Honours and Minor Thesis projects

Today's society and its organisations are becoming ever-increasingly dependent upon Information and Communication Technology (ICT) systems mostly based in cloud data centres. These cloud data centres, serving as infrastructure for hosting ICT services, are consuming a large amount of electricity leading to (a) high operational costs and (b) high carbon footprint on the environment. In response to these concerns, renewable energy systems are shown to be extremely useful both in reducing dependence on finite fossil fuels and decreasing environmental impacts.

This project takes a different approach to RL, inspired by evidence that Hippocampus replays to the frontal cortex directly. It is likely used for model building, as opposed to the mainstream view in cognitive science and ML - where 'experience replay' ultimately improves policy. The predicted benefits are sample efficiency, better ability to generalize to new tasks and an ability to learn new tasks without forgetting old ones.

SCIPPlan is a mathematical optimisation based automated planner for domains with i) mixed (i.e., real and/or discrete valued) state and action spaces, ii) nonlinear state transitions that are functions of time, and iii) general reward functions. SCIPPlan iteratively i) finds violated constraints (i.e., zero-crossings) by simulating the state transitions, and ii) adds the violated constraints back to its underlying optimization model, until a valid plan is found. The purpose of this project is to improve the performance of SCIPPlan.

Medical guidelines provide human experts with steps on conducting diagnosis. While existing online knowledge graphs such as Unified Medical Language System (UMLS) [1] provide a wide coverage of various biomedical entities, their coverage is limited in certain specific domains such as Ophthalmology. In this project we will build an automated system that convert the offline medical guidelines of Glaucoma [2] and Diabetic Retinopathy [3] to machine readable knowledge graphs.

Screen recordings of mobile applications are easy to obtain and capture a wealth of information pertinent to users (e.g., tutorials, feature usages), making them a popular mechanism for crowdsourced app usage. Manually following the screen recordings to replay the app usage can be a time consuming process, due to the long length of the recordings and UI bias between screens. Therefore, for users and developers, it is crucial to accelerate the replay process.

For Australians with impaired vision, accessible books are a lifeline to education and vital everyday information, and also to the independence and personal autonomy that sighted people take for granted. Yet much literature remains in an inaccessible format. 

Neuroscience is becoming an exciting and multidisciplinary field, with a combination of biology, psychology, engineering, and large-data processing. This project is suitable for those who are motivated to apply data-processing skills to biological questions. Our research projects aim to investigate how neural circuits in the mouse brain work during a behavioral task; we visualize neural activity in vivo using advance fluorescent microscopy (two-photon imaging), while filming the behavior of mice.

The aim of this project is to probe, conceptualize, and assess the fusion of generative AI with data comics to augment their narrative potency, user comprehension, and user engagement. Data comics, an innovative blend of data and visual artistry, effectively portray intricate narratives in an accessible comic strip format. By integrating generative AI, we can automate and enrich the storytelling process, further enhancing the visual and narrative depth of data comics. The primary areas of research encompass AI, data visualisation, and user experience.

This project is part of a larger project on Hand Hygiene, which aims to address hand hygiene in hospitals by combining a suite of digital monitoring products with advanced data analytics. We expect to create a system that monitors the full user journey and performs constant data analysis through a cloud-based system.

The aim for this project is to research, prototype and/or evaluate approaches to increase the explanatory effectiveness of the visualisations contained in analytics dashboards or similar support data-intensive tools. Explanatory visualisations are those whose main goal is the presentation and communication of insights. By contrast, exploratory visualisations are commonly targeted at experts in data analysis in search of insights from unfamiliar datasets.