Find Academic Supervisors

• Cyber Intelligence Analysis
• Cybersecurity
• Data Security
• +4 more

Dr. Geoffrey Skinner is a Senior Lecturer at the University of Newcastle's School of Information and Physical Sciences, specialising in Data Science and Statistics. He holds a PhD from Curtin University and a Bachelor of Engineering (Computer Engineering) from the University of Newcastle, complemented by a Graduate Certificate in Tertiary Teaching. His research expertise centers on Cyber Intelligence Analysis, with a focus on cybersecurity, data privacy, and eHealth technologies. He has contributed to over 100 publications and led multiple grants, including the SMS4dads initiative addressing mental health support for fathers. His work integrates technical, legal, and social dimensions to address privacy and security challenges in digital ecosystems. Notable projects include developing frameworks for trust management, secure authentication systems, and eHealth applications targeting youth and workforce health. His interdisciplinary approach bridges computer science, education, and healthcare, emphasizing practical solutions for contemporary cybersecurity and data protection challenges. Education: PhD, Curtin University Bachelor of Engineering (Computer Eng)(Honours), University of Newcastle Graduate Certificate Practice of Tertiary Teaching, University of Newcastle Research Interests: Cyber Intelligence Analysis Data Security & Privacy eHealth Innovations Trust and Reputation Management Grants: SMS4RRDads (2021-2025): $3.1M to support rural fathers' mental health. Be Positive Be Healthe: eHealth weight loss program for young women.

Kevin LeoView profile

Research Fellow

• Constraint Programming
• Modelling
• Presolving
• +3 more

Kevin Leo is a Research Fellow in the Department of Data Science & AI at Monash University. His work contributes to the United Nations Sustainable Development Goals (SDGs), though specific goals are not detailed in the text. **Research Interests**: Constraint Programming Modelling and Presolving Techniques Operations Research Programming Languages Algorithm Design for Optimization Applications in Medical Science and Autonomous Systems His recent research emphasizes core-guided reformulation, human-centred feasibility restoration, and globalization of constraint models. These advancements address both theoretical challenges and real-world applications such as UAV autonomy and medical treatment planning. Collaborations with international researchers are noted, though specifics are not elaborated. Kevin has no recorded part-time status or scientific awards. His advising and grants sections remain empty, with no listed students or active grants. His contributions focus on peer-reviewed publications in artificial intelligence and constraint satisfaction domains.

• Deep Learning
• Machine Learning for Biological and Medical Data
• Artificial Intelligence
• +4 more

Dr Benjamin Mashford is a Research Fellow at the John Curtin School of Medical Research, Australian National University (ANU), specializing in interdisciplinary applications of deep learning, machine learning, and nanoelectronics to biomedical challenges. His work bridges computational methods with immunology, infectious diseases, and advanced diagnostics. Affiliation: Division of Immunology and Infectious Diseases, ANU Research Group: The Enders Group - Models of Human Primary Immunodeficiencies Research Focus: Mashford's work spans: Deep learning for biological data analysis (flow cytometry, EEG, genomic sequences) Neuromorphic and low-power computing systems Quantum dot technology for optoelectronics Energy-efficient health-monitoring devices Medical signal processing and predictive analytics Publication Trends: Recent work emphasizes hyper-dimensional cytometry representations, automated phenotyping systems, and cross-domain applications of machine learning from immunology to mineral processing. He also explores hardware implementations for biomedical diagnostics, including neurostimulation and fall detection systems. Supervision: Registered as a supervisor for graduate research students.

• Statistical Inference
• Confidence Intervals
• Model Selection
• +5 more

Dr. Paul Kabaila is an Associate Professor of Statistics at La Trobe University's Mathematics & Statistics school. He holds a PhD from the University of Newcastle and prior degrees from the University of New South Wales. His research focuses on statistical inference methodologies, particularly confidence intervals affected by preliminary model selection, frequentist methods utilizing uncertain prior information, and model-averaged confidence intervals. He has supervised numerous PhD students across diverse fields including biostatistics, bioinformatics, and business analytics. Dr. Kabaila has held visiting appointments at prestigious institutions including Oxford University and the Australian National University. He secured an ARC Discovery Grant (2002–2005) advancing statistical analysis of count data, with applications in epidemiology and finance. He led La Trobe University's first Statistics Program accreditation by the Statistical Society of Australia. His recent work emphasizes high-dimensional data challenges in statistical/machine learning and post-shrinkage strategies. He received the 2020 Distinguished Author Award from the Journal of Time Series Analysis for sustained contributions. His teaching spans all levels of statistical education, including advanced subjects like Statistical Inference and Theory of Statistics .

• Data Privacy
• Evolutionary Computing
• Recommendation Systems
• +5 more

Dr. Jinli Cao is a full-time Associate Professor in the Department of Computer Science and Information Technology at La Trobe University. She holds a BSc from Hebei University, China, and a PhD from the University of Southern Queensland, Australia (1997). Her research focuses on evolutionary computing, data privacy, deep learning for vulnerability assessment, and decision support systems. She has published over 150 papers in top venues such as VLDB and IEEE Transactions series. Dr. Cao leads an ARC-funded project on software vulnerability risk discovery and has secured three ARC grants. She has supervised 11 PhD, 2 Master’s, and 57 Honours students, many of whom work in academia and industries like Oracle and Commonwealth Bank. Teaching contributions include developing courses in databases, data warehouses, and artificial intelligence. Research Interests: Privacy-preserving data publishing and optimization Evolutionary algorithms for dynamic data partitioning Deep learning applications in cybersecurity and healthcare Graph-based machine learning for access control and anomaly detection Decision support systems and top-k query processing Her recent articles explore cutting-edge topics like privacy-preserving spatial crowdsourcing tasks, graph neural networks for traffic prediction, and cybersecurity frameworks for vulnerability prioritization. Awards include competitive ARC grants totaling $450,000 (2023-2025). She actively serves as an Associate Editor for Health Information Science and Systems and has examined over 100 PhD theses across Australian universities. Teaching highlights: Developed 20+ courses including Database Management Systems, Data Warehousing, and Artificial Intelligence. Coordinates units like Decision Support Systems and Intermediate Programming in Java.

Gavin SuddreyView profile

Researcher

• Artificial Intelligence
• Robotics
• Human-Robot Interaction
• +3 more

Dr Gavin Suddrey is a Researcher at the Queensland University of Technology (QUT) Centre for Robotics, specializing in Artificial Intelligence and Electrical Engineering. His research focuses on human-robot interaction, natural language instruction for robots, and the development of autonomous systems. He holds a Bachelor of Information Technology (Honours) from QUT. Key research interests include social robotics, wellbeing applications via robotic interventions, natural language processing for task execution, and vision-based industrial robotics solutions. His work emphasizes practical applications such as automated laboratory tours, healthcare robotics, and industrial automation. Dr Suddrey has contributed to over a dozen publications since 2014, exploring topics ranging from path algebra for mobile robots to robot-delivered wellbeing training. His projects include the Mini Autonomous Vehicles initiative and competitive robotics entries like Team ACRV's OCRTOC participation. He develops software systems for human-robot interaction, including HTML/JavaScript-based interfaces on Pepper robots, and investigates user perceptions of robots through scales like the Robot Self-Efficacy measure. While no specific awards are listed, his work aligns with QUT's commitment to interdisciplinary robotics innovation.

• Bone Mechanics
• Osteoporosis
• Human Motion
• +5 more

Saulo Martelli is an Associate Professor in Biomedical Engineering and Biomechanics at Queensland University of Technology's School of Mech., Medical & Process Engineering. His research focuses on musculoskeletal biomechanics, particularly quantifying the relationship between anatomical structure, physical activity, and musculoskeletal function in health and disease. He has pioneered computational methods for studying neuromuscular control, bone fracture risk, and exercise prescription in osteoporosis, while advancing experimental techniques for bone microstructure analysis and real-time loading assessment. Teaching includes courses such as Finite-Element Modelling, Advanced Bio-Mechanics in Orthopedics, Sports Biomechanics, and Dynamics. His work aligns with UN Sustainable Development Goals related to health and innovation. He actively contributes to the medical devices industry through research optimizing joint replacements and virtual trials of orthopaedic devices. Research interests span bone mechanics, osteoporosis, human motion modeling, and joint mechanics. Key areas include musculoskeletal modeling validation, bone adaptation to mechanical loading, and non-invasive imaging techniques for clinical applications. Recent work emphasizes algorithm development for strain estimation and computational tools for personalized medicine. Publications highlight advancements in shoulder joint stability modeling, cortical bone adaptation, and friction dynamics. His work integrates biomechanical and computational approaches to address clinical challenges in orthopaedics and sports science. Advising includes supervision of Raissa Katembwe (2016). Collaborations focus on software development for clinical imaging integration and interdisciplinary projects with engineering and medical communities.

• Computer Science
• Information Security
• Wireless Sensor Networks
• +5 more

Dr. Jarrod Trevathan is a researcher at James Cook University, with a career spanning computer science, environmental monitoring, and information security. His work focuses on wireless sensor networks, virtual research environments, and online auction systems. He has collaborated extensively with colleagues like Trina Myers and Wayne Read. Research Highlights: Developed auction-based protocols for sensor network resource allocation Created middleware platforms for heterogeneous sensor integration Built Web2.0 disaster response systems (Riskr) Contributed to semantic enablement of virtual research environments Explored behavioral aspects of online auction fraud Teaching Innovations: Applied process-oriented guided inquiry learning to large IT courses Integrated active learning techniques for online ICT education

Guillaume PirotView profile

Research Fellow

• Subsurface Characterization
• Spatial Statistics
• Bayesian Inference
• +4 more

Guillaume Pirot is a Research Fellow at the School of Earth and Oceans, University of Western Australia. He specializes in subsurface characterization, spatial statistics, and Bayesian inference, with a focus on uncertainty quantification in geological models. His work integrates geophysical data inversion and computational methods to address challenges in hydrogeology and resource exploration. Currently, he co-coordinates Work Package 5 of the Loop project, focusing on uncertainty management in multi-disciplinary modeling frameworks. Education: PhD in Hydrogeology (University of Neuchâtel, 2015), focusing on stochastic heterogeneity modeling of braided river aquifers. Prior experience includes 7 years as an IT consultant in retail before transitioning to academia. Research interests span 3D geological modeling, spatial statistics, Bayesian inversion, and the rigorous treatment of uncertainty across data acquisition to predictive modeling. His contributions include open-source tools like dh2loop and Blockworlds for geological data processing and uncertainty quantification. Scientific awards include the MinEx CRC 2022-2023 Publication Prize and Prix Adrien Guebhard-Severine (2015). He collaborates extensively on projects involving geophysical integration, probabilistic inversion, and multi-disciplinary modeling platforms like Loop. Advising and grants: Co-supervises PhD students within the Loop project framework. Involved in grants such as the ARC-funded Loop 3D Linkage-2 project (2023-2026). His work emphasizes translational research bridging computational methods with practical geological exploration challenges. Labs/Teams: Active contributor to the Loop project team, developing tools for uncertainty-aware geological modeling. Collaborates with interdisciplinary groups in geophysics, hydrology, and computational science.

• Artificial Intelligence in Medical Imaging
• Neuroimaging Analysis
• Deep Learning Applications in Healthcare
• +3 more

Dongang Wang is a Research Fellow at the Brain and Mind Centre, University of Sydney since 2019, holding a PhD in Medicine and Health (2024) specializing in Neuroimaging Analysis using Artificial Intelligence. He earned a Master of Science in Electrical Engineering from Columbia University (2015) and a BEng. His work focuses on integrating AI into clinical neuroimaging workflows, particularly addressing challenges like limited annotations, data heterogeneity, and federated learning in multi-center studies. He contributes to FDA/TGA-approved medical devices through software like VeriScout and iQ-solutions, collaborating with academic and industrial partners. Research interests include AI-driven clinical validation of MRI/CT tools, lesion segmentation in multiple sclerosis, and improving algorithmic explainability. He has conducted projects under CRC-P and MRFF grants, emphasizing translational research and real-world clinical applicability. He is affiliated with the Sydney Neuroimaging Analysis Centre and maintains an active Google Scholar profile. His publications span 2018–2025, focusing on federated learning frameworks, deep learning applications in neuroimaging, and clinical validation of AI tools. No awards are explicitly listed, though his work reflects significant contributions to medical AI adoption.

• Cybersecurity
• Privacy-Preserving Machine Learning
• Cryptography
• +5 more

Dr. Maggie Liu is a Lecturer in Cybersecurity and Software Systems at RMIT University's School of Computing Technologies. Her research focuses on secure multi-party computation and its applications in privacy-preserving machine learning (PPML), with specific emphasis on practical deployments for medical diagnostics and mobile image classification. Key research areas include: Designing lightweight cryptographic protocols Secure computation techniques Machine learning systems security Interdisciplinary privacy-preserving solutions Her recent work involves PPML for medical services, mobile-edge computing, and collaborative learning systems. Notable projects include MediSC, CryptMed, OblivGNN, Sonic, EncSIM, and Leia. Maggie Liu earned her Ph.D. in Computer Science from RMIT University.

• Biomedical Engineering
• Paediatrics and Reproductive Medicine
• Computation Theory and Mathematics
• +3 more

Priya Rani is a Lecturer in the School of Engineering at RMIT University. Her research focuses on interdisciplinary areas including Biomedical Engineering, Artificial Intelligence, and Neurosciences. She actively supervises research projects such as developing peripheral electronics for neural recording systems and applying machine learning for clinical delirium diagnosis. Dr. Rani’s work bridges computational methods with healthcare applications, emphasizing robust machine learning models and medical imaging analysis. She is affiliated with RMIT’s City Campus and contributes to advancing technologies for sustainable and intelligent systems. Research Interests: Her primary areas include computational theory, biomedical engineering, and AI-driven solutions for healthcare challenges. Notable projects involve semantic segmentation using attention-augmented networks and damage monitoring in advanced materials. Dr. Rani’s research trends highlight machine learning robustness, medical image analysis, and smart home technologies for aging populations. Supervision Focus: Neural recording systems, clinical AI applications Key Projects: Cortical neural interfaces, delirium prediction algorithms Technical Expertise: Machine learning, image processing, medical diagnostics Collaboration and Impact: Her work addresses real-world challenges through interdisciplinary approaches, with publications spanning medical IoT, nanosensors, and composite materials analysis. Dr. Rani’s contributions emphasize practical applications of AI and computational methods in healthcare and engineering domains.

• Ecology
• Curriculum and Pedagogy
• Electrical Engineering
• +4 more

Dr. Jessica Rivera Villicana is a Research Fellow in Computational and Geospatial Data Science at RMIT University's School of Global, Urban and Social Studies (GUSS). Her work integrates interdisciplinary approaches across ecology, education technology, and biomedical engineering. She leads projects like liveability scorecards analyzing urban inequities in Australian local government areas (LGAs), using geospatial and machine learning methodologies. Her research also focuses on EEG-based seizure detection, contributing to both technical advancements and clinical professional insights. Collaborating with global teams, she explores AI applications in healthcare (e.g., caregiver support systems) and cybersecurity (e.g., MLGuard framework). Key research areas include: Urban policy analysis through geospatial data modeling Machine learning interpretability in medical diagnostics Embodied conversational agents for caregiving AI safety and security in critical systems Behavioral modeling in interactive narratives Her recent work highlights include: 2025 Burwood & Canada Bay LGA liveability reports 2023 seizure detection survey with medical professionals MLGuard cybersecurity tool for ML models Dr. Villicana holds an ORCID identifier (0000-0003-1955-3310) and is based at RMIT's City Campus in Melbourne, Australia.

• Service-Oriented Computing
• Edge Intelligence
• Blockchain
• +6 more

Dr. Hai Dong is a Senior Lecturer at the School of Computing Technologies, RMIT University, Melbourne, Australia. He leads the Smart Sensing and Services Research Area and directs the GreenCryptoLab, a joint laboratory with CloudTech. He chairs the IEEE Task Force on Deep Edge Intelligence and has held roles including Research Fellow at Curtin University and RMIT. Education: PhD (Curtin University), BEng (Northeastern University, China), Graduate Certificate in Learning & Teaching (Distinction) His research focuses on Edge Intelligence, Blockchain, AI Security, and Cyber Security. He has published 150+ articles in top venues like TIFS, ICML, and ICSOC, securing over $5M in research funding from ARC, CRC, and industry partners. Recent work emphasizes Green Cryptocurrency systems and federated learning applications. His awards include the 2023 RMIT Industry Engagement Award and Best Paper recognitions in ICSOC and IEEE ICBC. Supervision: Active in guiding PhD/Master students in AI Security, Edge Computing, and Blockchain. Grants: Major projects include Green Bitcoin platforms and secure crypto payment systems. Labs: GreenCryptoLab collaborates on sustainable blockchain tech and secure edge systems.

Li Ping ThongView profile

Associate Professor

• Virtual Reality
• Augmented Reality
• Serious Games
• +7 more

Associate Professor Li Ping Thong serves as the Associate Dean of Digital Design at RMIT University , leading the largest discipline within the School of Design . She oversees undergraduate and postgraduate programs in Games, Animation, and Digital Media, with 20 years of international teaching experience across Malaysia, Vietnam, and Australia. Current Academic Roles: Associate Dean (Digital Design), Associate Professor (Digital Design), Senior Lecturer (Digital Media) Research Focus: Immersive Technologies, VR/AR, Serious Games, User Experience Design, Cultural Heritage, Technology-Based Pedagogy Research Trends span applied digital media projects in education, health, architecture, and cultural heritage . Her work emphasizes cross-disciplinary collaboration with industry partners like the Australian Automobile Association and Foodbank Australia. External Research Funding includes over $1 million since 2017 , supporting initiatives in sustainability, cultural preservation, and community engagement through digital innovation.