ll need an interest in machine learning methods, visualisation and impactful communication. Some experience with these would be beneficial.



Contact the supervisor for more information.