Our projects

This project uses Nanopore sequencing to map micro-exon splicing events at a single cell level in human induced pluripotent stem cell derived brain organoids to gain insight into how abnormal micro-exon splicing leads to neurodevelopmental defects that cause epilepsy.

Microfluidic technology has come a long way since its advent and now we can carry out various analytical work on a microchip that usually required a large lab and multiple manpower.

Sugarcane is produced in large amounts in both India and Australia generating large amounts of bagasse as by-products of sugar production. This project will evaluate the options to identify the best use of this resource in India, Australia, and globally.

The project aims to develop new class of light-emitting devices operating at very high current densities.

This project is to develop a novel device that can effectively capture and release platelets from blood samples. The device will combine two advanced technologies that includes nanoyeast based capturing and “Electro-hemodynamics” induced release of captured platelets.

Novel materials and systems are being introduced into the built environment at an increasing rate. These often result in unforeseen consequences in the event of accidents, impacting on resilience of buildings. This project identifies the contribution of novel materials to building resilience.

The resource-constrained nature of internet-of-things (IoT) make them vulnerable against artificial intelligence (AI)-assisted cyber-attacks. In this project, we will develop an adversarial AI-driven framework for designing and evaluating an intrusion detection system that can detect such attacks.

This project addresses the cybersecurity of critical infrastructure by designing automated vulnerability detection testkits for heterogenous industrial control systems (ICS) and industrial Internet of Things (IIoT) equipment. The project will utilise the facilities of the UQ Industry 4.0 TestLab.

Software-defined networking (SDN) is a new paradigm that allows the use of programmable network devices, and separates the control and data planes. In this project, we will solve some key research problems on the control and security of SDN using machine learning techniques.

This project will analyse the resilience of communities vulnerable to fire hazards. With a specific focus on studying the resilience of communities with different vulnerability profiles, the aim of the project will help to inform policy and resource allocation to improve fire safety outcomes.