You can spend the first three weeks (often less) at the start of the honours programme to find a research project interesting enough to motivate you for the entire year.
The research project can be supervised by supervisor(s) outside the department, so long as it has a computational focus, but at least one of your supervisors must be an academic within the department.
|Link to homepage||Research Interests||ERO Groups|
|Bennamoun, W/Prof Mohammed||control theory, robotics, obstacle avoidance, object recognition, artificial neural networks, signal/image processing and computer vision (particularly 3D)|
|Cardell-Oliver, Assoc/Prof Rachel||data mining, wireless sensor networks; computing education; and formal methods in software engineering|
|Datta, Professor Amitava||parallel and distributed computing, mobile and wireless computing, bioinformatics, social networks, data mining and software testing|
|French, Dr Tim||extensions of modal logics, and their applications to formal methods for software engineering. I wrote my PhD thesis on bisimulation quantifiers for modal logics. This work has applications in the automated reasoning about properties of various systems and programs, and particularly for reasoning about different levels of abstraction. Recently, I have also been interested in many epistemic logics and formal langauges, particularly for reasoning about different levels of agent awareness|
|Glance, Dr David||Techno-sociology, eHealth, Open Source and various other areas|
|Huynh, Dr Du||Computer vision and video analysis; in particular, marker-free human motion capture from video, visual tracking, projective geometry, shape and outlier detection from multiple images or video sequences, 3D reconstruction from images, structured light stripe systems. I am also interested in machine learning and pattern recognition|
|Liu, Dr Wei||
Text Mining, Natural Language Processing, Data Mining, Dialogue Management and Multi-Agent Systems
My research focuses on knowledge engineering for distributed environments. In particular, semi-automatic ontology learning through analysing large text-corpus analysis using techniques including
|MacNish, Assoc/Prof Cara||My research focusses on systems that are able to adapt their response to the data, knowledge or environment in which they are working. My students and I study and develop computational systems that employ learning, optimisation and modelling techniques to improve system performance on complex problems.|
|Mian, Assoc/Prof Ajmal||SMART SURVEILLANCE, 3D FACE RECOGNITION, 3D OBJECT RECOGNITION, 3D MODELING,INTERNET KEY EXCHANGE|
|McDonald, Dr Chris||Chris has recently taught in the areas of computer networking; security & privacy; mobile & wireless computing; software design & implementation; C programming; and operating systems at The University of Western Australia and Dartmouth College. Together with these areas, his research interests include wireless, ad-hoc, & mobile networking; network simulation; and software tools for Computer Science Education.|
|Reynolds, Assoc/Prof Mark||Logic, geoscience modelling, Ecological Modelling, Software Engineering, and AI and pattern recognition|
|Smith, Professor David||David is generally interested in all problems in computational biology, including cellular signal transduction, bone, cartilage, tendon, cell mechanics, physiology of the kidney, problems in developmental biology and others. Furthermore, he is interested in all kinds of geotechnical and geoenvironmental engineering problems, but particularly multiscale models for clay soils. Experience as per published papers listed below.|
|Wise, Dr Michael||Bioinformatics, Computational Biology, Microbial Informatics|
|While, Dr Lyndon||Evolutionary algorithms: Walking Fish Group, Industrial applications of EAs, Multi-objective EAs, Noise in EAs, Game-playing EAs, Pub quiz at CIG'08, Special session on EC for Design at CEC'05. Functional programing implementation: Parallel pattern-matching via source-level transformation, Incremental garbage collection via self-modifying code.|
Our aim is to develop new techniques and systems to manage and make sense of big data.
Our research develops optimisation, scheduling and control solutions for the mining and offshore extraction sectors and associated operations, such as transport, energy supply and the servicing of remote communities. With a strong focus on real-time optimisation, we devise new ways for work plans and models to be rapidly adapted as needed. In addition to extensive technical expertise, the group has extensive experience in applied engineering, combinatorial mathematics, and in delivering practical reasoning and analytical tools across a range of commercial industrial operations.
Our researchers aim to bring significant change in the field of asset health management with improved sensing diagnostics and prognostics. We identify and minimise the gaps between research frontiers and practical application to improve predictive asset management, operation fault detection and a project’s overall lifecycle performance.