Research projects in Information Technology

For better or for worse, Generative AI is changing our world.

Markov Decision Processes (MDPs) are frameworks used to model decision-making in situations where outcomes are partly random and partly under the control of a decision maker. While small MDPs are inherently interpretable for people, MDPs with thousands of states are difficult to understand by humans. The K-MDP problem is the problem of finding the best MDP with, at most, K states by leveraging state abstraction approaches to aggregate states into sub-groups.

Turning Point is a renowned addiction treatment and research centre specialising in the prevention, treatment, and support services for individuals affected by substance use disorders, gambling addiction, and mental health issues. Turning Point operates a network of 26 helplines across the country, ensuring accessible and immediate support for individuals in need. These helplines serve as a vital resource for individuals seeking assistance, information, and guidance related to addiction and mental health concerns.

Classical propositional logic (CPL) captures our basic understanding of the linguistic connectives “and”, “or” and “not”. It also provides a very good basis for digital circuits. But it does not account for more sophisticated linguistic notions such as “always”, “possibly”, “believed” or “knows”. Philosophers therefore invented many different non-classical logics which extend CPL with further operators for these notions.

Classical propositional logic (CPL) captures our basic understanding of the linguistic connectives “and”, “or” and “not”. It also provides a very good basis for digital circuits. But it does not account for more sophisticated linguistic notions such as “always”, “possibly”, “believed” or “knows”. Philosophers therefore invented many different non-classical logics which extend CPL with further operators for these notions.

This project will result in a much fuller understanding of the state of the Birrarung than is currently possible, as well as qualitative and quantitative results to model different interventions and their effect on swimmability.

The project will build tools and techniques to understand and decide on effective interventions to improve the Birrarung’s swimmability.

In many branches of science (e.g., Artificial Intelligence, Engineering etc.), the modelling of the problem is done through the use of functions (e.g., f(x) = y). On a very high-level, we can think of Machine Learning as the problem of approximating function f from the pair of measurements (x,y), and Optimization as the problem of finding the value of input x that maximizes the output y given function f.

Automating code generation, SQL query formulation, and data preprocessing pipelines is a crucial step toward intelligent and efficient software development. This project aims to leverage large language models (LLMs) to address these challenges by developing a comprehensive framework that seamlessly integrates LLM capabilities for generating accurate and optimised code, constructing complex SQL queries, and automating data transformations.

Recent advancements in foundation models have significantly improved AI systems' capabilities in autonomous tool usage and complex reasoning. However, their potential for location-based and map-driven reasoning—crucial for optimising navigation, resource discovery, and logistics—remains underexplored. This project aims to address key challenges in this domain, including interpreting complex map visuals, performing spatial and temporal reasoning, and managing multi-step decision-making tasks.

Artificial Intelligence (AI) is revolutionizing the field of Magnetic Resonance Imaging (MRI) by enabling faster, more accurate, and cost-effective image reconstruction. This project explores cutting-edge AI methodologies, focusing on combining data-driven approaches with physics-informed models to tackle challenges in MRI reconstruction. By integrating MRI acquisition physics directly into neural networks, we aim to improve the interpretability and robustness of reconstruction techniques.