Find Academic Supervisors

Son Tung NguyenView profile

Researcher

• Robotics
• Computer Vision
• Machine Learning
• +3 more

Son Tung Nguyen is a PhD Researcher at the Queensland University of Technology (QUT) Centre for Robotics (QCR) , affiliated with Prof. David Milford’s research group. His work focuses on advancing visual localization techniques, particularly in adapting these methods to low-resolution imaging scenarios. He holds a Master’s degree from the University of Stuttgart, where his thesis explored integrating modern computer vision with task and motion planners for robotic manipulation. Education: Master’s Degree: University of Stuttgart (Thesis: Robotic manipulation combining computer vision and task planning) PhD Candidate: Queensland University of Technology (QUT) Research Interests: Nguyen’s research spans robotics, computer vision, and machine learning. His current projects emphasize visual localization in challenging environments, such as those requiring low-resolution imaging, and the application of these techniques to augmented reality (AR) systems. He has also contributed to self-supervised learning frameworks for robotic sequential manipulation and representation learning in scene-image analysis. Advising & Collaborations: As part of Prof. Milford’s group, Nguyen contributes to projects at QCR. His research aligns with the Centre’s focus on advancing autonomous systems, robotics, and AI-driven solutions. While no personal grants or awards are explicitly listed, his work is embedded in the group’s broader achievements, such as recent grants and recognitions highlighted in QCR’s news. Labs & Teams: Nguyen is actively involved in the QCR research community, collaborating with experts in robotics and computer vision to bridge theoretical advancements with practical applications in autonomous systems.

Zahir TariView profile

Professor

• Computer Science
• Cybersecurity
• Cloud Computing
• +4 more

Prof. Zahir Tari is a leading international expert in Computer Science, currently serving as a Professor at the School of Computing Technologies at RMIT University. He holds a prominent role as Research Director of the Centre of Cyber Security Research and Innovation (CCSRI) and has been appointed to the ARC College of Experts (2022-2024). Research Interests : Prof. Tari specializes in designing innovative solutions for large-scale systems including Cloud, Edge, and IoT environments, with particular emphasis on cybersecurity, performance optimization, and reliability of critical systems like SCADA and Smart Grids. His work integrates mathematical models with computational approaches to address complex security and scalability challenges. Research Trends : Recent publications highlight his leadership in blockchain-based energy trading privacy, cross-domain access control systems, IoT security frameworks, and federated learning approaches for vulnerability detection. His work combines theoretical rigor with practical implementations in domains ranging from healthcare to military applications. Scientific Recognition : Member, ARC College of Experts (2022-2024) Supervision & Funding : Prof. Tari has successfully supervised 31 PhD students to completion and secured over AUD 21 million in research funding through prestigious grants including an ARC Research Hub, CRC-P, multiple ARC Discovery Projects, and industry partnerships with organizations like Siemens. His research has produced 279 publications with an h-index of 49 and over 10,800 citations.

• Complex Systems Theory
• Dynamical Systems Theory
• Complex Networks
• +7 more

Michael Small is a Professor and the CSIRO-UWA Chair of Complex Systems at the University of Western Australia (UWA), holding dual appointments with UWA’s Faculty of Engineering and Mathematical Sciences and CSIRO’s Mineral Resources division. As Director of the UWA Data Institute, he leads research in complex systems theory and dynamical systems, with applications to health, mining, and energy infrastructure. His academic roles include Deputy Editor-in-Chief of Chaos and Editor of Physica A . He holds a PhD in Applied Mathematics (UWA, 1998) and a BSc(Hons) in Pure Mathematics (UWA, 1994). His research focuses on mathematical modeling of complex systems, including disease transmission networks, mental health dynamics, and industrial process optimization. Notable projects include the Transforming Indigenous Mental Health & Wellbeing initiative and collaborations with mining giants like BHP and Rio Tinto. His awards include the V. Afraimovich Award (2022) and UWA Senior Research Award (2016). Recent work spans reservoir computing, network science, and epidemic modeling, with 354 publications and leadership in 18 grants. His interdisciplinary approach bridges academia and industry, addressing challenges in data-driven decision-making and sustainability.

• Complex Networks
• Dynamical Systems
• Granular Materials
• +3 more

David Walker is a Senior Lecturer at the University of Western Australia (UWA), affiliated with the School of Physics, Maths and Computing and the Department of Mathematics and Statistics. His research focuses on complex systems, dynamical systems, and granular materials, with applications in epidemiology, environmental science, and engineering. He holds an h-index of 15 and has published over 86 peer-reviewed articles. Walker’s research interests include the analysis of granular media mechanics, network science applied to time series data, and mathematical modeling of biological systems. His work contributes to UN Sustainable Development Goals related to clean water and climate action through studies on aquifer systems and disease control strategies. Key contributions include advancements in reservoir computing, network-based analysis of granular dynamics, and epidemic modeling. Recent work addresses mobility data analysis for pandemic control and utilizes ordinal partition networks for time series analysis. His research methodologies often integrate nonlinear dynamics, data compression techniques, and complex systems theory. Walker has secured seven grants, including projects on animal welfare, smart farming technologies, and network modeling of biological systems. Notable collaborations span institutions like the University of Melbourne, The Hong Kong Polytechnic University, and Bioss.ac.uk. His expertise bridges theoretical mathematics, computational science, and applied engineering challenges. Despite extensive contributions, no scientific awards are explicitly listed. His work emphasizes interdisciplinary applications, with ongoing projects in livestock health monitoring and geospatial analysis of groundwater systems.

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.

Nur HaldarView profile

Research Fellow

• Data Science
• Social Network Analysis
• Graph Analytics
• +3 more

Nur Haldar is a Research Fellow at The University of Western Australia (UWA), Australia, where he also earned his Ph.D. in Data Science. He previously held roles as an Associate Lecturer at UWA, a Researcher at King Saud University (Saudi Arabia), and a Senior Software Engineer at Mahindra Comviva Technologies (India). His research focuses on Data Science, Social Network Analysis, Graph Analytics, Cyber Security, and Deep Learning. Ph.D. in Computer Science (2021) from UWA, supported by CSIRO. Postgraduate Researcher at CSIRO Data61 (2018–2021). Research interests include optimizing location-based social networks, debiasing recommendation systems, dynamic network analysis, and entity alignment using pre-trained models. He has contributed to 27 research outputs, including peer-reviewed articles in IEEE Transactions and the ACM Web Conference. Notable awards: Third Prize in Alpha Innovation Contest (2018), Travel Award (2020), CSIRO Data61 Scholarship (2018). Engagement includes organizing The Web Conference 2023, serving on CIKM 2022 review panels, and contributing to curriculum development in data science.

• 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.

Booi KamView profile

Professor

• Supply Chain Management
• Logistics
• Information Systems
• +3 more

Professor Booi Kam is a faculty member in the School of Accounting, Information Systems and Supply Chain at RMIT University's College of Business and Law. He holds a PhD from the University of California, Los Angeles (UCLA). His research focuses on supply chain management, logistics, information systems, and education technology, with notable contributions in fraud detection, e-government adoption, and flipped classroom methodologies. He has co-authored two books and received prestigious awards such as the Emerald Literati Network Award. His consulting spans Asia and Europe, advising projects funded by international agencies and governments. He currently supervises research in analytics translation, digital transformation, and organizational resilience. Education: PhD from UCLA, expertise in supply chain and logistics systems. Teaching interests include supply chain principles, risk analysis, and logistics practices. Editor roles: Editorial Advisory Board member for International Journal of Logistics Management and Logistics . Research spans 88+ outputs including books, journals, and conference papers, emphasizing practical applications in emerging markets and technological innovation. Awards: Two Emerald Literati Network Awards. Supervision: Open to guiding Masters and PhD students in his research areas. Labs/Teams: Engaged in cross-disciplinary collaborations on digital transformation and analytics frameworks.

Falk ScholerView profile

Professor

• Information Access and Retrieval
• Fairness and Ethics of AI Systems
• Search Engine Evaluation
• +5 more

Professor Falk Scholer is a Professor of Information Access and Retrieval Technologies in the School of Computing Technologies at RMIT University, Melbourne, Australia. He leads the RMIT University Centre for Human-AI Information Environments (CHAI). His research focuses on understanding how search engines and recommender systems assist users in resolving information needs and measuring their effectiveness. His work emphasizes fairness, accountability, and human-centered computing. Research interests include interactive information access, evaluation methodologies, misinformation, and the ethical implications of AI systems. His projects span topics like bias mitigation in conversational search, physiological data analysis for user behavior, and generative AI adaptation for diverse scenarios. He collaborates on industry projects, such as enhancing job search systems with SEEK, improving query performance prediction, and developing fact-checking tools. His teaching interests include Data Science, Web Programming, and Research Methods. He actively supervises PhD and Master’s students in areas like fairness-aware systems and complex answer retrieval. Professor Scholer serves on committees including RMIT’s Academic Board and Human Research Ethics Committee. His research has been recognized through high-impact publications and partnerships addressing real-world challenges in search and information systems.

Laleh TafakoriView profile

Senior Lecturer

• Extreme Value Theory
• Dependence Modelling via Copulas
• Time Series Analysis
• +8 more

Dr. Laleh Tafakori is a Senior Lecturer in Statistics and Analytics at RMIT University’s School of Science, based at the City Campus in Australia. Her research focuses on extreme value theory, dependence modeling via copulas, time series analysis, machine learning, financial econometrics, and stochastic processes. She actively supervises research projects in areas such as diabetes prediction, climate modeling, and network analysis. Her teaching interests include applied analytics, statistical inference, and stochastic processes. Dr. Tafakori’s work bridges statistical theory and real-world applications, particularly in healthcare, finance, and environmental science. Recent projects include predicting diabetes onset, analyzing extreme precipitation patterns, and developing machine learning models for risk assessment. She collaborates internationally, with studies conducted in Saudi Arabia, Oman, and Europe. Research Highlights: Predictive modeling in healthcare, network influence analysis, and climate data forecasting. Supervision Interests: Machine learning applications, time series modeling, and complex systems analysis.

• Mathematical Modeling of Infectious Diseases
• Complex Networks
• Theoretical Population Biology
• +5 more

Associate Professor Stephen Davis is affiliated with RMIT University's School of Science. His research focuses on mathematical modeling of vector-borne and zoonotic diseases, complex networks, and biometric systems. He holds a PhD and has extensive international experience, including postdoctoral roles at institutions like Yale University and Utrecht University. His work spans theoretical epidemiology, with contributions to understanding diseases like plague, Lyme disease, and African sleeping sickness. Davis has co-authored a textbook on essential mathematical skills for STEM students and leads research projects on mosquito population dynamics and disease network modeling. He has published in high-impact journals like Nature and Science, and his current projects include analyzing gene co-expression networks and retinal template protection systems. Research Interests: Applied Mathematics, Public Health, Artificial Intelligence in Biometrics, Ecological Modeling, and Disease Dynamics. His work integrates mathematical tools with ecological and biomedical datasets to address global health challenges. Notable Contributions: Development of MAPPINGS software for phospho-signalling analysis, GRETINA retinal image dataset, and models predicting disease emergence in wildlife networks. His articles emphasize interdisciplinary approaches to epidemiology and biometric security.

• Cryptography
• Machine Learning
• Database Systems
• +3 more

Michael Brand is an Adjunct Professor at RMIT University's School of Computing Technologies. His research focuses on cryptography, privacy-preserving technologies, machine learning, and algorithm design. Key areas include zero-knowledge proofs, secure financial systems like FinTracer, and computational theory. His work bridges theoretical foundations with practical applications in data security and real-time processing systems. Research interests span advanced cryptographic protocols, privacy-enhancing machine learning techniques, and distributed computing paradigms. Notable contributions include innovations in blockchain tracing mechanisms and foundational work in statistical estimation theory. He actively publishes in top-tier venues, addressing challenges in secure computation, network analysis, and database systems. Collaborations emphasize interdisciplinary solutions to modern computational and security challenges.

• Computer Vision
• Robotics
• Intelligent Automation
• +5 more

Professor Alireza Bab-Hadiashar is a Professor of Mechatronics at RMIT University's School of Engineering, leading the Intelligent Automation Research Group. He holds a PhD in Robotics from Monash University and has held academic roles at Swinburne University and the University of Copenhagen. His research focuses on computer vision, robotics, and intelligent automation, with industrial applications in manufacturing and healthcare. He leads the Master of Robotics and Mechatronics Engineering program and has extensive experience in interdisciplinary projects involving AI, biomedical imaging, and advanced manufacturing. Research interests span computer vision, AI-driven automation, and robotics with emphasis on safety-critical systems like autonomous vehicles and medical imaging. Notable projects include defect detection in additive manufacturing and domain generalization in medical imaging. He collaborates with industry partners like Ford Australia on lightweight vehicle design and quality control. His recent publications emphasize distributed tracking systems, robotic calibration, and domain adaptation techniques. He actively supervises research projects in automated inspection, computer vision for transportation safety, and cancer detection via CT imaging. Academic memberships include IEEE, FIEAust, and professional organizations in computer vision and robotics. Labs/Teams: Director of the Intelligent Automation Research Group at RMIT. Current initiatives include developing AI solutions for Industry 4.0 and smart manufacturing systems.

Kam-Fung (Henry) CheungView profile

Lecturer

• Cybersecurity
• Transport and Logistics
• Operations Research
• +3 more

Kam-Fung (Henry) Cheung is a Lecturer at the School of Information Systems and Technology Management (ISTM), UNSW Business School. He holds a PhD in Transport and Logistics Studies from the University of Sydney (2021) and dual bachelor's degrees in Information Engineering (with a Computer Science minor) and Mathematics from the Chinese University of Hong Kong (2013–2014). His research focuses on cybersecurity, transport logistics, operations research, and network vulnerability analysis. Prior to academia, he worked as a Senior Consultant (Cybersecurity) at Ernst & Young (2021–2022) and served as a Research Assistant at institutions including the Institute of Transport and Logistics Studies (ITLS) and the Chinese University of Hong Kong's Department of Systems Engineering. Henry has secured over $280k in research funding from Australian organizations such as the Digital Grid Futures Institute and Woodside Energy. His awards include the 2024 Rising Star Award finalist (Australian Logistics Council), multiple teaching excellence awards (University of Sydney), and scholarships spanning his academic career. He has contributed over 20 peer-reviewed publications, focusing on cybersecurity frameworks, network analysis, and supply chain resilience. Research Interests: Cybersecurity in logistics, network vulnerability mitigation, attacker-defender models, and system dynamics in transport networks Teaching: Courses include Networking & Cyber Security (INFS3617), Programming for Data Analytics (INFS2822), and Managing Cloud Security (INFS5917) Key Projects: Developed decision support systems for transport companies, analyzed global shipping network connectivity, and pioneered algebraic methods for compromised network recovery

Rohitash ChandraView profile

Senior Lecturer

• Artificial Intelligence
• Machine Learning
• Climate Science
• +13 more

Dr. Rohitash Chandra is a Senior Lecturer in Data Science at the University of New South Wales (UNSW) School of Mathematics and Statistics. He leads research in AI methodologies (e.g., Bayesian deep learning, neuroevolution) with applications to climate extremes, geoscience models, mineral exploration, biomedicine, and ancient religious text analysis. He is the Data Theme Lead of the ARC ITTC Training Centre for Data Analytics in Minerals and Resources (2020–2025) and a Chief Investigator in NHMRC projects on AI-driven drug repurposing for COVID-19. Education: PhD in Artificial Intelligence (Victoria University of Wellington, 2012), MSc (University of Fiji, 2008), BSc (University of the South Pacific, 2006). Research Interests: His work spans AI-driven climate modeling (cyclone forecasting, paleoclimate analysis), geoscience applications (remote sensing for mineral exploration), medical diagnosis (skin cancer detection, drug repurposing), and AI in humanities (sentiment analysis of religious texts like the Bhagavad Gita). He pioneers methods like Bayeslands for geoscientific parameter estimation and Bayesreef for reef evolution modeling. Articles Trends: Recent work focuses on LLM applications in cultural analysis, economic forecasting frameworks, and Bayesian neural networks for uncertainty quantification. His publications bridge AI with interdisciplinary challenges in earth sciences, healthcare, and social sciences. Grants & Awards: Recipient of the UNSW Science Silverstar Award (2022), Sydney Fellowship (2017–2019), and over $10M in ARC/industry funding. He has supervised 40+ students across PhD, masters, and honours programs. Labs & Teams: Founder of the Transitional Artificial Intelligence Research Group (t-AI). Collaborates with EarthByte Group, UN agencies, and institutions like Imperial College London on projects ranging from Mars landscape modeling to Hindu philosophy LLMs like Vedanta-GPT.