Research projects in Information Technology

We normally think of drawing as an (almost) exclusively human activity. The idea behind this research is to explore the concept of post-anthropocentric creativity. We want to understand what art made by an autonomous, non-human intelligence might look like, and if artificial systems can exhibit what we recognise as creative behaviour. This behaviour and the drawings produced might not be the same as what humans would do.

Since the 1990s, researchers have known that commonly-used public-key cryptosystems (such as RSA and Diffie-Hellman systems) could be potentially broken using an efficient algorithm running on a hypothetical quantum computer based on the principles of quantum mechanics. This potential threat remains a theoretical possibility, but may become a real threat in coming years  due to significant advances in quantum computing technology. 

Cybersecurity is regarded as a high priority for governments and individuals today. With the practical realization of quantum computers just around the corner, classical cryptographic schemes in use today will no longer provide security in the presence of such technology. Therefore, cryptography based on “Post-Quantum” (PQ) techniques (that resists attacks by quantum computers) is a central goal for future cryptosystems and their applications.

Automated Program Repair (APR) is the grand challenge in software engineering research. Many APR methods have shown promising results in fixing bugs with minimal, or even no human intervention. Despite many studies introducing various APR techniques, much remains to be learned, however, about what makes a particular technique work well (or not) for a specific software system.

The most challenging problems of our time are social dilemmas. Thes are situations where individuals are incentivised to free ride on others, but successful group outcomes depend on everyone’s contributions. Examples include, climate change action or compliance with non-pharmaceutical interventions in a large-scale pandemic. In both cases, individuals can rely on others doing their share, but when everyone adopts such a free-riding strategy the public good collapses [1].

This project aims to design effective and intelligent search techniques for large scale social network data. The project expects to advance existing social network search systems in three unique aspects: utilizing the geographical locations of queries and social network data to provide more relevant results; acknowledging and handling inherent uncertainties in the data; and exploiting knowledge graphs to produce intelligent search results. Expected outcomes of this project include a next-generation social network search system and enhanced international collaborations.

 Although deep learning has produces state of the art results on many problems, it is a data hungry technology requiring a lot of human supervision in the form of annotated data. Potential PhD topic include learning to learn and meta-learning, active learning, semi-supervised learning, multi-task learning, transfer learning, and learning representations for NLP. Techniques include deep generative models (eg auto-encoders and generative adversarial networks) and reinforcement/imitation learning algorithms for Markov Decision Processes.

The project develops methods to use acoustic data for the identification of animals in the wild and in controlled settings. It is part of a broader effort to build AI-enabled methods to support biodiversity and sustainability research. The initial objective is to use deep learning techniques to perform acoustic species identification in real-time on low-cost sensing devices coupled to cloud-based backends. Ultimately, we are aiming to move to Edge-AI, ie.

The proposed project aims to develop new methodologies for developing NMT systems between extremely low-resource languages and English. Recent advances in neural machine translation (NMT) are a significant step forward in machine translation capabilities. However, "NMT systems have a steeper learning curve with respect to the amount of training data, resulting in worse quality in low-resource settings".

Our research group tries to decipher the rules that govern decision making in social groups, from animals that forage and hunt in groups to humans that work in teams.