Honours and Masters project

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.

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.

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.

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

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 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.

Organisations continuously face cyberattacks that unfold over multiple stages, often generating vast volumes of intrusion alerts. While modern intrusion detection systems can flag suspicious activities, they typically produce fragmented and low-level alerts that make it difficult for security analysts to understand the overall attack progression and attacker strategies. Manual analysis of these alerts is time-consuming and does not scale to fast-evolving network environments.

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.

Communication networks show interaction between people over time, and are key to the identification of criminal networks and criminal activity. This project will investigate how future events might be able to be predicted, based on dynamic graphs representing prior interpersonal communications. The project will consider (a) how Graph Neural Networks can best be used for this machine learning task; (b) how visualisation techniques can best depict both known-past and predicted-future events.

As cities face unprecedented growth, the need for tools that can integrate diverse knowledge sources ranging from geospatial data to the nuanced intuition of urban planners is critical. This research will explore how Bayesian Networks can be adapted to serve as configurable, transparent models that empower decision-makers to weigh alternatives involving complex factors such as development yield, urban zoning, locality to services and infrastructure capacity.