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Research projects in Information Technology

Displaying 171 - 180 of 184 projects.


Discrete Optimisation for Multi-Agent Path Finding

The Multi-Agent Path Finding (MAPF) is a combinatorial problem in which agents must find a path from a start to a goal location without colliding with each other. The optimisation group at Monash is leading research in this area and has designed some of the most efficient methods to solve MAPF. Companies like Amazon have funded the optimisation group at Monash to do research on MAPF as it relies on this technology for its automated warehouses and fulfilment centres.

Supervisor: Dr Pierre Le Bodic

The Ethics of AI Art

In recent years, AI techniques such as GANs and associated deep learning neural networks have become popular tools applied to the production and creation of works of art. In 2018, AI Art made headlines around the world when a “work of art created by an algorithm” was sold at auction by Christie’s for $432,500 – nearly 45 times the value estimated before auction.

Supervisor: Prof Jon McCormack

Teaching Robots to Draw

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.

Supervisor: Prof Jon McCormack

Quantum-Resistant Public-Key Cryptography

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. 

Supervisor: Ron Steinfeld

Quantum Resistant Cryptographic Protocols

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.

Supervisor: Dr Amin Sakzad

Algorithm Selection for Automated Program Repair

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.

Supervisor: Aldeida Aleti

Computational Models for Complex Social Dilemmas

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].

Indoor Data Management

A large part of modern life is lived indoors such as in homes, offices, shopping malls, universities, libraries and airports. However, almost all of the existing location-based services (LBS) have been designed only for outdoor space. This is mainly because the global positioning system (GPS) and other positioning technologies cannot accurately identify the locations in indoor venues.

Neural Machine Translation for Low-Resource Languages

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".

Deep learning from less human supervision

 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.