Find Academic Supervisors

• Cybersecurity
• Software Engineering
• Artificial Intelligence
• +3 more

Ruitao Feng is a Lecturer in IT (Cybersecurity) at the Faculty of Science and Engineering, Southern Cross University. He holds an adjunct role as a Research Fellow at Nanyang Technological University (Singapore). His expertise spans cybersecurity, software engineering, and AI-driven security solutions. He earned his Ph.D. in Computer Science from NTU (2016–2021) and a Bachelor’s degree from Tianjin University (2010–2014). His research focuses on security and quality assurance in software systems, particularly leveraging AI4Sec & SE for intrusion detection, malware analysis, and vulnerability detection. He actively collaborates with students and researchers to advance these fields. He teaches courses like DATA2001 Database Systems and INFO6002 Cybersecurity Essentials . Ruitao is recruiting self-motivated Ph.D. candidates with strong programming skills and seeks honors/minor thesis students from SCU. His work has been published in top-tier conferences/journals (CORE A/A*, CCF A) in computer security and software engineering.

Rosetta RomanoView profile

Assistant Professor

• Indigenisation of Education
• Cybersecurity Standards and Frameworks
• Ontology Development
• +4 more

Rosetta Romano is an Assistant Professor in the Department of Information Systems at the University of Canberra. Her research focuses on indigenization of IT curricula, cybersecurity frameworks for small businesses, and ontology-driven solutions for regulatory alignment. She has led projects such as 'Indigenisation of Curriculum Design for STEM' and 'Cybersecurity Standards for Indigenous Businesses', emphasizing cultural pedagogy and technology ethics. Her work integrates Aboriginal Torres Strait Islander knowledge systems into IT education, co-designing curricula with Indigenous communities. She also develops knowledge graphs to simplify cybersecurity education for SMEs, addressing legislative complexity through ontology methodologies. Collaborations include government engagement frameworks and blockchain applications in social justice contexts. Romano’s projects have received funding for initiatives like the Indigenous Business Cybersecurity Standards Ontology and the 8 Ways Framework curriculum evaluation. Her research outputs span 20+ peer-reviewed articles, focusing on topics such as mobile salesforce automation, citizen-government co-creation, and blockchain’s role in combating tech-facilitated abuse.

Guanfeng LiuView profile

Professor

• Recommender Systems
• Privacy-Preserving Technologies
• Graph Neural Networks
• +5 more

Guanfeng Liu is the Vice Dean at the NNU-MQ Joint Institute, Macquarie University, within the Faculty of Science and Engineering. His research focuses on Recommender Systems, Privacy-Preserving Technologies, Graph Neural Networks, and Data Analytics. He leads projects like the 'Intelligent Health Data Analytics Platform' and 'Data-intensive Scheduling Optimisation in Database Systems.' His work emphasizes ethical AI, with contributions to trust prediction, spatio-temporal data analysis, and privacy-aware algorithms. In 2023, he won the Faculty of Science and Engineering Award for Inter-School Collaboration. Liu has published extensively in top journals like IEEE Transactions and ACM Conferences, with over 200 publications and a h-index of 34. Key projects include scalable document intelligence platforms and confidentiality preservation in graph learning. His research bridges theoretical advancements with practical applications in smart cities and healthcare, leveraging collaborative filtering and contrastive learning techniques.

Dong WenView profile

Lecturer

• Big Graph Analytics
• Distributed Algorithms
• Graph Neural Networks
• +5 more

Dong Wen is a Lecturer and DECRA Research Fellow at the School of Computer Science and Engineering, University of New South Wales, Australia . Previously, he served as a Postdoctoral Research Fellow at the University of Technology Sydney. His research focuses on big graph analytics, distributed algorithms, and graph neural networks , with applications in real-world scenarios through industry collaborations. Research Interests: Dong Wen specializes in data management, graph processing, and real-time analytics. His work intersects computer science with civil engineering through multifunctional cement-based sensors and energy-efficient materials, as evidenced by his recent publications on smart infrastructure and sustainable composites. Scientific Contributions: He has secured the prestigious DECRA fellowship and published extensively on energy-autonomous structural monitoring systems and waste-valorized materials. His research team actively recruits MPhil PhD students with scholarship opportunities available.

Qing WangView profile

Associate Professor

• Graph Machine Learning
• Data Management
• Data Analytics
• +3 more

Dr. Qing Wang is an Associate Professor at the Australian National University (ANU) since 2012, leading the Graph Research Lab and the Database Group within the School of Computing. Her research focuses on graph machine learning, data management, and algorithms for dynamic networks. With over a decade of industry experience in data analysis and management, she holds a PhD in Computer Science (Summa Cum Laude) from Christian-Albrechts-University Kiel, Germany. Education: PhD (Dr.rer.nat.) in Computer Science, Christian-Albrechts-University Kiel, Germany (2010) Master of Information Systems (First Class Honours), Massey University, New Zealand (2009) Master of Economics, Jinan University, China Bachelor of Engineering, South China University of Technology, China Research Interests: Dr. Wang explores cutting-edge techniques in graph machine learning, scalable algorithms for large-scale data, and knowledge tracing. Her work bridges theoretical foundations with practical applications, such as improving graph neural networks' expressivity and optimizing distance queries on dynamic road networks. She actively contributes to the development of tools for data integration and privacy-preserving analytics. Grants & Awards: ARC Discovery Project (2021–2023): 'Deep Learning for Graph Isomorphism' ARC Discovery Project (2016–2018): 'Creating the Social Genome' ANU Dean’s Award for Teaching Excellence (2015) Fellow of the Higher Education Academy (2015) Labs & Teams: She leads the Graph Research Lab , focusing on advancing graph algorithms and their applications in real-world systems. Her team collaborates on projects involving dynamic data integration, privacy-preserving techniques, and educational data analytics.

Lianhua ChiView profile

Lecturer

• Big Data Analytics
• Artificial Intelligence in Healthcare
• Machine Learning
• +7 more

Lianhua Chi is a Lecturer in the Department of Computer Science and Information Technology at La Trobe University, Melbourne, Australia, since February 2018. He holds a dual PhD in Machine Learning and Data Mining from the University of Technology Sydney (Australia) and Huazhong University of Science and Technology (China). Before joining academia, he worked as a Postdoctoral Researcher at IBM Research Australia on Watson Education and Health, and as a Data Specialist at the University of New South Wales (UNSW). His research focuses on big data analytics, particularly in healthcare applications, AI-driven Autism Screening, and hashing techniques for real-time data processing. Chi has received notable awards including the 'Young Global Changer' title from G20/T20 Germany, 'Top 200 Young Researchers Globally' by the Heidelberg Laureate Forum, and the IBM External Honors award. Education: PhD in Machine Learning and Data Mining (2015), University of Technology Sydney & Huazhong University of Science and Technology Previous roles: Postdoc at IBM Research Australia, Visiting Researcher at UTS Research Interests: Chi’s work emphasizes adaptive hashing algorithms, graph stream classification, geolocation prediction in social media, and time series analysis. He applies these techniques to healthcare challenges such as AI-based Autism Screening and medical imaging diagnostics. His methodologies prioritize scalability and real-time adaptability. Awards & Grants: Best Paper Award (PAKDD13) Romberg Grant (Heidelberg Laureate Forum, 2016) Innovation Connection Grant (NostraData, 2022–2023) Advising & Collaborations: Chi has collaborated with Harvard Medical School’s Gehlenborg Lab and led projects on healthcare AI and cybersecurity. His work integrates advanced hashing techniques with clinical data to improve diagnostic accuracy and patient care efficiency. Labs/Teams: Active in interdisciplinary teams focusing on AI ethics, healthcare analytics, and big data security at La Trobe University.

Charini NanayakkaraView profile

Research Fellow

• Record Linkage
• Data Mining
• Privacy-Preserving Techniques
• +3 more

Charini Nanayakkara is a Research Fellow at the Australian National University (ANU) School of Computing, where her work is partially funded by the University of Edinburgh for contributions to the Scottish Historic Population Platform (SHiPP) project. She earned her PhD in Computer Science from ANU in 2022, focusing on record linkage techniques for complex population data. Her prior roles include Senior Research Officer (2021–2022), Tutor (2019–2022), and software engineering experience at WSO2 and Google Summer of Code. Education: PhD in Computer Science (ANU, 2022) BSc (Hons) Computer Science (University of Colombo, Sri Lanka, 2016) Research Interests: Record linkage for population datasets Data mining and privacy-preserving techniques Applications in healthcare, historical data, and federated learning Key Projects: "Novel methods of data integration and federated learning" (ANU, 2024–2025) "Scottish Historical Population Platform (SHiPP)" (ANU/Edinburgh, 2022–2026) Advising & Grants: Registered to supervise research students Funded by ANU and Edinburgh University Labs/Teams: Collaborates with ANU’s data science and health informatics teams on interdisciplinary projects.

Gavin HuttleyView profile

Professor

• Genomics
• Molecular Evolution
• Phylogenetics
• +3 more

Professor Gavin Huttley is a leading academic in the Research School of Biology at the Australian National University (ANU), specializing in genome decryption and molecular evolution. He holds a B.Sc (Hons I) from Macquarie University and a PhD in Molecular Population Genetics from the University of California, Riverside. His research focuses on identifying genomic regions influencing disease susceptibility through genetic variation analysis, leveraging computational tools and phylogenetic methods. Notable contributions include developing the IQ-TREE phylogenetic software and advancing microbiome research via QIIME 2. He has supervised numerous projects in genomics, bioinformatics, and evolutionary biology, with over 60 publications and significant citations. Awards include the Howard Florey Young Investigator award. His work spans diverse areas such as mutation rate quantification, DNA methylation in invertebrates, and machine learning for mutagenesis classification. Active collaborations include projects on microbial communities, genome defense mechanisms, and epigenetic regulation. He leads grants totaling millions, emphasizing reproducible data science and scalable bioinformatics tools. Education: B.Sc (Hons I), Macquarie University PhD in Molecular Population Genetics, University of California, Riverside Research interests include phylogenetic analysis, computational genomics, and evolutionary processes. Projects highlight innovative tools like GraphBin-Tk for metagenomic binning and diverse-seq for sequence clustering. His lab contributes to understanding genomic evolution and disease mechanisms through interdisciplinary approaches.

• Cybersecurity
• Cloud Security
• Applied Cryptography
• +2 more

John Le is a Lecturer in Cyber Security at the School of Computing and Information Technology, University of Wollongong, within the Faculty of Engineering and Information Sciences. He obtained his PhD in Cybersecurity from the University of Technology Sydney (2020) and researches cloud security, applied cryptography, and machine learning applications in cybersecurity. He is a member of the Institute of Cybersecurity and Cryptology (iC2). Primary research areas include: Blockchain security and smart contract vulnerabilities Cloud infrastructure protection Adversarial machine learning Cybersecurity education innovations Recent publications focus on AI-enhanced cybersecurity education tools, blockchain oracles, and adversarial attack detection. His methodological work spans graph mining algorithms and lattice-based cryptography, with applications to network security automation. Notable funded projects: ARC Linkage Project: Blockchain-Based Quantum Safe for Secure Digital Medical Passport (2025-2027) Telstra-UOW Hub: LLMSecOps Framework for Pre-Employment Systems (2024-2025)

• Digital Health
• Artificial Intelligence
• Generative AI (GenAI)
• +8 more

Associate Professor Georg Grossmann is affiliated with the School of IT and Mathematical Sciences at the University of South Australia (UniSA), within the Advanced Computing Research Centre. He holds roles as the Research Degree Coordinator for Maths & Stats and Systems Engineering at UniSA STEM, and co-directs the Digital Health Innovation and Clinical Informatics (DHICI) Lab. His research focuses on Digital Health, Artificial Intelligence (AI), Industry 4.0, and interoperability solutions for complex systems. He is also a member of the AI & Software Engineering Lab in the Industrial AI Research Centre, addressing challenges in data integration and business process modeling. Research Interests: Georg's work spans process and data interoperability, AI-driven systems, generative AI (GenAI), and digital transformation. He explores applications in healthcare (e.g., clinical decision support systems), industrial IoT (IIoT), and cultural heritage management. His methodologies include multi-level modeling, knowledge graphs, and semantic frameworks to enhance system interoperability and decision-making. Labs & Collaborations: He leads the DHICI Lab and collaborates with institutions like MIMOSA, Practera, and BAE Systems. His projects include frameworks for predictive maintenance in Industry 4.0, adaptive clinical decision systems, and NLP-based artifact management in museums. He has secured grants from the South Australian Premier’s Research and Industry Fund and the Australian Government’s Department of Industry. Teaching & Supervision: As a Research Degree Supervisor, he guides students in areas like enterprise modeling, software engineering, and AI applications. He also contributes to courses on Business Process Management and Software & Data Modeling.

Michael BewongView profile

Senior Lecturer

• Data Science
• Artificial Intelligence
• Cyber Security
• +3 more

Dr. Michael Bewong is a Senior Lecturer in Computing at Charles Sturt University, specializing in Data Science, AI, and Cyber Security. He holds a PhD and BSc (Hons) from the University of South Australia. His roles include teaching database-related subjects and conducting research in data analytics and cyber security. Previously, he worked as a Research Fellow at UniSA, collaborating with Data to Decisions CRC on projects like 'Beat the News' (predictive data mining) and 'Predicting Cyber Security Exploits' (machine learning for vulnerability analysis). Education: PhD in Computing, University of South Australia BSc (Hons) in Computing, University of South Australia Research Interests: Focuses on applying data analytics and machine learning to solve real-world problems in cyber security, agriculture, and health. His work integrates AI-driven solutions for cyber threat detection, privacy-preserving data publication, and smart farming systems. Current projects include analyzing UAV threats to agriculture and developing frameworks for false information detection on social media. Articles Trends: Recent publications emphasize cyber security (e.g., threat hunting, exploit prediction), AI ethics (fairness algorithms), and precision agriculture (UAV threats, data-driven farming). Collaborations span interdisciplinary teams addressing challenges in graph data imputation, federated learning, and privacy techniques like LDP. Awards: Holds 12 prizes (specific names not listed in provided texts). Advising & Grants: No explicit student advisees listed. Collaborations include grants from Data to Decisions CRC and Food Agility Cooperative Centre. Labs/Teams: Affiliated with Data Science and Engineering Research Unit, Cyber Security Research Group (CSRG), and Advanced Network Research Group (ANRG) at Charles Sturt University.

• Computational Geometry
• Data Structures
• Approximation Algorithms
• +1 more

Joachim Gudmundsson is Professor and Head of School of Computer Science at the University of Sydney, holding SOAR and ARC Future Fellowships. His research develops algorithms for computational geometry problems including spatio-temporal analysis, graph spanners, geometric approximation, and trajectory mining. Key research domains: Spatio-temporal pattern analysis Graph spanners and approximation algorithms Trajectory clustering and map matching Geometric network optimization Multi-robot coordination algorithms Recent publications demonstrate strong focus on trajectory analysis algorithms under Fréchet distance, geometric graph decompositions, and pattern formation in multi-robot systems. Work increasingly addresses dynamic and real-world graph structures. Recognized with prestigious fellowships: SOAR Fellow (Sydney Research Accelerator) ARC Future Fellow (Australian Research Council)

Melinda HodkiewiczView profile

Professor

• Maintenance Engineering
• Asset Management
• Data Science
• +6 more

Professor Melinda Hodkiewicz is a leading academic at the University of Western Australia (UWA), affiliated with the School of Engineering's Mechanical Engineering department. Her work focuses on advancing maintenance, asset management, and safety practices through data-driven approaches, particularly in Technical Language Processing (TLP) and industrial ontologies. She has held significant roles such as the BHP Fellow for Engineering for Remote Operations (2015–2022) and leadership in the $8.8M Australian Government-funded Training Centre for Transforming Maintenance through Data Science. Research interests include applying TLP to maintenance and safety texts, ontology development for industrial interoperability, and advancing standards like ISO 23726. Awards include the MESA Medal (Lifetime Achievement in Asset Management) and Fellow of the Australian Academy of Technology and Engineering (ATSE). She has contributed to international standards (ISO 55000 series) and served on boards of organizations like NOPSEMA and MRIWA. Her work aligns with UN SDG 4 (Quality Education) and SDG 9 (Industry, Innovation & Infrastructure). Key contributions include developing the Industrial Ontology Foundry's Maintenance Working Group, creating open-source tools like OpenBoltRF for wind turbine analysis, and advancing semantic quality assurance frameworks for maintenance procedures. Her research bridges technical language processing with industrial applications, enhancing safety and operational efficiency in sectors like mining, energy, and offshore engineering. Education: BA(Hons) in Metallurgy (University of Oxford, 1985), PhD in Engineering (UWA, 2014) Grants: ARC Training Centre for Transforming Maintenance (2019–2025), ARC Hub for Offshore Floating Facilities (2014–2021) Advisory Roles: Standards Australia EL-069 (Automation Systems), ISO TC184/SC4 (Industrial Data)

Khanh LuongView profile

Research Fellow

• Machine Learning
• Data Mining
• Multi-Aspect Data Clustering
• +7 more

Dr. Khanh Luong is a Research Fellow at the School of Communication within the Faculty of Creative Industries, Education & Social Justice at Queensland University of Technology (QUT). She holds a PhD in Computer Science specializing in Data Science (2019) from QUT. Her research focuses on multi-aspect data, addressing challenges in high-dimensional, sparse datasets through innovative machine learning and data mining methods. She contributes to real-world applications in diverse domains like text, image, and bioinformatics data. Teaching roles include tutoring in Discrete Mathematics, Programming Principles, and Database Management for undergraduate/postgraduate students. She also designed the online unit 'Introduction to Programming' for the Master of IT program. Research interests span machine learning, data mining, multi-aspect data clustering, dimensionality reduction, and unsupervised learning. She works on the Applied Data Science project at QUT's Centre for Data Science, developing solutions for complex industry datasets. Publications emphasize multi-view clustering, manifold learning, and domain adaptation. Collaborations include roles in organizing the 2021 AusDM conference and reviewing for IEEE journals.

Aamir CheemaView profile

Associate Professor

• Urban Computing
• Sustainable Transportation Systems
• Smart Buildings
• +5 more

Aamir Cheema is an Associate Professor and Director of Education at the Department of Software Systems & Cybersecurity at Monash University. He co-directs the Urban Computing Lab and has held significant roles such as proceedings chair for ICDE 2019 and DASFAA 2015. His research focuses on data management, cloud/edge computing, and sustainable urban systems, with projects addressing electric vehicle integration, spatiotemporal data analytics for road safety, and smart building technologies. Education: PhD in Computer Science & Engineering (UNSW, 2011), Master of Engineering (UNSW, 2007), Bachelor of Science in Electrical Engineering (University of Engineering and Technology Lahore, 2005). Research interests include urban computing applications for sustainable transportation, smart buildings, and digital twins. Notable projects involve using big spatiotemporal data to reduce emissions, bidirectional EV charging systems, and indoor data management for emergency evacuations. Recipient of major awards including the ARC Future Fellowship (2018) and Victorian Young Tall Poppy Science Award (2019). Active in academic service roles such as Associate Editor of IEEE TKDE and conference organization (ADC 2015-2016 co-chair). Current projects include Electric vehicles for residential energy optimization (2025-2027) GenAI-driven ICT education reform (2025) Blockchain for peer-to-peer energy trading (2024-2025) Spatiotemporal data-driven road safety improvements (2023-2026)