Honours and Masters project

This project examines how films produced in Asian markets perform in terms of commercial success and critical recognition using real-world industry data. Students will compile a dataset of films from regions such as Hong Kong, China, South Korea, and Southeast Asia, drawing on publicly available sources to analyse indicators such as production budget, box office revenue, streaming platform release, and awards. Using quantitative data analysis methods, the project aims to identify patterns and factors associated with successful film outcomes.

Project Description

Teamwork is a big part of university life, but not all teams work smoothly. Students often face issues such as uneven contributions, unclear communication, or members falling behind. Teaching staff receive a large amount of peer feedback. But the information is often dispersed across multiple reports and can be time-consuming to interpret—particularly in large cohorts. A system that could automatically identify which teams are struggling, and why, would allow educators to offer timely, targeted support.

Multiple master thesis/honour projects on MLLM/ human understanding are available.

Benefits: we aim for publication at top conferences and journals. You will have chance for full PhD scholarship. For those working hard, paid RA opportunities will also be provided.

requirement: WAM>80 and high self-motivation 

This is one of our CSIRO Next Generation AI Graduates Honors projects:

https://www.monash.edu/it/ssc/raise/projects 

Note: You Must be a Domestic Student i.e. Australian or New Zealand Citizen or Australian Permanent Resident  

Project Description

With the widespread adoption of cross-chain bridges and decentralized swapping protocols, an increasing number of money laundering activities leverage multi-chain hopping to obscure the origin and flow of illicit funds. However, most existing public blockchain anti-money laundering datasets focus on transactions within a single blockchain, lacking a systematic characterization of cross-chain fund flows. This limitation significantly constrains the analysis of cross-chain money laundering behaviors and the development and evaluation of related detection methods.

Inclusive learning environments are essential for ensuring that all students can fully participate and succeed. Neurodivergent learners—including those with ADHD, autism, dyslexia, and other cognitive differences—often navigate university settings that were not designed with their learning profiles in mind. Challenges related to feedback interpretation, cognitive load, communication, and assessment design can create barriers that impact learning, confidence, and wellbeing.

The research project aims to build:

- An Energy Market Simulator for Future Energy Systems (Smart Grid). 

Future energy systems are envisioned to be running decentrally with full automatic control, high proportion of renewable energy (e.g., wind & solar), and abundant storage facilities. With many types of renewable energy sources are weather and climate dependent, accurate simulators with good visualization and data analytic capabilities are needed for operators to control the grid.

We are seeking a motivated student to contribute to the development of a digital risk-stratification tool that predicts short-term seizure recurrence in emergency care. This project involves working with free-text ambulance and emergency department clinical notes to extract prognostic indicators and support the fine-tuning of Large Language Models (LLMs) for medical risk prediction. The student will gain hands-on experience in data preprocessing, model development, and evaluation using real-world clinical datasets from one of Australia’s leading epilepsy centres.

Autoregressive moving average (ARMA) models remain a competitive tool for forecasting low signal-to-noise ratio time series, due to their flexibility, low complexity and physical plausibility. They predict the next observation in a time series as a linear combination of a number of previous observations as well as a number of hidden (latent) random innovations. The AutoARIMA package remains a staple benchmarking tool against which forecasting techniques must be compared.

This project seeks to explore and trial new map morphing representations for seeing river water quality data sets more effectively over time and space. 

We are particularly focusing on the Melbourne and the region of Victoria, but expect the visualisation to be applicable to any geographical region.