Potential PhD Projects

There are opportunities for talented researchers to join the School of Computer Science and Engineering, with projects in the following areas:

  • Artificial Intelligence
  • Biomedical Image Computing
  • Data and Knowledge Research Group
  • Embedded Systems
  • Networked Systems
  • Programming Languages & Compilers
  • Service Orientated Computing
  • Trustworthy Systems
  • Theoretical Computer Science

Artificial Intelligence

Supervisory team: Professor Claude Sammut 

Project Summary: Our rescue robot has sensors that can create 3D representations of their surroundings. In a rescue, it is helpful for the incident commander to have a graphical visualisation of the data so that he or she can reconstruct the disaster site. The School of Computer Science and Engineering and the Centre for Health Informatics have a display facility (VISLAB) that permits users to visualise data in three dimensions using stereo projection onto a large 'wedge' screen. 

This project can be approached in two stages. In the first stage, the data from the robot are collected off-line and programs are written to create a 3D reconstruction of the robot's surroundings to be viewed in the visualisation laboratory. In the second stage we have the robot transmit its sensor data to the VISLAB computers for display in real-time. 

This project requires a good knowledge of computer graphics and will also require the student to learn about sensors such as stereo cameras, laser range finders and other 3D imaging devices. Some knowledge of networking and compression techniques will be useful for the second stage of the project. 

A scholarship/stipend may be available. 

For more information contact: Prof. Claude Sammut

Biomedical Image Computing

Supervisory Team: Dr Yang Song

Project Summary: Various types of microscopy images are widely used in biological research to aid our understanding of human biology. Cellular and molecular morphologies give lots of information about the underlying biological processes. The ability to identify and describe the morphological information quantitative, objectively, and efficiently is critical. In this PhD project, we will investigate various computer vision, machine learning (especially deep learning), and statistical analysis methodologies to develop automated morphology analysis methods for microscopy images.

More research topics in Computer Vision and Biomedical Imaging can be found here.

A scholarship/stipend may be available.

For more information contact: Dr Yang Song

Supervisor Team: Professor Erik Meijering and Dr John Lock

Project Summary: Biologists use multiparametric microscopy to study the effects of drugs on human cells. This generates multichannel image data sets that are too voluminous for humans to analyse by eye and require computer vision methods to automate the data interpretation. The goal of this PhD project is to develop, implement, and test advanced computer vision and deep learning methods for this purpose to help accelerate the challenging process of drug discovery for new cancer therapies. This project is in collaboration with the School of Medical Sciences (SoMS) and will utilise a new and world-leading cell image data set capturing the effects of 114,400 novel drugs on the biological responses (phenotypes) of >25 million single cells.

A scholarship/stipend may be available.

For more information contact: erik.meijering@unsw.edu.aujohn.lock@unsw.edu.au

Supervisor Team: Professor Erik Meijering and Professor Arcot Sowmya

Project Summary: Current commercial 3D ultrasound systems for medical imaging studies often do not provide the ability to record volumes large enough to visualise entire organs. The first goal of this PhD project is to develop novel computational methods for fast and accurate image registration to digitally reconstruct whole organs from multiple ultrasound volumes. The second goal is to develop computer vision and deep learning methods for automated volumetric image segmentation and downstream statistical analysis. This project will be in collaboration with researchers from the UNSW School of Women’s and Children’s Health to improve monitoring organ development during pregnancy to support clinical diagnostics.

A scholarship/stipend may be available.

For more information contact: erik.meijering@unsw.edu.aua.sowmya@unsw.edu.au

Supervisor Team: Prof Arcot Sowmya, A/Prof Lois Holloway (Ingham Medical Research Institute, Liverpool Hospital)

Project Summary: Decisions on the most appropriate treatment for diseases such as colorectal cancer and diverticulitis can be complex. Advanced imaging such as MRI and CT can provide information on the location of the disease compared to other anatomy and also functional information on the disease and surrounding organs. There is also the potential to gain additional information from these images using techniques such as radiomics. At Liverpool hospital there is a database of previous patient histories, including outcome as well as imaging information which we can use utilise in collaboration with medical specialists. This project will use machine learning and deep learning approaches to determine anatomical and disease boundaries and combine them with clinical and response data to model treatment response and develop treatment decision support tools. The incoming PhD student should ideally have a computer science qualification with research skills and an interest to develop deep learning and decision support techniques in the medical imaging field. Research in this area are subject to ethics approvals and institutional agreements.

A scholarship/stipend may be available.

For more information contact: a.sowmya@unsw.edu.aulois.holloway@unsw.edu.au

Supervisor Team: Prof Arcot Sowmya, Prof Padmaja Sankaridurg (Brien Holden Vision Institute)

Project Summary: The prevalence of myopia (short sightedness) is expected to rise dramatically to affect nearly 50% of the global population by the year 2050. With the increase in myopia, the prevalence of high myopia, which is associated with greater risks of permanent ocular complications, is also expected to rise significantly and reach a global prevalence of 9.5% by the year 2050. Due to the increasing burden of the myopia epidemic, there is a need to identify children at risk of myopia and high-myopia so as to intervene early. A large amount of data is being collected for studying the incidence and progression of myopia in children, which has created an opportunity to analyse the multi-modal data using data mining, image analysis and deep learning techniques. The aim of the PhD project is to use the multimodal data sources and advanced data mining techniques to predict the onset and progression of myopia. The incoming PhD student should ideally have a computer science qualification with research skills and an interest to apply data mining and deep learning techniques in the ocular field. Research in this area are subject to ethics approvals and institutional agreements.

For more information contact: a.sowmya@unsw.edu.aup.sankaridurg@bhvi.org

Supervisor Team: Prof Arcot Sowmya and Dr Simone Reppermund

Depression and self-harm represent substantial public health burdens in the older population. Depression is ranked by the WHO as the single largest contributor to global disability and is a major contributor to suicide. This project will use large linked administrative health datasets to examine health profiles, service use patterns and risk factors for suicide in older people with depression. Given the vast amount of data included in linked datasets, new ways of analysing the data are necessary to capture all relevant data signals. This project will generate a sound epidemiological and service evidence base that informs our understanding of health profiles and service system pathways in older people with depression and risk factors for trajectories into suicide.

For more information contact: a.sowmya@unsw.edu.aus.reppermund@unsw.edu.au

Data & Knowledge Research Group

Supervisory team: Xuemin Lin, Wenjie Zhang 

Project Summary: Efficient Processing of Large Scale Multi-dimensional Graphs 

This project aims to develop novel approaches to process large scale graphs such as social networks, road networks, financial networks, protein interaction networks, etc. The project will focus on the three most representative types of problems against graphs, namely cohesive subgraph computation, frequent subgraph mining, and subgraph matching. The applications include anomaly detection, community search, fraud and crime detection.  

For more information contact: lxue@cse.unsw.edu.au or wenjie.zhang@cse.unsw.edu.au  

A scholarship/stipend may be available. 

Supervisory team: Wei Wang, Xin Cao 

Project Summary: The immense popularity of online social networks has resulted in a rich source of data useful for a wide range of applications such as marketing, advertisement, law enforcement, health, and national security, to name a few. Ability to effectively and efficiently search required information from the huge amounts of social network data is crucial for such applications. However, current search technology suffers from several limitations such as inability to provide geographically relevant results, inadequately handling uncertainty in data and failing to understand the data and queries resulting in inferior search experience. This project aims to develop a next-generation search system for social network data by addressing all these issues. 

A scholarship/stipend may be available. 

For more information contact: weiw@cse.unsw.edu.au or xin.cao@unsw.edu.au  

Embedded Systems

Supervisory team: Sri Parameswaran 

Project Summary: Reliability is becoming an essential part in embedded processor design due to the fact that they are used in safety critical applications and they need to deal with sensitive information. The first phase in the design of reliable embedded systems involves the identification of faults that could be manipulated into a reliability problem. A technique that is widely used for this identification process is called fault injection and analysis. The aim of this project is to develop a fault injection and detection engine at the hardware level for an embedded processor. 

A scholarship/stipend may be available. 

For more information contact: sridevan@unsw.edu.au 

Networked Systems and Security

Supervisory team: Sanjay Jha, Salil Kanhere 

Project Summary: This project aims to develop scalable and efficient one-to-many communication, i.e., broadcast and multicast, algorithms in the next generation of WMNs that have multi-rate multi-channel nodes. This is a significant leap compared with the current state of the art of routing in WMNs which is characterised by unicast in a single-rate single-channel environment. 

A scholarship/stipend may be available. 

For more information contact: sanjay.jha@unsw.edu.au

Supervisory team: Mahbub Hanssan 

Project Summary: A major focuses of the Swimnet project will be to look at a QoS framework for multi-radio multi-channel wireless mesh networks. We also plan to develop Traffic engineering methodologies for multi-radio multi-channel wireless mesh networks. Guarding against malicious users is of paramount significance in WMN. Some of the major threats include greedy behaviour exploiting the vulnerabilities of the MAC layer, location-based attacks, and lack of cooperation between the nodes. The project plans to look at a number of such security concerns, and design efficient protection mechanisms (Mesh Security Architecture). 

A scholarship/stipend may be available. 

For more information contact: mahbub.hanssan@unsw.edu.au  

Supervisory team: Wen Hu  

Project Summary: The mission of the SENSAR (Sensor Applications Research) group is to investigate the systems and networking challenges in realising sensor network applications. Wireless sensor networks are one of the first real-world examples of "pervasive computing", he notion that small, smart and cheap, sensing and computing devices will eventually permeate the environment. Though the technologies still in its early days, the range of potential applications is vast - track bush fires, microclimates and pests in vineyards, monitor the nesting habits of rare sea-birds, and control heating and ventilation systems, let businesses monitor and control their work spaces etc. 

A scholarship/stipend may be available. 

For more information contact: wen.hu@unsw.edu.au

Service Orientated Computing

Supervisory Team: Boualem Benatallah, Lina Yao, Fabio Casati

Project Summary: This project investigates the significant and challenging issues that underpin the effective integration of software-enabled services with cognitive and conversational interfaces. Our work builds upon advances in natural language processing, conversational AI and services composition.

We aim to advance the fundamental understanding of cognitive services engineering by developing new abstractions and techniques. We’re seeking to enable and semi-automate the augmentation of software and human services with crowdsourcing and generative model training methods, latent knowledge and interaction models. These models are essential for the mapping of potentially ambiguous natural language interactions between users and semi-structured artefacts (e.g., emails, PDF files), structured information (e.g., indexed data sets), apps and APIs.

For more information contact: b.benatallah@unsw.edu.au or lina.yao@unsw.edu.au

Supervisory Team: Lina Yao and Defence Science & Technology Group

Project Summary: This research is supported by the Defence Science and Technology Group. It aims to develop intelligent methodologies to capture the environment in sufficient fidelity to evaluate (model and predict) what application/system changes need to occur to fulfill the requirements (goals) of the mission.

A scholarship/stipend may be available.

For more information contact: lina.yao@unsw.edu.au

Supervisory Team: Lina Yao, Boualem Benatallah and Quan Z. Sheng

Project Summary: The overall goal of this project is to develop novel machine learning and deep learning techniques that can accurately monitor and analyse human activities. These techniques will monitor and analyse daily living on a real-time basis and provide users with relevant personalised recommendations, improving their lifestyle through relevant recommendations.

A scholarship/stipend may be available.

For more information contact: lina.yao@unsw.edu.au or b.benatallah@unsw.edu.au

Supervisory Team: Lina Yao

Project Summary: This project is supported by Office of Naval Research Global (US Department of Navy). The aim of this project is to develop a software package for resilient context-aware human intent prediction for human-machine cooperation.

A scholarship/stipend may be available.

For more information contact: lina.yao@unsw.edu.au

Supervisory Team: Lina Yao and Xiwei Xu

Project Summary: The research is supported by our collaborative research project with Data61. The aim is to develop an integrated end-to-end framework for fostering trust in Federated/Distributed AI systems.

A scholarship/stipend may be available.

For more information contact: lina.yao@unsw.edu.au or xiwei.xu@unsw.edu.au

Supervisory Team: Helen Paik

Project Summary: Micro-transactions stored in blockchain create transparent and traceable data and events, providing burgeoning industry disruptors an instrument for trust-less collaborations. However, the blockchain data and its’ models are highly diverse. To fully utilise its potential, a new technique to efficiently retrieve and analyse the data at scale is necessary.

This project addresses a significant gap in current research, producing a new data-oriented system architecture and data analytics framework optimised for online/offline data analysis across blockchain and associated systems. The outcome will strongly underpin blockchain data analytics at scale, fostering wider and effective adoption of blockchain applications.

A scholarship/stipend may be available.

For more information contact: h.paik@unsw.edu.au

Supervisory Team: Fethi Rabhi and Boualem Benatallah

Project Summary: All modern organisations use some form of analytics tools. Configuring, using and maintaining these tools can be very costly for an organisation. Analytics tools require expertise from a range of specialties, including business insight, state-of-the-art modelling approaches and tools such as AI and machine learning as well as efficient data management practices. A knowledge engineering approach can deliver flexible and custom data analytics applications that align with organisational objectives and existing IT infrastructures. This model uses existing resources and knowledge within the organisation. The project uses semantic-web based knowledge modelling techniques to build a comprehensive view related to an organisation’s analytics objectives while leveraging open knowledge and open data to expand its scope and reduce costs.

We aim to help organisations utilise and reuse public and organizational knowledge efficiently when conducting data analytics. Our work also involves the rapid development and deployment of analytics applications that suit emerging analytics needs, plugging new data and software on-demand using new approaches such as APIs and cloud services. The proposed techniques have already been piloted in the areas of house price prediction in collaboration with the NSW Government and portfolio management in collaboration with Ignition Wealth.

A scholarship/stipend may be available.

For more information contact: f.rabhi@unsw.edu.au or b.benatallah@unsw.edu.au

Theoretical Computer Science

Supervisory team: Ron van der Meyden 

Project Summary: The technology of cryptocurrency and its concepts can be broadly applicable to range of applications include financial services, legal automation, health informatics and international trade. These underlying ideas and the emerging infrastructure for these applications is known as ‘Distributed Ledger Technology’. 

A scholarship/stipend may be available. 

For more information contact: meyden@cse.unsw.edu.au  

Trustworthy Systems

seL4

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Supervisory team: Gernot Heiser, June Andronick 

Project Summary: seL4, the secure embedded L4 microkernel, is a key element of our research program. We developed seL4 to provide a reliable, secure, fast and verified foundation for building trustworthy systems. seL4 enforces security within componentised system architectures by ensuring isolation between trusted and untrusted system components, and by carefully controlling software access to hardware devices in the system. 

A scholarship/stipend may be available. 

For more information contact: gernot@unsw.edu.au or June.Andronick@data61.csiro.au 

Projects with Top Up Scholarship for Domestic Students

Supervisors:

Project description:

Previous studies have shown that cognitive training can effectively improve people's skillsets and emotional capabilities in cognitive deficits. Such training programs are known to enhance  the participants' brain health, and better prepare them for an independent life. However, the existing conventional technologies for such training are not scalable and lack personalized features to optimize the efficacy. In this project, we will develop a technology platform for automatically acquiring and processing multimodal training data. The project will be conducted in collaboration with Stronger Brains, a not-for-profit organization that provides cognitive training. We aim to develop a fully automated social and cognitive function assessment framework based on multimodal data. Such a framework is essential to establish a  system with less involvement of experts and increase its scalability. The project involves:

  1. Data collection.
  2. Developing multimodal predictive models for cognitive functions and affective states  in cognitive deficits.
  3. Developing adaptation techniques to personalize the framework.

Supervisors:

Project description:

The fields of Science, Technology, Engineering, and Math, otherwise known as STEM, play a key role in the sustained growth and stability of any economy and are a critical component  in shaping the future of our society. This project aims to develop new evidence-based guidelines for designing highly effective teaching simulations for a STEM subject that personalizes training to learner proficiency. In particular, we aim to design a novel AI- powered framework for dynamic adaptive learning in STEM educational technology to improve learning outcomes in an accessible and engaging environment. The potential contributions of the project involve:

  1. Developing a multimodal physio-behavioral AI for rapid assessment of proficiency level.
  2. Integration of affective state and cognitive load with proficiency level to form a comprehensive cognitive diagnosis and capture the interplay between affective and cognitive processes.
  3. Establishing dynamic adaptive learning in real-time based on the cognitive diagnosis that responds to the current individual needs of the learner.

Supervisor: Dr. Rahat Masood (rahat.masood@unsw.edu.au)

Supervisory Team: Prof. Salil Kanhere (CSE - UNSW), Suranga Seneviratne (USyd), Prof. Aruna Seneviratne (EE&T – UNSW)

Project description:

Children start using the Internet from a very early age for entertainment and educational purposes and continue to do so into their teen years and beyond. In addition to providing the required functionality, the online services also collect information about their users, track them, and provide content that may be inappropriate such as sexually explicit content; content that promotes hate and violence, and other content compromising users’ safety. Another major issue is that there is no established mechanism to detect the age of users on online platforms hence, leading children to sign up for services that are inappropriate for them. Through this research work, we aim to develop an age detection framework that can help detect children’s activities on online platforms using various behavioural biometrics such as swipes, keystrokes, and handwriting. The core of this project revolves around the ground-breaking idea that “User Touch Gestures” contain sufficient information to uniquely identify them, and the “Touch Behaviour” of a child is very different from that of an adult, hence leading to child detection on online  platforms. The success of this project will enable online service providers to detect the presence of children on their platforms and offer age-appropriate content accordingly.

Supervisors:

Project description:

Users unintentionally leave digital traces of their personal information, interests, and intents while using the online services, revealing sensitive information about them to online service providers. Though, some online services offer configurable privacy controls that limit access to user data. However, not all users are aware of these settings, and those who know might misconfigure these controls, due to the complexity or lack of clear instructions. The lack of privacy awareness combined with privacy breaches   on the web leads to distrust among the users in online services. Through this research study, we intend  to improve the trust of users on the web and mobile services, by designing and developing a user-centric privacy-preserving solutions that involves aspects of user privacy settings, user reactions and feedbacks on privacy alerts, user behavioral actions, and user psychology. The aforementioned factors will be first used in quantifying privacy risks and later used in designing privacy-preserving solutions. In essence, we aim to improve privacy in mobile and web platforms by investigating various human factors in: i) privacy risk quantification and assessment, and ii) privacy-preserving solutions.

 

Supervisors:

Project description:

Deep learning techniques have shown great success in many applications, such as computer vision and natural language processing. However, in many cases purely data-driven approaches would provide suboptimal results, especially when limited data are available for training the models. This dependency on large-scale training data is well understood as the main limitation of deep learning models. One way to mitigate this problem is to incorporate knowledge priors into the model, similarly to how humans reason with data; and there are various types of knowledge priors, such as data-specific relational information, knowledge graphs, logic rules and statistical modelling. In this PhD project, we will investigate novel methods that effectively integrate knowledge priors and commonsense reasoning with  deep learning models. Such models can be developed for a wide range of application domains, such as computer vision, social networks, biological discovery and human-robot interaction.

Supervisors:

Project description:

Deep learning models are typically considered a black-box, and the lack of explainability has become a major obstacle to deploy deep learning models to critical applications such as medicine and finance. Explainable AI has thus become an important topic in research and industry, especially in the deep learning era. Various methods for explaining deep learning models have been developed, and we are especially interested in explainability in graph neural networks, which is a new topic that has emerged very recently. Graph neural networks are becoming increasingly popular due to their inherent capability of representing graph structured data, yet their explainability is more challenging to explore with the irregular and dynamic nature of graphs. In this PhD project, we will investigate novel ways of modelling explainability in graph neural networks, and apply this to various applications, such as computer vision, biological studies, recommender systems and social network analysis.

Supervisors:

Project description:

Due to the graph’s strong expressive power, a host of researchers are turning to graph modeling to support real-world data analysis. Given the prevalence of graph structures with temporal information in user activities, temporal and dynamic graph   processing is an important and growing field of computer science. Driven by a wide spectrum of applications, such as recommendation and fraud detection in e-commerce, and malicious software detection in cybersecurity, this project aims to develop novel techniques for scalable and efficient temporal graph processing. The specific focus is to tame the challenges brought by the large volume, the high velocity, the complex structure of big temporal and dynamic graphs. The project will  lay theoretical foundations and deliver substantial outcomes including computing frameworks, novel indexes and incremental and approximate algorithms to process large-scale graphs.

Supervision team

Background

Most cyber threat intelligence platforms provide scores and metrics that are mainly derived from open-source and external sources. Organisations must then figure out if and how  the output is relevant to them.

Research problems

  • Dynamic Threat Risk/ Exposure Score

Continuous monitoring and calculation of an organisation’s ‘Threat Risk’ posture score  using a range of internal and external intelligence.

  • Customised/ targeted newsfeed

A curated cyber and threat newsfeed that is relevant to an organisation. The source of  the newsfeed will leverage the internal   and external analysis from the first question. The output will include information that helps a user understand and digest their organisation’s threat posture in a non-technical manner.

Proposed approaches

We propose to develop dynamic GNN models for discovering dynamic cyber threat intelligence from blended sources. GNN has achieved state-of-the-art performance in many high-impact applications, such as fraud detection, information retrieval, and recommender systems due to their powerful representation learning capabilities. We propose to develop new GNN models which can take blended intelligence sources into account in the threat intelligence prediction. Moreover, many GNN models are static that deal with fixed structures and parameters. Therefore, we propose to develop dynamic GNN models which can learn the evolution pattern or persistent pattern of dynamic graphs.