Want to know more about our team of researchers?

Dr. Quang Vinh Nguyen – Group Leader

Research Topics: Visual Analytics, Information Visualisation and Visual-Information Communication and Interaction.

Brief: his main research areas are in the fields of Visual Analytics and Information Visualisation, including Medical Data Analysis, Graph and Network Analysis, Graph Drawing, Applications with Visualisation and Visual Analytics, Visual Collaborative System, Human Computer Interaction, and related research areas. His research is now focusing on theoretical and domain specific research on Visual Analytics, Information Visualisation and Virtual and Augmented Reality based on the synergy of both human’s visual strength and automated analysis, particularly for large scale-relational data and network, medical and genomics data analysis and related applications.

Prof. Simeon Simoff – Honorable Member

Research Topics: Artificial intelligence, Data mining/analytics and knowledge discovery, Human computer interactions, Virtual worlds and immersive environments, Visual computing, and Visual data mining and analytics.

Brief: Professor Simeon is currently the Dean of School of Computing and Mathematics at Western Sydney University. Simeon was previously a Professor of Information Technology at the University of Technology, Sydney. He is a founding Director and a Fellow of the Institute of Analytics Professionals of Australia. Currently he is an editor of the ACS Conferences in Research and Practice in Information Technology (CRPIT) series in Computer Science and ICT. Prior to that he was the associate editor (Australia) of the American Society of Civil Engineering (ASCE) Journal of Computing in Civil Engineering.

Alumni Research Students (in Alphabetical Order of Surname)

Jesse Tran – PhD Candidate

Research Topic(s): Design Principles for Managing Cognitive Overload in Interactive Analysis of Corpus Data with Visualisation

Brief: Jesse graduated his PhD with a background in data visualisation system in a cross disciplinary research between Computer Science and Linguistics. The methodology adapted in his research is based on design science. His methods include web-based technologies, artificial intelligence (in particularly Bayesian Theorem), and Human Computer Interaction techniques. He is a member of the Centre of Excellence for the Dynamics of Language where he spends most of his time interviewing linguistic researchers and working with them to create new visualisation tools. In his free time, he enjoys creating pop beats and hip hop beats which he puts on his music website.

Research Students (in Alphabetical Order of Surname)

Mohammad ALKHALAILEH – PhD Candidate

Research Topic(s): QoS-Based Data-Intensive Hybrid Mobile Cloud Computing Framework.

Brief: Mobile cloud computing describes the usage of wireless communication to connect and locate information. The temporal and spatial movement flexibility, which is named “device mobility”, allows mobile applications to access data centres and processing sites from anywhere. Mobility can benefit mobile application in different ways: it improves the information accessibility, integrates technologies with information systems, and improves management effectiveness. Mobile computing encounters a set of limitations to work on data-intensive computing environment including insufficient bandwidth, low power, and low compotation and storage capabilities. Cloud computing can provide computation, storage, and communications resources as services in a scalable and virtualized manner. The integration between mobile computing and cloud computing is known by Mobile Cloud Computing (MCC). The computation offloading is the main concept of MCC, where a program code execution is performed in external resources via code offloading or task delegation techniques. Many architectures have adopted the code offloading technique. This has limitation as it is machine-dependent, and the code needs to be modified to match the computing environment. Other MCC architectures propose web services to enable single point of execution. However, not all tasks can be executed in the cloud as some require mobile information such as sensing and Bluetooth. In addition, data-intensive mobile applications involve participation of a huge number of mobile applications generating data requests that need to be managed in a way to minimize communication cost and increase responsiveness. For that, my research proposes a new hybrid MCC model that relies on a combination of code offloading and task augmentation over web services. that utilizes a decision and optimization algorithm to select the best computation optimization decision. In summary, my thesis aims to build a hybrid mobile cloud computing (MCC) model for data-intensive applications that optimizes three QoS parameters in the context of MCC, which are responsiveness, mobile device energy, and monetary cost for communication and computation.

Andrew Brunker – PhD Candidate

Research Topic(s): Visualisation of Genomic Data, Big Data, Immersive Visualisation.

Brief: Andrew is currently working on a 2D, and 3D immersive visualisation frame works, in order to compare the ability for researchers and clinicians to analyse data sets of varying sizes and complexity to discover more information about the cohort and also individual patients, in order to improve their decision making for personalising the medical treatment for a given patient. Currently Andrew is awaiting his candidature review. He is using facilities at both Western Sydney University and UTS to under take his research. Andrew also has extensive knowledge in web development as he is a front ent engineer for a digital advertising company and has extensive knowledge and practice in a wide array of web technologies and languages.

Jason Chu – PhD Candidate

Research Topic(s): Enabling Detection of DDoS Attacks with Visualisation.

Brief: Over the past decade, cyber attackers are compromising the weak network systems to launch the Distributed Denial of Service (DDoS) attacks against e-commerce businesses (Holme, Kim, Yoon, & Han, 2002; Lu & Li, 2016), etc. These attack results in cripple down their services to legitimate users and causes massive financial losses. Many solutions have been purported to combat against these DDoS attacks, but there is no perfect way to solve this challenging problem till date. Most of the existing solutions have been validated using experiments based on simulation, but recently, the researchers have started using publicly available real data sets for the validation of DDoS research (Seo & Lee, 2016). One of the solutions to monitor networks in an efficient and effective way is to utilise visualisation technology. The visualisation tool can assist the network administrator in protecting the system against abnormal activities via visual alerts. Through visual tracking and monitoring systems, DDoS attacks will be detected and analysed. The aim of this research is not only to study visualisation methods to analyse information on network security focusing on DDoS attacks but also to develop visualisation software prototype in an easy way to digest manner. There will be three main processes in this research project. The first step explores the theory, methodology and state-of-the-art studies in the visualisation area of the DDoS attacks on vulnerable networks. This insight will help show the current trends in this field and theory of the state of the visual art, which is enabling a better understanding of the theory of visualisation applied to corporate network security. It allows users to both a critical review of the research in the region and also is a better view of the development of mechanisms for the network security. The second step is to develop new visualisation methods for visualising data of DDoS attacks to a vast network, deriving from the superior knowledge. And the last step will evaluate the effectiveness of the developing visualisation methods.

Nader Khalifa – PhD Candidate

Research Topic(s): Unlocking The Complexity of Genomics and Biomedical Data with Visual Analytics.

Brief: His thesis will present a novel and new visual analytics that have not been studied for interactively presenting complex biomedical and genomic data. Technically, it will develop a new visualisation tool with a 3D graphic engine. Currently, the project discusses how Unity3D, a cross platform game engine and 3D graphics, can build an interactive tool with new analysis methods and functionality. By visualising and interacting large data sets with overview, drill-down and reverse manner based on selected features, it can potentially support the users in predicting and making fast comparisons among patients’ medical profiles.

Olu Okunade – PhD Candidate

Research Topic(s): Managing Technology and Innovation in the Life Science Industry: Exploring the benefits of virtual office using today’s technology and addressing related risk factors.

Brief: Managing Technology and Innovation in the Life Science Industry: Exploring the benefits of virtual office using today’s technology and addressing related risk factors.

Jolin Qu – Master of Philosophy

Research Topic(s): Using Machine Learning to Support Better and Intelligent Visualisation for Genomic Data.

Brief: The main goal of her research is to develop an intelligent and interactive visualisation prototype which combined with Machine Learning algorithms for medical data analysis and show them on different screens including mobile devices. It aims to illustrate the complex genomics data from childhood cancers in meaningful and dynamic ways to guide the effective treatment decisions in the cohort. The purpose is to intelligently provide most effective visualisations to the current patients’ medical data on PC and portable screens. Machine Learning algorithms are used during visualizing the cancer genomic data in order to provide highly accurate predictions.  This research will open a new and exciting path to discovery for disease diagnostics and therapies.