Research projects in Information Technology

As a pregnancy approaches term (the point at which the foetus is considered fully developed), decisions are made about the timing of birth and the way babies are born. These decisions are incredibly challenging for clinicians and pregnant women. Digital health records, advances in big data, machine learning and artificial intelligence methodologies, and novel data visualisation capabilities have opened up opportunities for a dynamic, ‘Learning Health System’ – where data can be harnessed to inform real-time and personalised decision-making.

Metals are made of small crystals - i.e., atoms are arranged in a particular geometric arrangement, which are typically in the range of a few 10s of microns (0.01 mm). The arrangement of these crystals greatly affects the performance of the metal and hence the performance of components where metals are used - such as in aeroplanes, gas turbine engines, cars, etc. The manner in which such materials deform, crack and fail under a variety of conditions is an important area in terms of cost and safety.

This project aims to identify novel methods for inferring actors, activities, and other elements from short message communications. Covert communications are a specialist domain for analysis in the Law Enforcement (LE) context. In this project we aim to improve law enforcement’s understanding of online criminal communications, exploring texts for automated understanding of intent, sentiment, criminal capability, and involvement.

This project will investigate and develop the ways in which AI algorithms and practices can be made transparent and explainable for use in law enforcement and judicial applications

The use of AI in law enforcement and judicial domains requires consideration of a number of ethical issues.  This project will investigate and develop frameworks that embed ethical principles in the research, development, deployment ,and use of AI systems in law enforcement (LE). A major focus is expected to concern the acquisition, use, sharing and governance of data for AI in this context.

Complex questions are those that involve discrete, aggregate operators that operate on numbers (min, max, arithmetic) and sets (intersection, union, difference). Recent advances in complex question answering take a neural-symbolic approach and combine meta-learning and reinforcement learning techniques [1,2,3]. On the other hand, the generation of complex questions, the dual problem, is less explored. Recent works on knowledge graph question generation [4,5] have mainly focussed on multi-hop questions.

This scholarship will provide a stipend allowance of $29,000 AUD per annum for up to 3.5 years, plus $4,000 travel allowance. If you are currently in Australia you are strongly encouraged to apply. If successful, you will join Dr. Roberto-Martinez Maldonado, Prof. Dragan Gasevic, and a strong team of academics, researchers and other students at the Centre of Learning Analytics at Monash University, Melbourne.

The Australian Research Council (ARC) Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA) is seeking applications for ten ARC fully-funded PhD projects with generous top-up scholarships.

We're looking for talented students with a background in mathematics, computer science, statistics, economics, engineering or other related fields. These positions are offered across OPTIMA's nodes located at Monash University or The University of Melbourne. Projects will be available from June 2021 onwards.

The proposed PhD project aims to build a machine learning/deep learning-based decision support system that provides recommendations on precision medicine for paediatric brain cancer patients based on clinical, genomics and functional dependency data (CRISPR, drug screens).

Anomaly detection is an important task in data mining. Traditionally most of the anomaly detection algorithms have been designed for ‘static’ datasets, in which all the observations are available at one time. In non-stationary environments on the other hand, the same algorithms cannot be applied as the underlying data distributions change constantly and the same models are not valid. Hence, we need to devise adaptive models that take into account the dynamically changing characteristics of environments and detect anomalies in ‘evolving’ data.