Honours and Minor Thesis projects

This opportunity is tailored for master’s students who eager to engage in academic research under the mentorship of research-active academics. This project provides technical and research guidance from your supervisory team.

Project description

Electricity is an essential part of modern life and the economy. Driven by a combination of policy support and rapidly falling costs of low-carbon technologies, Australia is experiencing a sharp rise in the deployment of distributed energy sources (DERs). Typical DERs include wind, solar photovoltaics (PV), battery storage, and electric vehicles (EVs) on the consumer side.

Thanks to the widespread deployment of smart meters, high volumes of residential load data have been collected and made available to both consumers and utility companies. Smart meter data open up tremendous opportunities, and various analytical techniques have been developed to analyse smart meter data using machine learning. This project will provide a new angle toward energy data analytics and aims to discover the consumption patterns, lifestyle, and behavioural changes of consumers.

The rapid growth of electric vehicles (EVs) is transforming the transportation systems worldwide. Both EV fleets and private EVs are emerging as a cleaner and more sustainable component of urban mobility, forming an effective way to solve environmental problems and reduce commute costs in future smart cities. Due to the complex spatiotemporal behaviors of passengers and their travel patterns, the unmanaged electric charging demand from EVs may significantly impact the existing transportation and electrical power infrastructure.

Propositional satisfiability (SAT) is a well-known example of NP-complete problems. Although NP-completeness may be perceived as a drawback, it allows one to solve all the other problems in NP by reducing them to SAT and relying on the power of modern SAT solvers. This is confirmed by a wealth of successful examples of use cases for modern SAT solving, including generalisations and extensions of SAT as well as a wide variety of practical applications in artificial intelligence (AI).

Online fraud, also referred to as cyberscams, is increasingly becoming a cybersecurity problem that technical cybersecurity specialists are unable to effectively detect. Given the difficulty in the automatic detection of scams, the onus is often pushed back to humans to detect. Gamification and awareness campaigns are regularly researched and implemented in workplaces to prevent people from being tricked by scams, which may lead to identity theft or conning individuals out of money.

Using Adobe Photoshop to generate or edit images is old news, we don't need to edit using our hand and mouse, nor even a stylus pen.  All we need to do is command the gen AI to do it.

Right now the AI literature is trending with many techniques that enable to generate or edit realistic pictures by simply describing them: let there be pictures.

Gen AI has taken the world by storm, it's been applied to many disciplines including in pure sciences.  Notably, Google Deepmind used their graph deep learning based generative AI model (GNoME) to discover millions of new materials, as well as their AlphaFold to predict the structure & interactions of all of life’s molecules.

The energy transition to net zero is in full swing! We at Monash University's Faculty of Information Technology (FIT) are in the unique position that we support the transition across an immensely broad range of topics: from model-predictive building control and community battery integration to wind farm optimisation and multi-decade investment planning, we support clever algorithms and data crunching to make decisions automatically and to let humans make informed decisions, too. 

Genetic improvement (GI) of software is a family of techniques that can automatically improve code using evolutionary algorithms. The idea is to apply changes (swap lines/blocks, change + to -, etc.) to existing code until it is improved. This has been successfully deployed for automated bug fixing, speeding up existing programs, and making software more energy efficient, with impressive results.