Research projects in Information Technology

    DNA or RNA motif discovery is a popular biological method to identify over-represented DNA or RNA sequences in next generation sequencing experiments. These motifs represent the binding site of transcription factors or RNA-binding proteins. DNA or RNA binding sites are often variable. However, all motif discovery tools report redundant motifs that poorly represent the biological variability of the same motif, hence renders the identification of the binding protein difficult.

Antimicrobial resistance (AMR) is one of the most significant and immediate threats to health in Australia and globally. As an Infectious Diseases physician and researcher, the second supervisor is working on harnessing new technologies such as next-generation sequencing and artificial intelligence to improve the diagnosis, treatment and prevention of AMR infections. The specific aims of this project are:

There are sometimes emerging prolonged periods of highly persistent evolution in time series.

Species’ distributions are shifting in response to global climate change and other human pressures. Accurate methods to monitor and predict distribution shifts are urgently needed to manage threatened species and ecosystems, and to control invasive species and diseases. This requires a step-change in the data and methods used to monitor and predict organism behaviours and ultimately shifts in species' distributions.

This PhD project is funded by a successful ARC Discovery Project grant: "Improving human reasoning with causal Bayesian networks: a user-centric, multimodal, interactive approach" and the successful applicant will work as part of a larger research team.

Effective allocation of tasks is essential for any socially living group. This project investigates self-organised task allocation, ie groups in which tasks are not centrally assigned to individuals. In self-organised groups, individuals rather select their tasks autonomously based on their own choices and preferences. Under which conditions does this achieve the desired group outcomes?

We have a range of potential research projects on offer in partnership with VIFM - https://www.vifm.org/ - looking at ML techniques in predicting forensic diagnoses  / image analysis, across multiple data types found at VIFM. These include atomic data, text data and text documents, medical images, clinical photographs and digital pathology slides. 

This research has high potential to support our IT for social good agenda in addition to its technical attractiveness. 

#digitalhealth #health #AI #ML #imagenalysis #computervision #CT #forensics 

Reasoning, constraint solving and optimisation technologies have made remarkable progress over the last two decades. A number of formalisms like Boolean satisfiability (SAT), satisfiability modulo theories (SMT) and their optimisation extensions (MaxSAT and MaxSMT) as well as constraint programming and optimisation (CP) and mixed-integer linear programming (MILP) can be seen as success stories in computer science.

Time series are an ever growing form of data, generated by numerous types of sensors and automated processes. However, machine learning and deep learning methods for analysing time series are much less advanced than for other forms of data.

Our research is revolutionising the analysis of time series data. But it is early days, and many more impactful challenges are yet to be overcome.

This project is funded by the Australian Research Council and will be conducted as part of a large world-leading research team.

This project works with leading researchers in the Faculty of Pharmacy to develop new artificial intelligence technologies to aide discovery of drugs to treat pharmacoresistant epilepsy.

You can find some of our publications here: https://i.giwebb.com/research/computational-biology/