Scholarship funded PhD research projects
Featured research projects
The objective of this ARC-funded PhD position is to build the first scientific framework to provide such answers with quantifiable accuracy. The candidate will explore and extend various probabilistic and statistical frameworks. Practitioners should be able to leverage a rich statistical toolset to assess residual risk, to obtain statistical guarantees, and to analyze the cost-benefit trade-off for ongoing fuzzing campaigns. As a first starting point, the perspective of software testing as species discovery (STADS) provides access to a substantial biostatistical framework in ecology to tackle this fundamental challenge. A recent vision statement provides a large number of concrete opportunities for future research.
The PhD student will work on the development of highly efficient techniques for automated vulnerability detection in large software projects. The PhD student will investigate scalable fuzzing techniques, publish in premier venues for software engineering and cyber security, and develop practical test generation tools that can detect real-world vulnerabilities in existing widely-used, security-critical C libraries. We will explore deep integrations of heavy-weight, systematic, whitebox fuzzing techniques and light-weight, random, greybox fuzzing techniques. We will also study the efficient fuzzing of stateful, protocol-based applications as well as gui-based (Android) apps.
The project will explore how far we can push tracking in very crowded scenes using deep learning segmentation and classification combined with advanced optimisation methods.
The project will develop methods to use acoustic data for the identification of insect species in the wild and in controlled agricultural settings.
This project aims to generate a more complete picture of how environmental factors and stress influence division of labour in a colony and with this the limits of this flexibility. The project builds on evolutionary game theory to develop a new approach for analysing how environmental factors impact on DOL and colony viability. Our work combines mathematical models and computer simulations with biological experiments in the lab and field.
This project is a part of an exciting collaboration with Monash Tech School, Victorian Space, Science Education Centre, micro:bit, Tech Girls Movement, Hacker Exchange and Monash Generator to develop and nurture an entrepreneurial mindset in the next generation of women in the Internet of Things (IOT). We are trying to organize hundreds of teenage girls to learn the concepts of IOT by developing software programs using the hands-on micro:bit devices. This project will explore that how to enhance girls’ learning experience about STEM and how such learning experience can contribute to their future entrepreneurial potential.
This study centres on how computer-based decision procedures, under the broad umbrella of artificial intelligence (AI), can assist in improving health and health care. The goal of this project is to develop computational models using text and images (and possibly other data modalities) to help medical experts to improve the quality of patient care.
The proposed project aims to develop new methodologies for developing NMT systems between extremely low-resource languages and English. Recent advances in neural machine translation (NMT) are a significant step forward in machine translation capabilities. However, "NMT systems have a steeper learning curve with respect to the amount of training data, resulting in worse quality in low-resource settings". This project investigates methods to enable high performing NMT in low-resource scenarios.
This project aims to design effective and intelligent search techniques for large scale social network data. The project expects to advance existing social network search systems in three unique aspects: utilizing the geographical locations of queries and social network data to provide more relevant results; acknowledging and handling inherent uncertainties in the data; and exploiting knowledge graphs to produce intelligent search results.
In this project, we are interested in developing efficient query processing techniques for indoor location data considering textual keywords associated with objects, and data uncertainty.
This PhD project will explore novel methods for Nomadic Augmented Reality, a class of systems, applications and interaction techniques designed to support field workers with visual information on the go.
The project will investigate the mechanisms of self-organised task allocation in insect colonies.
This project will investigate new approaches to modelling, designing and generating mobile eHealth apps for phones and wearables.
This project will explore different deep learning techniques for Android app analyses, e.g., to detect Android malware, to identify common vulnerabilities, or to pinpoint repackaged Android apps.
This PhD project will investigate the creative use of digital and networking technologies that go beyond automating and augmenting aging and antiquated systems.
The dominant paradigm for interacting with computers now involves new media and multimodal input on cell phones, which involves speech, multi-touch, writing, gestures, images, gaze, and sensors.