Honours and Masters project

In the evolving landscape of data reporting, traditional text-based and quantitative methods are increasingly being supplemented by rich, community-generated qualitative data, including audio and video content. This shift presents unique challenges and opportunities in how non-profits, government bodies, and community organizations present and utilize this data.

Become part of the Monash Immersive Analytics Lab, and explore exciting new ways to visualise, interact, and analyse all types of data with VR and AR! We are looking for enthusiastic students to work on immersive visualisation using latest technology, such as head-mounted displays with integrated eye-trackers (Microsoft HoloLens and others), gesture recognition devices, and large wall displays.

Are you ready to dive into the future of education and revolutionise how software projects are assessed? Join this innovative project aimed at creating cutting-edge learning analytics capabilities within the Faculty of IT at Monash University. This project seeks to provide automated support for teaching staff in augmenting the marking of software development and design assignments, specifically software projects submitted to the FIT-based GIT lab platform, using advanced large language models (LLMs).

As the number of mental health patients increases, the demand for qualified counselors is on the rise. However, training/practice sessions with actual patients are often limited, let alone meeting a sufficient number of patients of different personalities. This project aims to use large language models to simulate therapy sessions under certain predefined circumstances. This project is co-supervised by a collaborator from the Psychology department in Jeffrey Cheah School of Medicine and Health Sciences.

Machine learning (ML) training and evaluation usually involve large-scale datasets and complicated computation. To process data efficiently, a promising solution is to outsource the processes to cloud platforms. However, traditional approaches of collecting users' data at cloud platforms are vulnerable to data breaches. Specifically, during the ML model training or inference service offering, the cloud server could learn the input data used to train the model, model structures, user queries and inference results, which may be sensitive to users or companies. 

This project will apply feature selection techniques for identifying features that can effectively predict the Logistics Performance Index (LPI), building upon our previously published work [1].

This project aims to analyse the comments of Twitter  on non-communicable diseases.  Students are expected to carry out Aspects Detection to identify the specific aspects discussed in the tweets e.g., causes, transmission and symptoms. Subsequently,  students are expected to conduct sentiment analysis utilizing tools like TextBlob or VADER, while also taking into account the importance of considering emojis to enhance classification accuracy. Students also need to provide solutions to the problem of ironic  tweets (e.g., seem positive but actually negative) and misinformation.

SCIPPlan is a mathematical optimisation based automated planner for domains with i) mixed (i.e., real and/or discrete valued) state and action spaces, ii) nonlinear state transitions that are functions of time, and iii) general reward functions. SCIPPlan iteratively i) finds violated constraints (i.e., zero-crossings) by simulating the state transitions, and ii) adds the violated constraints back to its underlying optimization model, until a valid plan is found. The purpose of this project is to improve the performance of SCIPPlan.

Cybersecurity researchers are contemplating how to best use the currently trending AI techniques to aid cybersecurity, beyond just for classification. 

The aim of this Honours project is to get the student to work with the supervisors on the latest AI techniques to adapt them over for cybersecurity, building first on baseline approaches for which code is available.

The student is free to discuss with the supervisor on any specific aspects of his/her choice and interest.

Cryptographic applications require a careful implementation to avoid side-channel attacks that reveal secret information to an attacker (e.g. via run-time measurements). In particular, for floating point arithmetic it is known that the timing of some basic arithmetic operations and functions on some CPUs depends on the input values [1], and thus the timing may leak secret information when the input contains secret values. Constant-time implementation tries to mitigate such run-time timing leakage on typical devices.