Honours and Masters project

In EdgeVLMOpt (EVO): Optimizing Vision-Language Models for Resource-Constrained Edge Devices, we aim to develop efficient and scalable techniques to enable the deployment of advanced vision-language models (VLMs) on edge hardware. While VLMs have demonstrated strong capabilities in multimodal reasoning and understanding, their high computational and memory demands pose significant challenges for real-time, on-device applications.

In EdgeFusionAI (EFAI): Real-Time Multi-Sensor Multi-Modal Intelligence on Edge Devices, we aim to design and develop efficient techniques for fusing heterogeneous sensory data, including vision, LiDAR, radar, and other modalities, to enable robust and real-time decision-making on resource-constrained edge platforms. This project focuses on building intelligent systems capable of integrating diverse data sources while addressing the challenges of limited computation, memory, and energy availability at the edge.

Are you interested in applying your database knowledge to a medical domain? In this project, you will explore data curation, management, processing and analysis of medical data. You will explore various medical and patient datasets available publicly, such as the UK Biobank, Cancer Atlas, etc.

Are you interested in applying your AI knowledge to music, especially classical music? You will explore how AI (and Deep Learning) can be used in music, such as using Generative AI to create Theme & Variations in classical music, analysing classical music structures (e.g., sonata form, theme & variation form, etc.), and identifying instruments (e.g., violin vs. viola).

AI has been increasingly used in Medicine. There are big opportunities for AI in medical research, including medical imaging diagnosis. AI and Deep Learning have been used to detect and classify lesions in various diseases, such as cancers. 

Are you interested in applying your AI/DL knowledge to the medical domain? This project focuses on the use of AI in Medical Imaging (e.g., CT, MRI, X-ray, ultrasound). The work includes segmentation and classification; for example, segmenting tumour from medical images and then classifying the tumour grade. We will use various Deep Learning techniques, such as CNNs, and experiment with a variety of Deep Learning frameworks, such as U-Net and ResNet.

This project aims to develop a historical map of greater Melbourne, showing its yearly road development and residential land use from 1966 to the present. You will learn how to use a geospatial DBMS and a geospatial visualisation tool. 

Are you interested in biomedical? You could combine your data science and computing expertise to analyse DNA and genetics.

Are you interested in learning Quantum Computing from scratch? If yes, this project might be for you. Week by week, you will explore and learn the basics of quantum computing, focusing on data processing. You will answer questions such as what the limits of classical computing are, why new paradigms are needed, and where quantum computing may help.

Bundle recommendation systems enhance user experience and increase sales by recommending a set of items as a bundle rather than individual items [1].   The understanding of items in bundles is that they should be complementary some how.  In this project, we will explore the relationship between causal reasoning on items purchased and bundles.  Causal reasoning could be used to infer if two purchases are related, and, moreover, language language models can be used to assess the plausability of this, to help create an argument using the Bradford-Hill criteria.  Some recent research has…