Find Academic Supervisors

Xuyun ZhangView profile

Associate Professor

• Scalable Machine Learning
• Big Data Privacy
• Cyber Security
• +5 more

Dr. Xuyun Zhang is an Associate Professor at the School of Computing, Macquarie University, Australia. He holds a PhD from University of Technology Sydney (2014), and Master's/Bachelor's degrees from Nanjing University (China). His research focuses on scalable machine learning, big data privacy, cyber security, edge computing, and IoT. He has led 11 research projects including Next-Gen Banking (2024-2028) and Scalable Document Intelligence (2022-2027). His work spans secure AI, privacy-preserving techniques, and edge-cloud collaboration. He has received awards such as the Chancellor's Best PhD Thesis (2015) and Early-Career Research Excellence (2019). His recent publications address areas like metaverse service offloading, hypergraph trust prediction, and federated learning frameworks. He actively organizes conferences like the Australasian Information Security Conference (2017) and chairs industry-research initiatives in cloud computing and mobile networking. Education: PhD (UTS, 2014), MEng (Nanjing University, 2011), BSc (Nanjing University, 2008). Teaching includes Machine Learning in Cybersecurity, Data Science, and Software Engineering. Key research contributions include privacy-preserving publishing, edge computing offloading methods, and blockchain-based secure access management. His projects often integrate AI with cloud-edge systems to address real-world challenges in finance, healthcare, and smart manufacturing.

Foad TaghizadehView profile

Lecturer

• Electric Vehicle Technology
• Power Electronics
• Renewable Energy Integration
• +3 more

Dr Foad Taghizadeh is a Lecturer in electrical engineering at Macquarie University, Australia, where he also held prior research fellowships. He holds honorary visiting scholar positions at the University of Technology Sydney and Griffith University. His research focuses on electric vehicle (EV) charging systems, grid integration of renewables, and microgrid technologies. He earned his PhD in Electrical Engineering from Macquarie University in 2018. Education: PhD in Electrical Engineering, Macquarie University (2018) Research interests include EV charging infrastructure, grid impact analysis, renewable energy integration, and energy storage systems. His work addresses challenges in high-speed EV charging, microgrid stability, and converter control for distributed energy resources. Awards and Honors: Fellow of Higher Education Academy (FHEA), UK (2020) Highly Commended Finalist, Vice-Chancellor's Learning & Teaching Early Career Award (2022) New South Wales Fresh Science Award (2019) FSE Excellence in Early Career Teaching Award (2022) Current Projects: Wireless power transfer for heavy EVs and marine vessels (2024–2027) Development of economically sustainable EV charging facilities Modular microgrid for NSW sustainable development Labs/Teams: Active in Macquarie University's energy systems research group and collaborates with industry partners on EV charging infrastructure projects.

Carlo KoppView profile

Lecturer

• Modelling Deception
• Cognitive Cycle Modelling
• Ad Hoc Networks
• +9 more

Carlo Kopp is a Lecturer in the Department of Software Systems & Cybersecurity at Monash University's Faculty of Information Technology. His research focuses on information-theoretic models of deception in social and biological systems, perceptual errors, cognitive cycles, and distributed computing. He has held adjunct roles at Monash Asia Institute (2005–2010) and ADFA@UNSW (2004), and serves as Editor-in-Chief and Research Lead at Air Power Australia since 2004. He advises parliamentary committees and media on issues like high-power electromagnetic events (e.g., NCIS LA episode 3.11). Education: PhD in Computer Science, Monash University (2000) MSc (Research) in Computer Science, Monash University (1996) BEng (Hons) in Electrical Engineering, University of Western Australia (1984) Research Interests: Modelling deception in complex systems, ad hoc networks, radar propagation, and strategic military analysis. He explores topics like disinformation effects, renewable energy in cloud computing, and cognitive biases. Awards: Best Paper Award (2002) Outstanding Teaching Award (2015) Australian Industry Certificate Award (2010) Teaching: Chief Examiner and Lecturer for courses including Network Architecture (FIT2020), Distributed Computing (FIT3142), and Advanced Network Design (FIT5011). Previously led curriculum design for Computer Networks and Telecommunications Management units. Industry Experience: 20+ years as a software/hardware engineer, project manager, and consultant in embedded systems, operating systems, and strategic technology planning. Managed ISO9001 accreditation for an Australian computer manufacturer. Community Work: Advised US Congressional bodies and testified before Australian parliamentary committees on disinformation and cybersecurity. Serves as a Fellow of the Lean Systems Society and Associate Fellow of AIAA.

• Recommender Systems
• Artificial Intelligence
• Data Mining
• +6 more

Dr. Yongli Ren is an Associate Professor in the School of Computing Technologies at RMIT University, located at the City Campus in Australia. His research focuses on advancing Recommender Systems, with particular emphasis on fairness, quantum computing optimization, and spatio-temporal data analysis. He holds expertise in data mining, collaborative filtering, and context-aware systems. Dr. Ren is open to supervising Masters and PhD students in areas such as quantum annealer-based optimization, fairness-aware recommendation, and heterogeneous time-series analysis. His teaching interests include web search algorithms, collaborative filtering, data mining methodologies, and log analysis. Dr. Ren has contributed to over 70 research outputs, with recent works addressing recommendation system evaluation metrics, fairness in AI, and energy-efficient routing protocols in wireless sensor networks. His research has been published in prestigious venues like ACM Transactions on Information Systems, IEEE Access, and the ACM Web Conference (WWW). Key research themes include addressing the precision-diversity trade-off in recommendations, enhancing fairness through dual-temporal networks, and leveraging quantum computing for QUBO optimization. He has also explored applications in mobility pattern analysis, workplace productivity systems, and fog-based distributed recommendations.

• Development Economics
• International Development
• Poverty and Inequality
• +7 more

Simon Feeny is a Professor of Development Economics at RMIT University's School of Economics, Finance and Marketing, and co-directs RMIT's Centre for International Development. He holds adjunct roles at Charles Darwin University and Deakin University. With over 20 years in international development, his research focuses on poverty, inequality, and aid effectiveness, spanning countries like Bangladesh, Vietnam, and Fiji. His work has been recognized with awards such as being named Australia’s Top Development Economist (2018). Feeny's research interests include foreign aid allocation, vulnerability to economic shocks, and sustainable development. He has led projects valued at nearly $3 million for organizations like the UN Food and Agriculture Organisation and AusAID. His editorial roles include Associate Editor for Australian Economic Papers and membership in Amrita University's International Advisory Board. Research Themes: Development cooperation, climate action, multidimensional poverty, and behavioral economics. Global Reach: Projects in 20+ countries, including work on child sponsorship, climate resilience, and agricultural sustainability. Feeny's recent publications analyze poverty measurement in Sri Lanka, the impact of cultural heterogeneity on violence in Indonesia, and sustainable development frameworks. His work bridges theoretical insights with practical policy design, emphasizing real-world applicability.

Zsuzsanna CsereklyeiView profile

Associate Professor

• Energy Economics
• Environmental Economics
• Electricity Markets
• +4 more

Associate Professor Zsuzsanna Csereklyei is affiliated with RMIT University's Department of Economics Finance & Marketing. Her research focuses on energy transitions, electricity markets, and efficient technology adoption. She leads the Energy Theme within RMIT's Sustainable Technologies and Systems Impact Platform (2022–2025). Her work spans energy policy, renewable integration, and climate change economics, with notable contributions to understanding market design, battery storage, and transport electrification. Education and Career: Holds a PhD recognized by Austria's Excellence Award, with prior experience in industry management and consulting. Teaches energy policy, political economy, and econometrics. Research Interests: Energy and resource economics, environmental policy, electricity market design, sustainable technologies, and asteroid mining economics. Active in collaborative projects like RACE for 2030 CRC and Australian Energy Market studies. Publications: Over 27 peer-reviewed articles in top journals including Energy Economics , Ecological Economics , and Journal of Econometrics . Recent work emphasizes Australia's energy transition challenges and opportunities. Awards: Award of Excellence for Thesis by Austrian Ministry of Science and Research (best national dissertation). Supervision: Guides PhD/Masters research on renewable energy socioeconomic impacts, electric vehicle diffusion in Indonesia, and retail petrol pricing patterns. Labs/Teams: Coordinates interdisciplinary teams in energy systems modeling and policy analysis through RMIT's Energy Theme leadership.

• Additive Manufacturing
• Laser Technology
• Materials Science
• +3 more

Distinguished Professor Milan Brandt is a leading academic in advanced manufacturing and laser technology at RMIT University. He serves as Director of the RMIT Advanced Manufacturing Precinct and founded the Centre for Additive Manufacturing (2013). His academic roles include Professor (2010–present) and Technical Director of the Advanced Manufacturing Precinct (2012–2027). He holds adjunct roles at the University of Waterloo and Melbourne University's Department of Medicine. His research focuses on additive manufacturing, laser applications, and materials science, with over 35 years of industry and academic contributions. Prof. Brandt’s work has led to commercialized technologies, patents, and international recognition, including the Arthur L. Schawlow Award (2020) and Engineers Australia Centenary Hero (2018). He pioneered Australia’s first 3D-printed spinal implant (2016). Leadership roles include President of the Laser Institute of America (2018). His research spans laser cladding, welding, and cutting, with a focus on titanium alloys, lattice metamaterials, and process optimization. Key Awards: Arthur L. Schawlow Award, Engineers Australia Centenary Hero, CAST Commercialisation Awards. Industry Impact: Commercialization of LMCut, Laser Repair for turbine blades, and additive manufacturing of medical implants. Leadership: Laser Institute of America President (2018), Director of multiple research centers. His current projects explore multi-material additive manufacturing, high-speed laser deposition, and advanced materials for aerospace and medical applications. He actively supervises PhD/Masters students in these areas and collaborates with global industry partners.

Jeff LeeView profile

Research Fellow

• Structural Engineering
• Materials Science
• Mechanical Engineering
• +3 more

Dr. Jeff Lee is a Research Fellow in the Centre for Innovative Structures and Materials (CISM) at RMIT University's School of Engineering, supervised by Distinguished Professor Mike Xie. His research focuses on developing novel techniques for designing and manufacturing innovative structures and materials, with applications in industrial projects. Lee's work emphasizes structural efficiency, bio-inspired design, and advanced manufacturing processes. He is actively involved in supervising research projects related to digital design, topology optimization, and architectural applications of structural systems. Lee's research outputs span structural engineering, materials science, and computational design, with a notable emphasis on deployable structures, energy absorption mechanisms, and interactive design tools. His projects often bridge theory and practice, addressing challenges in both academic and industrial contexts. He has contributed to advancements in topology optimization, curved surface construction, and bio-inspired metamaterials. Lee is open to supervising master’s and PhD students in areas such as advanced manufacturing, structural topology optimization, and innovative design methodologies. His collaborative work with industry partners ensures real-world applicability of his research findings.

• Electrical and Electronic Engineering
• Applied Mathematics
• Physical Sciences
• +3 more

Ali Moradi Amani is a Senior Lecturer at the RMIT University School of Engineering , specializing in Electrical & Electronic Engineering. Based at the City Campus in Australia, his research focuses on clean energy systems, machine learning applications in power networks, and smart grid technologies . Affiliation: RMIT University, School of Engineering Academic Rank: Senior Lecturer Email: ali.moradiamani@rmit.edu.au His recent publications (2020-2025) demonstrate expertise in: AI-driven energy sustainability solutions Electric vehicle charging infrastructure optimization Community battery storage systems Multi-energy system demand response Dynamic grid operating envelopes Solar system demand management He actively supervises Masters and PhD research projects in these areas, emphasizing scalable and robust energy solutions through data-driven approaches and advanced optimization techniques.

• Power Electronics
• Energy Storage
• Electromobility
• +2 more

Mahinda Vilathgamuwa is Professor in Power Engineering at QUT's Faculty of Engineering, specializing in power electronics and energy systems. An IEEE Fellow, his research covers wireless power transfer, grid resilience, battery management, and renewable integration. Key innovations include control strategies for grid-forming inverters, degradation-conscious battery scheduling, and dynamic wireless charging systems. Supervision focuses on electromobility and grid-interactive storage systems.

• Power Systems
• Renewable Energy
• Power Electronics
• +1 more

Gerard Ledwich is an Adjunct Professor at Queensland University of Technology's School of Electrical Engineering & Robotics, where he serves as Discipline Leader for Power Engineering and Theme Leader at the Institute for Future Environments. His research group focuses on risk-managed distribution planning, wide-area grid controls, demand management, and power electronics applications including microgrid control and power hardware-in-the-loop systems. Research interests include: Electricity industry restructuring and market design Renewable energy integration challenges Advanced power electronics for grid stability Interconnection of state electricity networks Recent publications (2022-2024) primarily investigate grid stability solutions for renewable integration, distribution network optimization, and advanced control algorithms for power converters. Work consistently addresses challenges in transitioning to sustainable energy systems through computational methods and hardware innovations. Currently supervises doctoral research on superconducting systems. Secured multiple Australian Competitive Grants including: LP140100923: Customer Responsive Risk-Managed Network Planning LP120100302: Increased power transfer through SVC control Leads the Power Engineering Group at QUT and collaborates internationally through the Institute for Future Environments.

James McGreeView profile

Professor

• Statistical Methodology
• Bayesian Design
• Clinical Trial Design
• +1 more

Professor James McGree is Professor of Statistics in the School of Mathematical Sciences at Queensland University of Technology, holding a PhD from the University of Queensland. His research focuses on optimal experimental design, Bayesian adaptive methodology, and innovative clinical trial development. Research interests span: Bayesian adaptive designs for clinical and ecological applications Statistical methodology for complex spatial and temporal data Optimization of sampling strategies for environmental monitoring His publication portfolio demonstrates extensive methodological development in Bayesian statistics applied to environmental science, healthcare, and industrial optimization. Recent work includes flood mitigation strategies, coral reef monitoring, and clinical trial designs for rare diseases. Professor McGree actively supervises doctoral candidates working on Bayesian system identification, subsampling methods for big data, and robust clinical trial designs.

Jason FordView profile

Professor

• Robotics
• Decision Systems
• Autonomous Systems
• +4 more

Jason Ford is a Professor of Electrical Engineering at Queensland University of Technology (QUT), leading research in trustworthy autonomous systems and decision-making under uncertainty. He holds positions in QUT's Centre for Robotics and Centre for Data Science. Ford earned his PhD from the Australian National University (ANU) and has over 20 years of experience in aerospace, energy, and defense sectors. His research focuses on model-based filtering, estimation, and decision systems for dynamic environments, with applications in aerial autonomy, infrastructure inspection, and low-signal-to-noise detection. Education: B.Sc. and B.E. (1995), PhD (1998), all from ANU. Professional Experience: Research Scientist at DSTG (1998-2004), Research Fellow at UNSW (2004-2005), QUT faculty since 2005, promoted to Professor in 2019. Research Interests: Robust autonomous systems, vision-based sense-and-avoid, model-driven decision systems, and resilient robotic technologies. His work on ROAMES asset management systems has saved Queensland $40M/year and won international awards. Awards: 2019 Academic of the Year (Australian Defence Industry), multiple best paper awards, and industry recognition for collision avoidance and UAV technologies. Grants: Over $10M in competitive research funds since 2009. Supervision: 8 completed HDR students since 2008, teaching control systems and autonomous systems to 1000+ undergraduates. Labs/Teams: Program Lead for Decision and Control in QUT's Robotics Centre, collaborator with industry partners like Insitu Pacific and Caterpillar.

Dakshi Kapugama GeeganageView profile

Research Fellow

• Text Mining
• Topic Modelling
• Natural Language Understanding
• +3 more

Dr. Dakshi Kapugama Geeganage is a Research Fellow in Process Science and Sessional Academic at the School of Information Systems, Queensland University of Technology (QUT). She holds a PhD in Data Science from QUT (2022) and has prior academic qualifications including an MSc in Artificial Intelligence (University of Moratuwa, Sri Lanka, 2014) and a BSc (Special) Honors in Information Technology (Sri Lanka Institute of Information Technology, 2010). Her research focuses on text mining, topic modelling, natural language understanding, and process mining. She has contributed to advancements in semantic-based topic representation, event log enrichment using unstructured text (Text2EL+), and digital health data quality analysis. Her work bridges data science methodologies with practical applications in healthcare and process optimization. Key publications include studies on semantic-enhanced topic modelling (e.g., Semantic-LDA), digital health data imperfections, and event log enrichment. Her research trends emphasize integrating semantic analysis and domain-specific expertise to enhance data-driven decision-making. Awarded Fellow of the Higher Education Academy (FHEA), Dr. Geeganage is actively involved in teaching and research supervision. Her projects often involve collaborations with interdisciplinary teams, focusing on real-world challenges in healthcare data systems and process automation. She is affiliated with the QUT Centre for Data Science, contributing to initiatives in sports data science and AI.

Aaron McFadyenView profile

Senior Lecturer

• Unmanned Traffic Management
• Aerospace Engineering
• Robotics
• +3 more

Dr. Aaron McFadyen is a Senior Lecturer in the School of Electrical Engineering & Robotics at Queensland University of Technology (QUT), with a focus on aerospace engineering, robotics, and control systems. He holds a PhD from QUT and has extensive industry experience in flight simulation and autonomous systems research. His research emphasizes unmanned traffic management (UTM), air traffic modeling, and quantitative risk analysis, with applications in drone integration and airspace safety. He has secured over $1.5M in research funding and led projects for organizations like Airservices Australia and CASA. Key achievements include developing Australia's Automated Airspace Authorization system and winning the 2019 IEEE/AIAA Best Unmanned Traffic Management Paper Award. Education: PhD in Robotics and Autonomous Systems (QUT), Private Pilot License (CASA), and qualifications in aerospace engineering. Professional memberships include IEEE, JARUS, and the Control System Society. Research interests span UTM systems, collision risk modeling, and control systems for autonomous vehicles. His work bridges academia and industry, addressing challenges in air traffic management and robotics. Recent publications focus on nonlinear control, airspace risk analysis, and multi-agent systems. He has supervised undergraduate, masters, and PhD students in aerospace and robotics. Awards include the ARC DECRA Fellowship (2020) and Advance Queensland Mid-Career Fellowship (2020). His lab collaborations include the Australian Research Centre for Aerospace Automation (ARCAA) and ETH Zurich's Autonomous Systems Lab.