Honours and Masters project

Which route is the best to drive from Monash University (Clayton campus) to Melbourne CBD? 

For many of us, answering this question would likely mean opening a route natvigation app and asking the provider to give us the fastest route. For some of us, this question might not need to be answered as you may already be experienced to drive from Monash Uni to CBD, or simply find that the route computed by the app is insufficent to handle your specific requirements, preferences, or constraints. 

What is a mixture model?

You may have learned about mixture models in a machine learning or statistics course. A mixture model with K component densities is defined by

The sum of the product of the mixture weights and component densities is guaranteed to be nonnegative and integrates to one, meaning it is a valid probability density.

Verifiable Dynamic Searchable Symmetric Encryption (VDSSE) enables users to securely outsource databases (document sets) to cloud servers and perform searches and updates. The verifiability property prevents users from accepting incorrect search results returned by a malicious server. However, the community currently only focuses on preventing malicious behavior from the server but ignores incorrect updates from the client, which are very likely to happen in multi-user settings. Indeed most existing VDSSE schemes are not sufficient to tolerate incorrect updates from users. For instance,…

Since the 1990s, researchers have known that commonly-used public-key cryptosystems (such as RSA and Diffie-Hellman systems) could be potentially broken using efficient algorithms running on a special type of computer based on the principles of quantum mechanics, known as a quantum computer. Due to significant recent advances in quantum computing technology, this threat may become a practical reality in the coming years. To mitigate against this threat, new `quantum-safe’ (a.k.a.

The last several years have witnessed the promising growth of AI-empowered techniques in mobile devices, from the camera to smart assistants. Users can find traces of AI in almost every aspect of mobile devices.

Word sense disambiguation (WSD), the process of computationally identifying the appropriate meaning of a word within its context, is a fundamental task in Natural Language Processing (NLP). Effective WSD is crucial for building accurate machine translation systems, information retrieval tools, and sentiment analysis applications, especially when dealing with diverse languages and linguistic variations.

This project focuses on simulating the organic growth of criminal communication networks by leveraging techniques such as Reinforcement Learning and Graph Theory. The goal is to curate a synthetic dataset that models the evolving structure and dynamics of illegal networks, taking into account factors like social connections, communication patterns, and resource allocation. By using graph-based models, the project aims to create realistic representations of how criminal groups form, expand, and operate under various conditions.

Quantum algorithms such as Grover’s Search promise quadratic speed-ups over classical search but are sensitive to hardware noise. This project will use Qiskit Aer to model realistic noise channels (decoherence, gate and readout errors) and evaluate their impact on algorithmic performance. By varying circuit depth, qubit count, and noise parameters, the student will identify conditions under which quantum advantage remains achievable and investigate possible error-mitigation strategies.

Network visualisation (or 'graph drawing') algorithms allow us to see connections and patterns in a network clearly. There are a large number of such algorithms that depict ('lay out') networks 'nicely' - according to a set of well-established criteria. These criteria apply to neat diagrams - with clear straight (nor neatly bent or curved) lines ('edges').

This project will investigate how networks that have been sketched (with curved/ wavy lines) can be adapted so that they, too, can be assessed by typical graph layout criteria.

In SmartScaleSys (S3), we aim to design and build resource management solutions to learn from usage patterns, predict future needs, and allocate resources to minimize latency, energy consumption, and costs of running diverse applications in large-scale distributed systems. This project offers researchers and students a chance to explore cutting-edge concepts in AI-driven infrastructure management, distributed computing, and energy-aware computing, preparing them for impactful roles in industry and research.