Honours and Masters project

Propositional satisfiability (SAT) is a well-known example of NP-complete problems. Although NP-completeness may be perceived as a drawback, it allows one to solve all the other problems in NP by reducing them to SAT and relying on the power of modern SAT solvers. This is confirmed by a wealth of successful examples of use cases for modern SAT solving, including generalisations and extensions of SAT as well as a wide variety of practical applications in artificial intelligence (AI).

Since the COVID-19 pandemic there has been an increasing shift within higher education away from traditional lectures and towards asynchronous content delivery through pre-recorded videos. This has a number of benefits: students can consume content at their own pace, videos can be reused, and production value can be increased. However, academics typically have no training or experience in video production, so pre-recorded videos are most often just a simulacrum of a standard lecture (i.e., a slideshow with voiceover).

This project is within the scope of the project “Artificial Intelligence in carDiac arrEst” (AIDE), which was led by Ambulance Victoria (AV) in Australia, involving a team of researchers at Monash University. This AIDE project has developed an Artificial Intelligence (AI) tool to recognise potential Out-of-Hospital-Cardiac Arrest (OHCA) during the Triple Zero (000) call by using transcripts produced by Microsoft Automatic Speech Recognition service. In the next step, we aim to optimise the workflow of call-takers and investigate which workflows can lead to earlier identification of OHCA.…

Access to cultural institutions, such as galleries and museums, is often compromised for people with disability. This includes people who are blind or have low vision (BLV). This project seeks to improve experiences within cultural institutions such as galleries and museums for BLV people, by applying AI and human-centred design principles to the creation of mediating artefacts and experiences.

This is a research and development project focused on designing a Generative AI tool that supports equitable team practices in software development. The project combines qualitative research, such as persona profile creation, with AI prototyping to explore how GenAI can foster inclusion, improve team dynamics, and accommodate diverse working styles in technical environments. The outcome includes both a functional AI prototype and practical resources for inclusive collaboration.

Inclusive learning environments are essential for ensuring that all students can fully participate and succeed. Neurodivergent learners—including those with ADHD, autism, dyslexia, and other cognitive differences—often navigate university settings that were not designed with their learning profiles in mind. Challenges related to feedback interpretation, cognitive load, communication, and assessment design can create barriers that impact learning, confidence, and wellbeing.

Minimum Message Length (MML) is an elegant information-theoretic framework for statistical inference and model selection developed by Chris Wallace and colleagues. The fundamental insight of MML is that both parameter estimation and model selection can be interpreted as problems of data compression. The principle is simple: if we can compress data, we have learned something about its underlying structure.

Expected outcomes: The student will learn inference and representation learning methods for network data. The knowledge can be easily used to analyse other networks, including but not limited to social networks, citation networks, and communication networks. A research publication in a refereed AI conference or journal is expected. A student taking this project should ideally have at least a reasonable background mathematical knowledge, including differential calculus (e.g., partial derivatives) and matrix determinants.

This project investigates the technical dimensions of real estate asset tokenization, with a particular focus on the challenges of integrating blockchain technology into the real estate sector. While tokenization promises to enhance liquidity, efficiency, and transparency in property transactions, its practical implementation faces significant technological hurdles. The project will examine key issues such as data interoperability, smart contract design, secure digital identity management, and scalability of blockchain networks in handling complex real estate assets.