Find Academic Supervisors

Iain MacgillView profile

Associate Professor

• Renewable Energy Integration
• Electricity Market Design
• Distributed Energy Systems
• +4 more

Associate Professor Iain MacGill at the University of New South Wales leads interdisciplinary research at the intersection of renewable energy integration , electricity market design , and policy frameworks for low-carbon transitions. As a core member of the Centre for Energy and Environmental Markets , he develops computational models for coordinating distributed energy resources in restructured power systems. PhD in Electrical Engineering (UNSW) B.Eng. & M.Eng.Sci. (University of Melbourne) His work examines technical-economic-commercial challenges of renewable integration, with recent focus on: 24/7 zero-emission energy tracking frameworks Temporal matching in renewable procurement Open-source tools for green hydrogen value chains Residential solar-battery interaction dynamics Current research projects involve smart grid technologies , community microgrid economics , and climate-resilient energy systems . He collaborates with institutions like ARENA , CSIRO , and ARC on grants addressing grid stability, cost allocation, and policy innovation.

Justin LipmanView profile

Professor

• Cybersecurity
• Internet of Things (IoT)
• Wireless Communications
• +6 more

Justin Lipman is a Professor in the School of Electrical and Data Engineering at the University of Technology Sydney (UTS). He serves as Director of the Cyber Digital Centre and previously led the RF and Communications Technologies Lab. With over 12 years of industry experience at Intel and Alcatel, his expertise spans cybersecurity, IoT, 5G/O-RAN, digital agriculture, and smart cities. Lipman holds a PhD in Telecommunications Engineering from the University of Wollongong. Research & Funding Secured $35M+ in research grants, including projects like UTS Vault ($8M) and the Nokia 5G Futures Lab. Focus areas: Cybersecurity, IoT infrastructure, wireless communications, and smart agriculture. 24 U.S. patents granted, including innovations in IoT security and metamaterials. Education PhD in Telecommunications Engineering (University of Wollongong, 2004) Industry experience: Chief Architect at Intel (2006–2016), Program Manager at Alcatel-Lucent (2005). Awards & Recognition IEEE Senior Member (2012) Leadership roles: Deputy Chief Scientist (Food Agility CRC), Board Director (Internet of Things Alliance Australia). Teaching & Labs Supervises HDR/PhD students in areas like SDN and IoT. Labs: RF and Communications Lab, BlueSky initiative.

Gea RahmanView profile

Lecturer

• Data Science
• Machine Learning
• Ensemble Learning
• +7 more

Dr. Gea Rahman is a Lecturer in Computing at Charles Sturt University (CSU), specializing in data science and machine learning. He holds a PhD in Computer Science from CSU, and MSc/BSc degrees from Rajshahi University, Bangladesh, where he was awarded a Gold Medal for academic excellence. With over 20 years of teaching experience, he previously served as Professor and Programme Director at Bangladesh Agricultural University. Education: PhD in Computer Science (Data Science/ML), Charles Sturt University (2011-2015) MSc in Computer Science & Engineering, Rajshahi University (2002-2003) BSc (Hons) in Computer Science & Technology, Rajshahi University (1998-2002) Research Interests: Data science applications in agriculture, healthcare, and environmental monitoring Machine learning techniques including ensemble/deep learning, transfer learning, and incremental learning Data preprocessing methods (missing value imputation, outlier analysis) AI ethics and healthcare consent strategies Awards: Global Research Impact Recognition Award 2020 Best Researcher Award Gold Medal for Academic Excellence (2003) Advising & Grants: Principal supervisor for multiple postgraduate students Recipient of grants including: Ai-Enabled Segmentation of Brain MRI (2024) Adaptive Federated Learning Framework (2024) Unusual Behaviour Detection in Aged Care (2023) He is part of the Data Mining Research Group (DaMRG) and actively contributes to conferences/journals as a reviewer and editorial board member.

• Cybersecurity
• Data Security and Privacy
• Context-Aware Access Control
• +5 more

Dr ASM Kayes serves as Senior Lecturer in Cybersecurity and Cyber Curriculum Lead at La Trobe University's Department of Computer Science and Information Technology, where he shapes cybersecurity education programs including Master's, Bachelor's, and Double Degrees. His academic journey began with a PhD from Swinburne University of Technology in 2015, followed by postdoctoral research at La Trobe before joining as Lecturer in 2019 and promotion to Senior Lecturer in 2022. His research spans critical cybersecurity domains including data security, privacy preservation, context-aware access control, malware/ransomware defense, and IoT/fog/cloud security leveraging AI/ML techniques. Dr Kayes has established himself as a leading voice in blockchain security frameworks, privacy policy analysis, and cyber incident response through publications in top-tier venues like ACM Computing Surveys, IEEE Internet of Things Journal, and Computers & Security. His recent publications reveal a strong trajectory toward integrating AI with traditional security frameworks, particularly in blockchain risk assessment (2025), cross-domain access control (2025), and IoT behavior prediction (2024). The research demonstrates consistent focus on practical security solutions addressing ransomware mitigation, privacy breaches, and emerging threats in decentralized systems. Over $880,000 secured as Chief Investigator for cybersecurity projects Australian Government Department of Social Services grant (2023-2026) for cyberbullying prevention AustCyber research funds with industry partners (2020-2023) SmartSat CRC and ASCRIN PhD scholarship grants (2021) Dr Kayes has successfully supervised 5 PhD candidates to completion and currently mentors 5 doctoral students across diverse topics including AI-driven threat hunting, satellite network security, and blockchain risk frameworks. His collaborative network spans UK, USA, Europe, and Asia, with active industry partnerships through Westpac, BHP, and Quantum Victoria. He serves on editorial boards for leading cybersecurity journals and has examined HDR dissertations globally, reflecting his significant standing in the academic community.

Davide GuccioneView profile

Researcher

• Rock Mechanics
• Geomechanics
• Photogrammetry
• +2 more

Davide Guccione is an ARC Industry Fellow at the University of Newcastle within the College of Engineering, Science and Environment . His research focuses on rock mechanics , rockfall analysis , and photogrammetry , with significant contributions to understanding rock fragmentation dynamics and improving monitoring systems. Education : PhD in Civil Engineering (University of Newcastle, 2021), Master of Civil Engineering (University of Parma), Bachelor of Civil Engineering (University of Parma), and Diploma in Mining and Geoenvironmental Qualified Industrial Technician. Research Interests span experimental rock mechanics, rockfall hazard assessment, and photogrammetry. His work includes developing a novel VoxFall algorithm for volumetric rockfall detection and a stochastic fragmentation model for rockfall simulations. Current projects involve coastal cliff monitoring and deep learning-based photogrammetric calibration. Scientific Awards include: D.H. Trollope Medal (2023) Early Career Researcher Excellence Award - Highly Commended (2023) NSW Young Geomechanical Professionals Award 1st Runner-Up (2024) Australian Geomechanics Society Postgraduate Research Prize (2019) Grants and Funding highlight a $496,614 ARC Early Career Industry Fellowship and a $300,000 Newcastle City Council grant for coastal cliff monitoring. He supervises three PhD students and collaborates with institutions like Rocscience and the European Commission.

• Data Management
• Social Media Analysis
• User Behavior Analysis
• +4 more

Dr. Ying Zhou is a Lecturer in the School of Computer Science at The University of Sydney. She holds a BSc and MEng from Nanjing University (1997) and a PhD from the School of Computing at the National University of Singapore (2003). Her research focuses on human-centred data management, including large-scale data storage, user behavior analysis, and improving image query systems. Current projects include mining socially tagged images and accountability mechanisms for multitenant cloud platforms. Teaching responsibilities include courses on Advanced Data Models (COMP5338), e-Commerce Technology (COMP5347), and Cloud Computing (COMP5349). She collaborates with industry partners like Amazon and IBM, and advises four research students. She is a member of the Sydney Southeast Asia Centre. Her publication record spans 20 years, with recent work emphasizing machine learning applications, big data systems, and cloud security. Research trends include optimizing distributed computing frameworks (Hadoop/Spark/Flink), adversarial machine learning, and trustworthy database systems. Earlier work focused on social network analysis, web communities, and blogosphere dynamics. Current research projects address both technical challenges (e.g., efficient image search, cloud platform accountability) and applied solutions for leveraging social media data. Her lab activities involve interdisciplinary collaboration between computer science and information systems domains.

Sanjoy PaulView profile

Associate Professor

• Supply Chain Risk Management
• Supply Chain Resilience
• Sustainable Supply Chain Management
• +3 more

Sanjoy Paul is an Associate Professor at the University of Technology Sydney (UTS) Business School, specializing in supply chain management and operations research. He holds roles as Associate Editor of Business Strategy and the Environment and Global Journal of Flexible Systems Management . His research focuses on supply chain resilience, risk modeling, and sustainable practices, with applications to global disruptions like pandemics and IT outages. Paul has published in top-tier journals such as the European Journal of Operational Research and secured grants from government bodies including the Department of Defence. Education and Career: Prior to UTS, he worked at RMIT University and Bangladesh University of Engineering and Technology. He holds a PhD from UNSW, recognized with the Stephen Fester Prize for outstanding thesis. His career spans academic roles from Lecturer (2017) to Senior Lecturer (2019) before his current position since 2023. Research Contributions: Paul’s work bridges theoretical models and real-world applications, including recovery frameworks for supply chains during crises and strategies for sustainable practices in post-pandemic contexts. He frequently advises media on supermarket pricing, supply chain disruptions, and business strategies, appearing in outlets like The Guardian and ABC News . Awards and Recognition: His honors include the ASOR Rising Star Award, Research with Relevance Award, and inclusion in the top 2% global scientists (2020–2023). He has contributed to policy debates on supermarket competition, EV market dynamics, and Australia’s industrial strategies.

• Service-oriented Computing
• Web Services
• Workflow Technology
• +7 more

Jun Yan is a Professor at the University of Wollongong's School of Computing and Information Technology within the Faculty of Engineering and Information Sciences. His roles include academic leadership and research supervision, with active involvement in committees like the Student Academic Experience Sub-Committee and Quality Assurance Review Group. Current research focuses on service-oriented computing, workflow technology, adaptive process management, and AI-driven systems. His work intersects with IoT, UAV systems, federated learning, and multi-agent reinforcement learning. Research interests span service-oriented software engineering, decentralized workflow management, and cybersecurity challenges in autonomous systems. Notable projects include an ARC-funded initiative on robust defenses against adversarial ML for UAV systems (2025–2027). He supervises Masters/PhD projects on topics like diffusion model-based MRI, graph prompt learning, and industrial defect detection. His publications from 2023–2025 emphasize scalable multi-agent systems, federated learning with non-IID data, and UAV applications in intelligent transportation. Key areas of contribution include trust models for e-commerce, privacy-preserving cloud computing, and fault-tolerant service architectures.

Alistair BarrosView profile

Professor

• Service Computing
• Enterprise Systems
• Business Process Management
• +5 more

Professor Alistair Barros is Head of School and Academic Program Leader of Service Science at QUT’s School of Information Systems (Faculty of Science). He holds a PhD from the University of Queensland, with extensive ICT experience in academia and industry (e.g., Global Research Leader at SAP AG). His research focuses on enterprise systems design, optimization, and interoperability in cyber-physical environments leveraging cloud, IoT, and blockchain technologies. Key areas include service computing methods (microservices, business process management), model-based systems re-engineering, and distributed service coordination. Education: PhD in Information Systems from the University of Queensland. Research Interests: Enterprise systems modernization, service-oriented architectures, Industry 4.0 applications, and digital transformation in sectors like construction, manufacturing, and supply chains. He has published 180+ peer-reviewed articles (including 6 edited books) and holds 17 US patents. Notable contributions include the BPMN 2.0 standard co-authorship and the 'Workflow Patterns' paper (most cited in BPM). Awards: Test of Time Award (BPM 2015), Wharton Education Award (TeachConnect platform), and impactful industry collaborations (e.g., SAP NetWeaver, Federal Government WPIT project). Grants & Projects: Led ARC, EU FP6, and industry-funded projects. Current focus includes re-engineering legacy systems for microservices, EV charging network design, and adversarial ML in cybersecurity. Labs/Teams: Active in QUT’s Centre for Data Science, focusing on service science and cyber-physical systems integration.

• High-performance computing
• Modelling of Acoustic Wave devices
• Embedded system design
• +3 more

Dr. Glenn Matthews is a Senior Lecturer in the School of Engineering at RMIT University, specialising in Electrical and Computer Engineering. He holds positions in both academic and research capacities, including Principal Investigator roles at CSIRO and Smart Services CRC. His teaching responsibilities include coordinating undergraduate courses such as Introduction to Engineering Computing and Engineering Design modules. Dr. Matthews' research focuses on high-performance computing, acoustic wave device modeling using Finite Element Method (FEM), and embedded system design. Notable projects include developing FEM software for SAW device analysis and investigating asynchronous computing architectures for high-throughput systems. He has supervised numerous research projects spanning machine learning applications in clinical analysis, gas sensing technologies, and neuromorphic learning. His work integrates hardware-software co-design principles, with contributions to radar SLAM systems, mercury vapor sensors, and neural network frameworks like SwiftSpike. Dr. Matthews collaborates with industry partners through ARC Linkage Grants and maintains affiliations with IEEE and DSP/Embedded Systems groups. His research outputs include over 20 peer-reviewed articles, with impactful contributions to sensor technology, circuit design, and biomedical applications.

Xiaoyu XiaView profile

Lecturer

• System Privacy and Security
• AI Privacy
• Parallel and Distributed Computing
• +4 more

Dr. Xiaoyu Xia is a Lecturer (equivalent to Assistant Professor in North America) in Cybersecurity & Software Systems at RMIT University's School of Computing Technologies. He received his PhD with the prestigious Alfred Deakin Medal from Deakin University, Australia, and has established himself as a leading researcher in distributed systems and cybersecurity with over 50 peer-reviewed publications in top-tier venues including IEEE S&P, ACM WWW, and IEEE Transactions. Dr. Xia's research spans critical areas at the intersection of computing and security: System Privacy and Security Distributed Systems and Edge Computing AI Privacy and Machine Learning Systems Sustainable Computing Cybersecurity and Privacy-Preserving Technologies His recent work demonstrates a clear trajectory toward developing practical privacy-preserving frameworks for emerging technologies, particularly in edge computing environments and large language models. Dr. Xia has made significant contributions to machine unlearning, secure data management in distributed systems, and energy-efficient edge computing solutions that balance performance with sustainability concerns. Dr. Xia has received notable recognition for his scholarly impact: World's Top 2% Scientists by Stanford University (2022-2024) Alfred Deakin Medal for PhD research excellence (2021) Teaching Excellence Award from Swinburne University of Technology (2021) As an active researcher, Dr. Xia currently leads multiple funded projects including an ARC Discovery Project grant worth over $500,000 for developing privacy-aware intelligent digital twins for secure critical infrastructures. He is open to supervising motivated PhD students with interests in system security and privacy, and distributed ML systems. Dr. Xia serves the academic community through editorial roles as Associate Editor for IEEE Transactions on Dependable and Secure Computing and as a Review Board Member for IEEE Transactions on Parallel and Distributed Systems, and regularly participates in program committees for major conferences including ACM WWW and IEEE ICDCS.

• Structural Engineering
• Architectural Science
• Building Energy Management
• +3 more

Professor Gianluca Ranzi is a Professor in the School of Civil Engineering at the University of Sydney, serving as Chairman of the Centre for Advanced Structural Engineering. His expertise spans structural engineering, architectural science, and heritage conservation, with a focus on sustainable building technologies. He leads research on composite materials, energy-efficient structures, and the mitigation of urban heat islands. Research Interests: His work addresses the behavior of concrete and composite steel-concrete structures, building energy management systems, and heritage conservation strategies for twentieth-century concrete structures. He develops adaptive systems to enhance indoor comfort and building functionality while reducing energy consumption. Publications: Ranzi has authored/edited key texts including Design of Prestressed Concrete to Eurocode 2 (2017) and Structural Analysis: Principles, Methods and Modelling (2015). His recent articles explore topics like lithium slag composites, P2P energy trading, and dynamic characterization of historic structures. Teaching: He teaches courses such as CIVL3511/CIVL9511 (Basics of Integrated Building Engineering) and CIVL5531 (Advanced Integrated Building Engineering). Supervises PhD/Master's students on projects like composite brick-concrete construction and crack control in shotcrete linings. Affiliations: Member of Standards Australia committees (BD-002, BD-032), American Concrete Institute (ACI), and the International Association for Bridge and Structural Engineering (IABSE).

Dan SteinbergView profile

Academic

• Probabilistic Machine Learning
• Variational Inference
• Bayesian Deep Learning
• +7 more

Dan Steinberg is a senior research scientist and team leader of the Decisions & Statistical Learning team at CSIRO Data61 in Canberra, Australia. His expertise lies in probabilistic machine learning, variational inference, Bayesian deep learning, causal inference, and their application to domains spanning synthetic biology, geospatial analytics, and algorithmic fairness. Education PhD in Computer Vision / Machine Learning (2013) – University of Sydney, Australian Centre for Field Robotics Bachelor of Engineering (Mechatronics, First-Class Honours) – University of Sydney (2008) Bachelor of Commerce (Finance) – University of Sydney (2008) Research Interests Steinberg’s core research agenda revolves around building scalable probabilistic models that can learn efficiently from limited or noisy data and provide principled uncertainty estimates. Key themes include: Variational Inference & Bayesian Deep Learning: developing lightweight yet powerful algorithms for approximate posterior inference in complex models (e.g., Aboleth, Revrand). Active Learning & Experimental Design: creating methods that decide which experiments or measurements will maximise information gain, with recent focus on in-silico protein engineering via Variational Search Distributions (VSD). Causal Inference: leveraging machine-learning tools to perform robust observational causal studies for evidence-based policy, including work on youth well-being and academic outcomes. Algorithmic Fairness: translating normative notions of equity into quantifiable objectives for regression-based decision systems. Large-scale Spatial Analytics: Landshark—an open-source TensorFlow toolkit for supervised learning on massive geospatial raster datasets. Notable Software & Tools Aboleth: A minimal-overhead TensorFlow framework for Bayesian deep learning. Landshark: Command-line tools for large-scale spatial inference. Revrand: Scalable Bayesian generalised linear models with non-conjugate likelihoods. libcluster: Extensible C++ library for hierarchical Bayesian clustering. Scientific Awards Oral Presentation Award – ICML 2025 Workshop on Scaling up Intervention Models (SIMS) Oral Presentation Award – NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU) Oral Presentation Award – NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning Spotlight Paper Award – NeurIPS 2014 (Extended and Unscented Gaussian Processes) Research Team & Collaborations As Team Leader – Decisions & Statistical Learning at CSIRO Data61, Steinberg directs a multi-disciplinary group that partners with government agencies (e.g., Jobs and Skills Australia, Australian Institute of Health and Welfare) and industry to deploy machine-learning solutions at scale. He has previously held roles as Principal Researcher at Gradient Institute (2019-2023), Senior Research Engineer at CSIRO Data61 (2016-2019), Researcher at NICTA (2013-2016), and Research Associate at the University of Sydney (2012-2013).

Aruna Prasad SeneviratneView profile

Professor

• Cybersecurity and privacy
• Artificial intelligence
• Communications engineering
• +5 more

Professor Aruna Prasad Seneviratne serves as the Foundation Professor of Telecommunications at the University of New South Wales (Australia), where he holds the prestigious Mahanakorn Chair of Telecommunications. He is currently the Research Director for the Cyber Physical Systems Research Program within Data61, following the merger of NICTA with CSIRO. Previously, he directed the Australian Technology Park Laboratory of NICTA and led their Networked Systems research activities. Professor Seneviratne's research focuses on physical analytics - technologies enabling applications to interact intelligently and securely with their environment in real time. His recent work includes behavioral biometrics, wearable device optimization, and IoT system verification. His extensive publication record spans cybersecurity, artificial intelligence, communications engineering, and mobile technologies, with particular emphasis on integrated communications and sensing systems. His scholarly contributions include over 180 refereed technical papers and book chapters, reflecting his leadership in telecommunications and networked systems research. Professor Seneviratne's work demonstrates a consistent trajectory toward developing practical solutions for next-generation digital services and security frameworks. His scientific recognition includes prestigious fellowships at British Telecom and Telecom Australia Research Labs, underscoring his industry impact alongside academic contributions. Professor Seneviratne has supervised 30 PhD dissertations throughout his career, mentoring the next generation of telecommunications researchers. His leadership extends to directing major research initiatives at NICTA and Data61, where he has guided the development of new technologies for establishing trust, energy-efficient content storage, search, and distribution within digital economies. His laboratory work centers on the Cyber Physical Systems Research Program at Data61, where his team develops innovative approaches to secure and intelligent interaction between digital systems and physical environments.

Inigo AuzmendiView profile

Research Fellow

• Functional-structural plant modeling
• Horticultural crop physiology
• Agricultural systems
• +6 more

Dr. Inigo Auzmendi is a Research Fellow at the University of Queensland within the Faculty of Science , affiliated with the Queensland Alliance for Agriculture and Food Innovation and the ARC Centre of Excellence for Plant Success in Nature and Agriculture. His work focuses on functional-structural plant modeling to improve management practices in avocado, macadamia, and mango orchards through analysis of architecture, carbon balance, and environmental interactions. His research interests include: Biophysical processes in fruit trees (light interception, photosynthesis) Carbohydrate dynamics in grapevine growth Computational modeling of plant architecture and physiology Application of L-systems and L-Studio in horticultural research Recent publications emphasize modeling tree canopies to optimize yield through planting density and pruning strategies, with a strong focus on carbon allocation and multi-scale physiological interactions. Current projects involve supervising PhD and Honours students in computational approaches to orchard productivity and photosynthesis simulation. Dr. Auzmendi collaborates with multidisciplinary teams on projects funded by organizations including the Queensland Department of Agriculture and Fisheries and Horticulture Innovation Australia Limited.