QUT offers a diverse range of student topics for Honours, Masters and PhD study. Search to find a topic that interests you or propose your own research topic to a prospective QUT supervisor. You may also ask a prospective supervisor to help you identify or refine a research topic.
Found 5 matching student topics
Displaying 1–5 of 5 results
Building explainable and trustworthy intelligent systems
Existing machine learning-based intelligent systems are autonomous and opaque (often considered “black-box” systems), which has led to the lack of trust in AI adoption and, consequently, the gap between machine and human being.In 2018, the European Parliament adopted the General Data Protection Regulation (GDPR), which introduces a right of explanation for all human individuals to obtain “meaningful explanations of the logic involved” when a decision is made by automated systems. To this end, it is a compliance that an intelligent …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
- Centre for Data Science
Explainable AI-enabled predictive analytics
Modern predictive analytics underpinned by AI-enabled learning (such as machine learning, deep learning) techniques has become a key enabler to the automation of data-driven decision making. In the context of process monitoring and forecast, predictive analytics has been applied to making predictions about the future state of a running process instance - for example, which task will be carried out next, when and who will perform the task, when will an ongoing process instance complete, what will be the outcome …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
- Centre for Data Science
Explainability of outlier detection methods
Outliers are anomalous observations in a data set that are "outside the norm" of what would be expected. Identifying outliers is an important part of exploratory data analysis and data analysis in general. It is often a challenging problem and calls for advanced methods and approaches, including machine learning-based tools. As methods become more and more complex, their explainability becomes more difficult and more important. This research project will look at all aspects of explainability and explore new approaches and …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Assessing the quality of cluster analysis
Machine learning cluster methods are common classification methods, but methods for assessing performance are limited as are methods for explaining how they work. Exploring methods for both assessing and explaining performance are the subject of this research with application to real-world contexts with the Australian Bureau of Statistics.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
A Human-centric eXplainable Automated Vehicle
CARRS-Q has developed a strong expertise in AV and ADAS, and operate an Automated Vehicle for its research on test track and open roads.We have collected more than 12,000km of sensor data in various Australian conditions, and we are progressing quickly to a broader understanding of safe operation of AV technologies on our roads. We are looking for PhD candidates to progress further on these topics. PhD positions are available for highly motivated domestic and/or international students to work on …
- Study level
- PhD
- Faculty
- Faculty of Health
- School
- School of Psychology and Counselling
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