Answering real-world complex queries, such as complex product search, often requires accurate retrieval from semi-structured knowledge bases that involve blend of unstructured (e.g., textual descriptions of products) and structured (e.g., entity relations of products) information.
Honours and Masters project
Displaying 1 - 10 of 244 honours projects.
Sign Language Segmentation
Computational modelling of sign languages aims to develop technologies that can serve as means for understanding (i.e., recognising) and producing (i.e., generating) a particular sign language. The objective of this project is to study and contribute to the state-of-the-art in sign language segmentation.
This is a research project best suited for students who are independent and willing to take up challenges. It is also a good practice for students who wish to pursue further study at a postgraduate level.
Sign Language Recognition
Computational modelling of sign languages aims to develop technologies that can serve as means for understanding (i.e., recognising) and producing (i.e., generating) a particular sign language. The objective of this project is to study and contribute to the state-of-the-art in sign language recognition.
This is a research project best suited for students who are independent and willing to take up challenges. It is also a good practice for students who wish to pursue further study at a postgraduate level.
Sign Language Generation
Computational modelling of sign languages aims to develop technologies that can serve as means for understanding (i.e., recognising) and producing (i.e., generating) a particular sign language. The objective of this project is to study and contribute to the state-of-the-art in sign language generation.
This is a research project best suited for students who are independent and willing to take up challenges. It is also a good practice for students who wish to pursue further study at a postgraduate level.
Nonverbal Behaviour Recognition
Recognising conversational nonverbal behaviour for speakers and listeners, such as hand gestures, facial expressions, and eye-gaze, is of great importance for natural interaction with intelligent agents. The objective of this project is to study and contribute to the state-of-the-art in conversational nonverbal behaviour recognition.
This is a research project best suited for students who are independent and willing to take up challenges. It is also a good practice for students who wish to pursue further study at a postgraduate level.
Nonverbal Behaviour Generation
Generating conversational nonverbal behaviour for speakers and listeners, such as hand gestures, facial expressions, and eye-gaze, is of great importance for natural interaction with intelligent agents. The objective of this project is to study and contribute to the state-of-the-art in conversational nonverbal behaviour generation.
This is a research project best suited for students who are independent and willing to take up challenges. It is also a good practice for students who wish to pursue further study at a postgraduate level.
MentalTAC: Mental Health Triage App for Clinician
Mental health is an ongoing issue in Australia. The cause of mental health can be due to a variety of reasons: workplace culture, high workloads, job insecurity, disparity in pay, lack of career advancement opportunities and turnover intentions. Mental healthcare workers are not able to cope with it and are suffering from burnout. There is a need to ease mental healthcare workers' workload and provide consistent patient triage with the help of technology. The project aim is to investigate the existing approaches and tools in facilitating mental health workers to perform efficient patient care…
Using AI-Based Smart Glasses to assist People with Low Vision
Smart glasses that combine mixed reality head-mounted displays with computer vision and natural language understanding, such as the Apple VisionPro or Google XR Glass, have the potential to revolutionise the lives of people with low vision by providing access to information about their environment through augmented vision and audio.
Towards Trustworthy Medical Diagnosis via Causal Machine Learning and Graph Neural Networks (Malaysia)
Modern clinical decision-making is constrained by associative models that conflate correlation with causation and overlook interactions among patient factors. This project introduces a unified framework that fuses causal inference with graph neural networks to deliver interpretable, high-precision diagnosis. Using electronic health records, Double Machine Learning isolates causal drivers (e.g., treatment effects, biomarkers) from spurious associations while adjusting for confounders such as socioeconomic status.
A Multi-Agent Web System for Context-Aware Discovery
We will design a multi-agent web agent system (powered by LLMs) capable of understanding natural language user preferences, decomposing complex queries into sub-tasks, and dynamically interacting with heterogeneous online tools and databases (e.g., real estate listings, school ranking data, maps). The goal is to generate recommendations that meet users’ multi-objective constraints. Inspired by recent agentic approaches, this project will explore how autonomous web agents can coordinate and perform sequential web-based operations to solve real-world decision-making problems.