Honours and Masters project

Theory and applications in data analytics of time series became popular in the past few years due to the availability of data in various sources. This project aims to investigate and generalise Hybrid and Neural Network methods in time series to develop forecast algorithms. The methodology will be developed as a theoretical construct together with wide variety of applications.

Behavioural manifestations of epileptic seizures (ESs) and certain non-epileptic seizures (psychogenic non-epileptic seizures, or PNESs) have considerable overlap, and so discerning between these solely based on clinical criteria is difficult.  Video EEG (electroencephalogram) monitoring (VEM) has high resource demands and is also expensive.  We endeavour to classify seizures based on non-invasive measures.

Congenital heart disease affects 1 in 100 babies. Spatial gene expression patterns are critical to understand how the heart develops and what underlying genetic patterns are behind heart malformation. High-throughput spatial temporal data have been recently generated with spatial transcriptomics technologies. Capitalising on these rich datasets, we aim to build a custom analysis workflow in which the cells are profiled with precise spatial gene expression information. The student will provide fundamental contribution to of this project, by:

Develop, implement, and test deep learning techniques for automatic classification of epileptic seizures using video data of seizures

Turning Point’s National Ambulance Surveillance System is a surveillance database comprising enriched ambulance clinical data relating to alcohol and other substance use, suicidal and self-injurious thoughts and behaviours, and mental health-related harms in the Australian population. These data are used to inform policy and intervention design and are the subject of ever-increasing demand from academic professionals and units, government departments, and non-government organisations.

Deep learning has achieved ground-breaking performance in many vision tasks in the recent years. The objective of this project is to apply state-of-the-art visual-language foundation models such as Qwen for medical image analysis and report generation.

Deep learning has achieved groundbreaking performance in many 2D vision tasks in recent years. With more and more 3D data available, such as that captured by LiDAR, the next research trend is to conduct advanced 3D perception and generation tasks. The objective of this project is to study the state-of-the-art 3D foundation models and apply them for tasks that require spatial intelligence.

For decades, the primary way people who are blind or have low vision (BLV) interact with computers has been through screen readers. These linearise an application or document interface, presenting content as synthetic speech or on a single-line braille display, which the user navigates using keyboard shortcuts or touch gestures on a smartphone. Screen readers have a major limitation: they cannot show graphics. Instead, they rely on alternative text descriptions, which are often incomplete or inadequate.

Vision-Language Models (VLMs) are increasingly capable of understanding visual scenes, interpreting natural-language instructions, and reasoning across visual and textual information. These capabilities create new opportunities for intelligent systems that can interact with users and understand their surroundings. However, most state-of-the-art VLMs are designed for cloud infrastructure or high-performance GPU systems, making their direct deployment on embedded platforms challenging because of memory, computation, latency, and energy constraints.

Embodied intelligence represents a rapidly emerging paradigm in robotics where intelligent behaviour arises through the integration of perception, decision-making, action, and interaction within a physical agent operating in the real world. Advances in autonomous robotics, computer vision, sensor fusion, and human-robot interaction have enabled mobile robotic systems to perform increasingly sophisticated tasks in dynamic environments.