Research projects in Information Technology

In this project we are interested in exploring new paradigms for solving Combinatorial Optimisation problems, and generally NP-hard ones. One direction of research could consist in using approximation algorithms for deriving dual bound within a branch-and-bound algorithms. Other directions could use Machine Learning or new decompositions. This subject is generally quite open so it is important to be highly creative.

The brain is a complex machine and brain function remains yet to be fully understood. This project works at the intersection of dynamical modelling, statistical signal processing, statistical inference and machine learning to develop subject specific mathematical models of the brain that can be used to infer brain states and monitor and image the brain. This work is centred around a  neurophysiological variable estimation framework we have been developing that can be applied to all kinds of brain activity recordings.

We are seeking a highly motivated and innovative PhD student interested in exploring the opportunities for using AI to enhance personalisation of services and resource recommendations, ultimately optimising the overall user journey. This project will improve the care pathway experience for young people and families accessing mental health services through the headspace website.

Possible approaches to addressing this challenge might include:

This project will seek to further the research into and development of machine learning techniques that may be used to triage, classify, and otherwise process material of a distressing nature (such as child exploitation material). It will involve the use of deep neural networks for image, video, audio, social network, and/or text classification.

This project aims to identify novel methods for inferring where and when photographs and videos were recorded from features of the material itself. A key requirement of image processing in a Law Enforcement (LE) context is to augment classification of material by identifying its spatio-temporal context.

Adversarial Machine Learning (AML) is a technique to fool a machine learning model through malicious input. Due to its significance in many scenarios, including security, privacy, and health application, AML has attracted a large amount of attention in recent years. However, the underlying theoretical foundation for AML still remains unclear and how to design effective and efficient attack and defence algorithms are remain a challenge in the research community. Furthermore, most existing  AML algorithms can only apply to Euclidean space.

The brain is a complex system and monitoring and imaging methods to observe critical neurophysiological variables underlying brain function are limited. This project works at the intersection of statistical signal processing, inference, machine learning and dynamical systems theory to develop new semi-analtyical filtering approaches for state and parameter estimation to infer neurophysiological variables such as network connection strengths between neural population networks underlying brain activity.

I am seeking PhD candidates interested in working on designing Learning Analytics innovations to study classroom proxemics by analysing and visualising indoor positioning data (along with other sources of evidence such as audio, physiological activity and characteristics of the students).

The meaning of an utterance depends on the broader context in which it appears. The context may refer to the paragraph, document, conversational history, or the author who has generated the utterance. In this project, we develop effective methods for translating text using the context, e.g. the rest of the sentences in the document or the conversational history.

Insects are vital components of natural and agricultural ecosystems that interact with plants in complex ways. Computer simulations can help us understand these interactions to improve crop production, and to assist us to sustain our natural ecosystems as we change the Earth's climate. This technology is vital to inform our strategies to protect global food supplies and manage our national parks and forests.