Student topics

Securing the information manipulated by computer systems, such as privacy and integrity in social software, is a challenge. Traditional methods to impose limits on the information disclosure, such as access control lists, firewalls, and cryptography, provide no guarantees about information propagation. For instance, cryptography provides no guarantees about the confidentiality of the data are given once it is decrypted.Information flow control (IFC) is the problem of ensuring secure information flow according to specified policies within computer systems. Modern applications are …

Study level

PhD, Master of Philosophy

Faculty

Faculty of Science

School

School of Computer Science

Deep learning is a mainstream to increase the capability of industry systems, particularly for those with massive data input and output. It is seen that many tools are now claimed to be freely available and could facilitate such process of development and deployment significantly with scalability and quality.However, limited attention has been on developing reproducible and productive workflows to identify the tools and their values towards large-scale industry systems. In this project, we will explore how to design such a …

Study level

PhD, Master of Philosophy

Faculty

Faculty of Science

School

School of Computer Science

Applying machine learning algorithms to source code related SE task is rapidly developing and attracts the attention from both researchers and industry engineers. While there are many program languages available, applying such techniques, i.e., the representation learning models, for different languages may achieve different performance. Particularly, they all have their own strict syntax, which determines the abstract syntax tree. Thus, a lot of different open-source supply chain are available, for example the parsing tools are used to build AST from …

Study level

PhD, Master of Philosophy

Faculty

Faculty of Science

School

School of Computer Science

Software vulnerability is a major threat to the security of software systems. Thus, the successful prediction of security vulnerability is one of the most effective attack mitigation solutions. Existing approaches for software vulnerability detection (SVD) can be classified into static and dynamic methods. Powered by AI capabilities, especially with the advancement of machine learning techniques, current software has been produced with more sophisticated methodologies and components. This has made the automatic vulnerability proneness prediction even more challenging. Recent research efforts …

Study level

PhD, Master of Philosophy

Faculty

Faculty of Science

School

School of Computer Science

This research project aims to investigate the effects of interactions with intelligent agents on player experiences in various contexts, including videogames, learning-teaching, and complex data analysis. The intelligent agents will be developed using ChatGPT as a backend, and the studies will be conducted in both single-player and multiplayer settings, utilizing virtual and augmented reality technologies.

Study level

PhD, Master of Philosophy, Honours

Faculty

Faculty of Science

School

School of Computer Science

Stochastic simulation-based models are very attractive to study population-biology, disease transmission, development and disease. These models naturally incorporate randomness in a way that is consistent with experimental measurements that describe natural phenomena.Standard statistical techniques are not directly compatible with data produced by simulation-based stochastic models since the model likelihood function is unavailable. Progress can be made, however, by introducing an auxiliary likelihood function can be formulated, and this auxiliary likelihood function can be used for identifiability analysis, parameter estimation and …

Study level

PhD, Master of Philosophy

Faculty

Faculty of Science

School

School of Mathematical Sciences

Research centre(s)

Centre for Data Science

The tourism industry is rapidly expanding in Antarctica, increasing by an order of magnitude over the past two decades. The resilience of this industry depends on the resilience of the Antarctic ecosystem but which element of Antarctica is the most important? Is it better a penguin or an iceberg?The proposed study will make use of social media channels to collect data on Antarctica tourism. By assessing photos’ captions and Twitter hashtags, the study aims to determine how frequently terms such …

Study level

Honours

Faculty

Faculty of Science

School

School of Mathematical Sciences

Monte Carlo methods use random sampling to approximate solutions to challenging problems. These methods are helpful for statistical models with many parameters, as discussed in this short video. The methods are particularly useful for Bayesian inference where one wishes to get a rigorous understanding of parameter uncertainty.Despite having many advantages over their competitors, Monte Carlo methods can be very slow in the context of big data. In this project, you'll help develop scalable Monte Carlo methods to enable timely and …

Study level

PhD, Master of Philosophy, Honours

Faculty

Faculty of Science

School

School of Mathematical Sciences

Research centre(s)

Centre for Data Science

Dementia exhibits the presence of Lewy bodies in the cerebral cortex, which are composed of α-synuclein (αSYN) or Amyloid-β (Aβ) plaques, as well as hyperphosphorylated tau (P-tau) tangles in various forms of dementia. The exact pathological mechanisms underlying this disease are not well understood; however, there is evidence suggesting the involvement of inflammatory activity. Microglia, macrophage cells residing in the brain responsible for clearing external pathogens and dead cells, are of particular interest.Our study aims to investigate whether Lewy bodies …

Study level

Master of Philosophy, Honours

Faculty

Faculty of Science

School

School of Biomedical Sciences

Research centre(s)

Centre for Microbiome Research

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

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

Aerosols, tiny solid or liquid particles, play an important role in global climate regulation, firstly, by scattering and absorbing incoming solar radiation and, secondly, by their ability to take up water vapor from the atmosphere and serve as nuclei for cloud droplet formation (Cloud Condensation Nuclei (CCN)).With oceans covering 71% of the Earth’s surface, marine aerosols present a significant proportion of the global aerosol budget. Production of particles in the marine environment occurs via 2 pathways: 1) wave breaking and …

Study level

PhD, Master of Philosophy

Faculty

Faculty of Science

School

School of Earth and Atmospheric Sciences

Research centre(s)

Centre for the Environment