Honours and Masters project

Ensuring construction quality and safety requires timely detection of defects, yet traditional manual inspections are slow, costly, and inconsistent. This research presents a computer vision-driven solution that automates defect detection using images captured by drones and site cameras. By applying advanced deep learning models, the system are expected to identify cracks, corrosion, and surface irregularities with high accuracy, even under challenging site conditions.

Foundation models have shown strong reasoning abilities, but their reasoning process is often implicit, difficult to inspect, and not always reliable. This project explores neuro-symbolic reasoning as a way to make AI reasoning more structured, interpretable, and robust.

The project will study how neural models can be combined with explicit logic-based reasoning. A neural model may be used to understand or decompose a problem, while a symbolic reasoning component performs structured inference using logic, rules, programs, or other formal representations.

Large Language Models (LLMs) are increasingly used to automatically evaluate other AI systems in tasks such as writing, reasoning, and question answering. This approach called LLM-as-a-Judge is now widely used in research benchmarks and AI development pipelines.

Evaluation benchmarks are a foundational component of artificial intelligence (AI) research, providing standardized ways to measure and compare the capabilities of AI systems. Benchmarks such as MMLU, GSM8K, HumanEval, and HellaSwag have been instrumental in tracking progress in large language models and related systems. However, benchmark usefulness is not static.

Our research explores novel map representations and projections.

This project seeks to design and trial new map representations for seeing Australian population data sets in new and ideally more effective ways. 

Why is this needed? 

Large Language Models (LLMs) such as GPT, Llama, Qwen, and Mistral are increasingly used in commercial and academic applications. As more models become available, identifying which model generated a particular response becomes important for copyright auditing, model verification, and AI transparency.

Current fingerprinting methods often rely on manually selected benchmark questions. However, manually designing discriminative questions is time-consuming and may not capture unique behavioral differences between models.

Videos contain rich information about actions, events, interactions, and changes over time. While recent AI models have made strong progress in video understanding, reasoning over complex video content remains challenging, especially when the task requires understanding temporal context or connecting information across different moments.

Digital identity systems are increasingly used to access online services in areas such as healthcare, banking, education, and e-government. While these systems improve convenience and accessibility, they also raise significant security and privacy concerns, including identity theft, unauthorized data disclosure, user tracking, and large-scale data breaches.

Embodied intelligence represents a rapidly emerging paradigm in robotics where intelligent behaviour arises through the integration of perception, decision-making, action, and interaction within a physical agent operating in the real world. Advances in autonomous robotics, computer vision, sensor fusion, and human-robot interaction have enabled mobile robotic systems to perform increasingly sophisticated tasks in dynamic environments.

Despite an increase in the usage of AI models in various domains, the reasoning behind the decisions of complex models may remain unclear to the end-user. Understanding why a model entails specific conclusions is crucial in many domains. A natural example of this need for explainability can be drawn from the use of a medical diagnostic system, where it combines patient history, symptoms and test results in a sophisticated way, estimate the probability that a patient has cancer, and give probabilistic prognoses for different treatment options.