Our research

Expertly advancing Australia’s engineering future.

ADFA Canberra UNSW female students in lab

The School of Engineering & Information Technology at UNSW Canberra is at the forefront of engineering and technology research and further advances our understanding of the world. As a forward-thinking school with a respected voice, our research helps shape our society and our region’s defence and security future. In a wide range of fields, our dedicated researchers are engaged in cutting-edge exploration across areas important to creating a sustainable future.

Join our collaborative community to have a real-world impact and contribute to Australia’s technological edge. 

Whether you’re interested in cybersecurity, space engineering, robotics, civil engineering, computer science, electromagnetics or hypersonics, see how we are creating solutions to the biggest issues facing our planet.  

Designing the future of bushfire modelling

Providing accurate predictions of the spread of wildland fires has long been a goal of the fire research community. Our research supports the need to better understand the interactions between fire, fuel, weather and topography. Factors include rate of spread, flame height, intensity and spotting for wildfire. 

Decision support and analytics

Decision support and analytics are necessary to assist enterprises in making better decisions. Data visualisation, statistical analysis, predictive modelling, and prescriptive analytics are some examples of this. We’re working to close the research gap in developing acceptable XAI approaches for intelligent risk management systems. 

Ethics, society and technology

Our projects include engineering ethics education, as well as developing ethical frameworks that encourage ethically and socially sound systems design. We scrape online digital content to better understand societal preferences and values regarding innovations, and how these may change over time, and the moral aspects of AI systems.  

Deep learning - achieving effective results

The huge success of deep learning artificial intelligence models has inspired many researchers to apply them to an increasingly wide range of application domains. Yet, the deep learning field is developing rapidly, and models are increasingly complex.  Our expert group works to maximise research results through the application of deep learning.   

Distributed intelligence

Our team adopts multidisciplinary approaches that take inspiration from biological systems while utilising AI & machine learning. We focus on designing solutions for the following swarm and multi-agent problems: guidance and control, teaming with humans, collective decision making, multi-agent learning, transparency and knowledge representation.  

Systems & control

Our research group delivers world-class research in the areas of control theory, quantum control engineering, applied mathematics, quantum optics, machine learning and control systems engineering. We develop fundamental theories and novel principles and methodologies to create new opportunities in a range of control.

Serious games

Beyond entertainment, serious games seeks to harness humankind’s propensity to play to yield outcomes ranging from education and training to better health and even organisational decisions. At the intersection of simulation, AI, and HCI the serious game group works in areas ranging from G4H (Games 4 Health) through to G4D (Games 4 Defence) as well as applying gamification and playification for educational purposes.

IoT Cyber Security Laboratory 

IoT Cyber Security Laborartory researches the development of novel defensive cyber security measures for future networks. We work at the intersection of IoT security, smart devices, and machine learning.

Tradespace exploration 

Tradespace exploration is a research approach that utilises intelligent algorithms rooted in computer science to systematically analyse the trade-offs among different design alternatives, options, and parameters. Its goal is to optimise the performance, cost, and other relevant factors of a system or product, empowering informed decision-making and efficient allocation of resources.

Canberra Evolutionary Optimisation (EvOpt)

The key objective of this group is to design and development optimisation techniques for solving complex decision and optimisation problems. It covers computational intelligence, population based search algorithms (such as evolutionary algorithms) as well as conventional search algorithms.