Find Academic Supervisors

• Data science in smart grids
• Electricity markets for renewable-rich power systems
• Network modelling/planning for renewable integration
• +3 more

Dr. Jiajia Yang is a Senior Lecturer in Renewable Energy and Electrical Engineering at James Cook University (JCU), Australia. He holds a Ph.D. from the University of New South Wales (2018) and has extensive experience in power system modeling, renewable integration, and energy markets. Prior to JCU, he worked as a Senior Research Associate at UNSW and a Senior Power System Engineer in industry projects like the Renewable Energy Zone (REZ) programs. Education: B.E., Northeast Electric Power University, China (2011) M.E., Zhejiang University, China (2014) Ph.D., University of New South Wales, Australia (2018) Research Interests: Data science in smart grids Electricity markets for renewable-rich systems Localized/P2P trading mechanisms Hydrogen economy Recent Research Trends: Dr. Yang’s work focuses on decarbonizing power systems through innovative market designs, renewable integration, and cybersecurity. His 2024 studies emphasize hydrogen credit frameworks and carbon-neutral energy systems, while earlier projects address P2P trading and grid stability under high renewables. Awards: Outstanding Editorial Board Member (2023) Best Research Paper Award (2018) Advising & Grants: He coordinates teaching modules like Power Engineering 1/2 and leads the 2024 International Conference on Energy and Environment Engineering. His research is supported by projects like the North Queensland Transmission Grid Planning initiative (2024–2027). Labs/Teams: Engaged in IEEE PES roles, including IEEE Transactions on Industry Applications editorial work and chairing multiple conferences.

Hassan Habibi GharakheiliView profile

Associate Professor

• Programmable Computer Networks (SDN)
• Data-Driven Inference Systems (AI)
• Critical Networked Infrastructures (IoT)
• +3 more

Associate Professor Hassan Habibi Gharakheili holds a PhD from UNSW Sydney (2015) and BS/MS degrees from Sharif University of Technology (Iran). He is affiliated with the School of Electrical Engineering and Telecommunications at UNSW, focusing on programmable networks (SDN), IoT security, and data-driven systems. His research addresses challenges in enterprise cybersecurity, IoT device behavior analysis, and smart infrastructure optimization. Education: PhD (UNSW), MS/BS (Sharif University) Affiliations: UNSW Sydney, Editorial Board of IEEE TMC Research interests include SDN-based traffic management, IoT anomaly detection, and network policy enforcement. Key contributions include work on MUD profiles for IoT security, DNS encryption analysis, and UAV-assisted data collection. Over 80 peer-reviewed publications across journals/conferences, with patents on network security and IoT monitoring. Advises students like Shuai Zhang. Active in IEEE/ACM conferences and industry collaborations.

• Materials Science
• Semiconductor Physics
• Solar Energy
• +2 more

Zhongshu Yang is a Research Fellow at the Australian National University's College of Engineering and Computer Science, specializing in advanced materials for energy applications. His research focuses on silicon-based solar technologies, defect engineering in semiconductors, and thin-film passivation techniques. With expertise spanning materials characterization and nanofabrication, he investigates recombination mechanisms in silicon wafers, gettering processes for impurity control, and CVD growth of 2D heterostructures. His recent work demonstrates strong emphasis on improving photovoltaic efficiency through fundamental materials research, particularly addressing iron contamination issues and developing novel passivating contact structures. Publications frequently combine experimental validation with computational modeling to elucidate kinetic mechanisms in semiconductor processing.

Matthew HoldenView profile

Senior Lecturer

• Biodiversity Conservation
• Theoretical Ecology
• Fisheries Management
• +3 more

Dr. Matthew Holden is a Senior Lecturer in Mathematics at the University of Queensland (UQ), affiliated with the Centre for Biodiversity and Conservation Science (CBCS) and the Centre for Marine Science (CMS). He holds a Doctoral Diploma in Applied Mathematics from Cornell University. His research focuses on applying mathematical tools to improve environmental outcomes, particularly in biodiversity conservation, fisheries management, and natural resource modeling. He currently serves as Vice President of the Resource Modeling Association and Deputy Director of Research for CBCS. Holden's work bridges theoretical ecology and practical conservation, emphasizing the use of optimal control theory, dynamical systems, and statistical methods. Key areas include sustainable fisheries strategies, marine protected area effectiveness, and mitigating biodiversity loss through evidence-based policies. He collaborates with institutions like the Queensland Department of Agriculture and Fisheries and the Australian Fisheries Management Authority. His educational background includes a PhD from Cornell University, advised by Dr. Stephen Ellner, with postdoctoral experience in applied mathematics. Holden has contributed to high-impact publications in Ecology Letters , Biological Conservation , and Nature Sustainability , focusing on topics like invasive species management, data-limited fisheries, and sustainability trade-offs in global food systems. Holden advocates for rigorous environmental policies in academic conferences and actively engages in science communication through platforms like The Conversation . He is available for student supervision and media commentary on conservation science and environmental decision-making.

Hugh PossinghamView profile

Research Fellow

• Biodiversity Conservation
• Marine Conservation
• Environmental Policy
• +4 more

Professor Hugh Possingham is a VC Senior Research Fellow at the University of Queensland's School of the Environment. His work focuses on systematic conservation prioritization , spatial planning , and global biodiversity policy . He develops tools like the prioritizr R package to optimize conservation investments. His research addresses urgent challenges such as protected area effectiveness, climate adaptation, and species recovery strategies. Key areas of expertise include marine conservation leadership , cost-efficient conservation , and policy implementation . He has contributed to frameworks like Essential Life Support Areas (ELSAs) and advised governments on biodiversity targets. Recent work examines human behavior's role in conservation decisions and evaluates the impact of industrial activities on biodiversity. Education: Not explicitly stated in text Grants/Projects: Leads multidisciplinary projects on climate-smart conservation and protected area design His lab produces actionable science for policymakers, emphasizing evidence-based decision-making. Key collaborations involve international organizations and government agencies to align conservation efforts with global frameworks like the Kunming-Montreal Agreement.

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

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

Nan YeView profile

Senior Lecturer

• Machine Learning
• Statistics
• Optimization
• +4 more

Dr. Nan Ye is a Senior Lecturer in Statistics and Data Science at the University of Queensland's School of Mathematics and Physics. His research focuses on machine learning, statistics, and optimization, with contributions to sequential decision making, weakly supervised learning, and probabilistic graphical models. He holds a PhD in Computer Science from the National University of Singapore (NUS) and double first-class honors in Computer Science and Applied Mathematics from NUS. Previously, he held postdoctoral positions at QUT, UC Berkeley, and NUS. Dr. Ye teaches advanced courses such as STAT3007/7007 Deep Learning, covering topics from foundational machine learning to state-of-the-art deep learning architectures and applications. His work has been published in top venues like NeurIPS, ICML, and UAI, earning awards including the IJCAI-JAIR Best Paper Prize (2022) and UAI Best Student Paper Award (2014). His research interests span theoretical and applied machine learning, including reinforcement learning, optimization algorithms, and their applications in fields like healthcare and environmental science. He actively supervises students in these areas and collaborates on interdisciplinary projects. Dr. Ye's academic profile includes extensive contributions to open-source tools and educational materials, reflecting his commitment to advancing both research and pedagogy in data science.

Richard ThomasView profile

Senior Lecturer

• Software Engineering
• Cyber Security
• Curriculum and Pedagogy
• +4 more

Richard Thomas is a Senior Lecturer and Director of International at the School of Electrical Engineering and Computer Science, part of the Faculty of Engineering, Architecture and Information Technology at The University of Queensland. His academic career spans over three decades, focusing on software engineering education and innovative teaching methodologies. He has held roles including Director of International and has contributed to curriculum design, assessment frameworks, and interdisciplinary collaboration in computing education. His research interests bridge software engineering and pedagogy, emphasizing practical industry integration, student engagement, and global education models. Notable contributions include frameworks for software development education, fault prediction systems, and the use of agile methodologies in teaching. He has published extensively in journals like IEEE Transactions and conferences such as ACM Transactions on Computing Education. Thomas has authored textbooks on software architecture, with editions published in 2022 and 2023, reflecting his commitment to modernizing educational resources. His work on 'Haery' (a Hadoop-based big data system) and energy efficiency optimization highlights his technical contributions to software systems research. He has presented globally, including a 2024 talk on AI-driven entrepreneurship education for sustainability. His research consistently integrates technology advancements with educational innovation, aiming to enhance both student outcomes and industry relevance.

Marcus GallagherView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Optimization
• +6 more

Associate Professor Marcus Gallagher is part of the School of Electrical Engineering and Computer Science at The University of Queensland. His research focuses on artificial intelligence, optimization, and machine learning, with an emphasis on algorithm performance benchmarking and cross-disciplinary applications such as healthcare and cybersecurity. He holds a PhD from The University of Queensland (2000) and degrees from the University of New England (BCompSc, 1994; GradDipSc, 1995), as well as a GradCert in Higher Education (2010). His work involves collaborations across domains like network intrusion detection, opioid prescribing analysis, and evolutionary algorithms for game AI. He has published extensively in journals and conferences, including IEEE Transactions on Games, Information Security and Applications, and Evolutionary Computation. His research explores topics such as clustering optimization, data-driven problem benchmarking, and interpretable machine learning policies. Recent studies include analyzing synthetic vs real-world network intrusion datasets and developing graph neural networks for Android malware classification. He also leads initiatives like the IJCNN 2023 Competition on Reproducibility and Loss Landscape Mapping in Neural Networks. His contributions span theoretical advancements and practical applications in AI-driven problem-solving.

• Predictive Analytics
• Recommendation Systems
• Graph Learning
• +4 more

Professor Hongzhi Yin is a leading academic at The University of Queensland, Australia, serving as a Professor and director of the Responsible Big Data Intelligence Lab (RBDI). His research focuses on predictive analytics, recommendation systems, graph learning, and decentralized intelligence. He holds an ARC Future Fellowship and has received numerous awards, including the 2023 Young Tall Poppy Science Award and multiple best paper awards. His work spans machine learning, data mining, and social media analysis, with over 350 publications and an H-index of 83. He advises PhD students and leads collaborative projects in trustworthy AI, federated learning, and privacy-preserving systems. Education: Completed his PhD at Peking University, receiving recognition for his thesis. Research contributions include foundational work on graph condensation, federated recommendation systems, and generative AI trustworthiness. He serves as an editor for top journals like IEEE Transactions on Knowledge and Data Engineering and ACM Transactions on Information Systems . Awards and Honors: ARC Future Fellowship, Young Tall Poppy Science Award (2023), AI 2000 Most Influential Scholar Honorable Mention (2022-2024), and eight international best paper awards. He is also a recognized leader in data mining and computer science, ranked #52 in Australia's Best Scientists 2025. Research Interests: Trustworthy recommendation systems, graph neural networks, federated learning, and privacy-preserving AI. His lab develops methods for robust graph condensation, secure decentralized systems, and ethical AI applications. Recent work includes surveys on point-of-interest recommendation and graph condensation techniques. Labs/Teams: Leads the Responsible Big Data Intelligence Lab (RBDI), fostering innovation in data science and AI ethics. Collaborates globally with institutions like MIT, Stanford, and Carnegie Mellon on foundational AI research.

Feifei BaiView profile

Senior Lecturer

• Renewable Energy Integration
• Power Systems
• Smart Grid Technology
• +4 more

Dr. Feifei Bai is a Senior Lecturer at the School of Electrical Engineering and Computer Science, University of Queensland (UQ), and an adjunct Senior Research Fellow at Griffith University. She holds an Advance Queensland Fellowship (2018) and has secured over $4.6M in research grants. Her research focuses on renewable energy integration, power system stability, and smart grid technologies. She collaborates with industry partners like Energy Queensland and Powerlink, and her work has led to commercialized patents and an Australian Engineering Excellence Award (2020). Education: PhD in Engineering from Southwest Jiaotong University. Active editorial roles include Associate Editor for IET Generation, Transmission & Distribution and Assistant Editor for International Journal of Green Energy. Research Interests: Renewable energy systems, synchrophasor applications, power system oscillation control, and cybersecurity. Her work bridges theoretical advancements with practical industry solutions, emphasizing grid resilience and efficient energy management. Publications: Over 75 journal/conference papers (2025 highlights include EV charging optimization, microgrid scheduling, and Bayesian frequency modeling). Her research trends emphasize smart grid innovation, renewable integration challenges, and advanced control strategies for high-renewable grids. Awards: Advance Queensland Fellow (2018), Australian Engineering Excellence Award (2020). Grants & Industry: $4.6M in funded projects. ARENA-backed research on EV charging infrastructure received national recognition. Active in industry partnerships for technology deployment. Labs/Teams: Leads the Power & Energy Systems Research Group at UQ, contributing to initiatives like the Flexible Urban Energy Systems special issue (2021).

Daniel EghbalView profile

Adjunct Associate Professor

• Distributed Energy Resources Integration
• Intelligent Grids
• Renewable Energy Systems
• +2 more

Dr. Daniel Eghbal is an Adjunct Associate Professor at the University of Queensland's School of Electrical Engineering and Computer Science, with additional responsibilities as Manager of Future Network Strategy at Energy Queensland. His research focuses on integrating distributed energy resources into power grids and developing smart grid solutions. He holds a B.S. in Electrical Engineering from Ferdowsi University of Mashhad, an M.S. in Power Systems from Tarbiat Modares University, and a Ph.D. in Artificial Complex System Engineering from Hiroshima University. Research interests center on renewable energy integration, grid modernization, and distributed energy systems. His work bridges industry innovation with academic research through collaborations with Power & Energy Systems Research Division. Publications emphasize renewable grid integration, power system optimization, and photovoltaic system performance, with consistent focus on practical solutions for high-penetration renewable scenarios. Fellow of Engineers Australia IEEE Senior Member Chartered Engineer (CPEng, RPEQ, NER) Advises industry projects and contributes to ARC and ARENA grant applications. Collaborates with IEEE Queensland Section and UQ research teams on grid transition strategies.

• Astrophysics
• Cosmology
• Gravitational Lensing
• +3 more

Dr Ellen Sirks is an academic researcher affiliated with the School of Physics at the University of Sydney. Her work focuses on astrophysical phenomena such as dark matter dynamics, gravitational lensing, and high-altitude astronomical observations. She contributes to projects like the SuperBIT (Super Pressure Balloon-borne Imaging Telescope), advancing stratospheric telescope technology and data retrieval systems. Her research integrates computational simulations with observational data, particularly in galaxy cluster mergers and self-interacting dark matter studies. Recent work includes analyzing weak gravitational lensing effects and improving balloon-borne instrument performance. She has published extensively in journals like Monthly Notices of the Royal Astronomical Society and the Astronomical Journal. Sirks collaborates on international initiatives involving NASA and academic consortia, emphasizing next-generation telescope design and data analysis techniques. Her contributions bridge theoretical astrophysics with engineering solutions for observational astronomy.

• Ecology
• Entomology
• Agricultural Science
• +7 more

Dr. Tom Swan is a Postdoctoral Research Fellow at the University of Sydney's School of Life and Environmental Sciences. His work focuses on interdisciplinary approaches to global challenges in agriculture, ecology, and public health. He holds a PhD in Ecology from James Cook University (2022) and has extensive field experience in mosquito control, soil management, and community-driven research initiatives. Current projects include ACIAR-funded research optimizing soil management strategies for PNG cocoa farmers. He is affiliated with the Sydney Environment Institute and Sydney Institute of Agriculture. His research integrates entomology, genomics, and ecology to address issues like vector-borne disease prevention and food security. Research interests span mosquito ecology, soil security linkages to public health, and participatory approaches with Indigenous communities. He has contributed to over 15 peer-reviewed publications since 2016, focusing on topics ranging from mosquito population genomics to innovative soil testing technologies. Key awards include the 2015 Fulbright New Zealand Science & Innovation Award and the 2022 James Cook University Medal. He collaborates internationally, having worked in Australia, Peru, Indonesia, and the Pacific Islands. Current supervision includes a PhD candidate studying coffee management systems in PNG.

• Hydrology
• Catchment Management
• Sustainable Water Management
• +4 more

Willem Vervoort is a Professor in Hydrology and Catchment Management and Director of the ARC Training Centre in Data Analytics for Resources and Environments (DARE) at the University of Sydney. He holds academic affiliations with the School of Life and Environmental Sciences and is a member of the Sydney Environment Institute, Sydney Institute of Agriculture, Sydney Southeast Asia Centre, and The Net Zero Institute. His expertise lies in quantitative hydrology, sustainable water management, and climate change impacts. Vervoort earned a PhD in field hydrology from the University of Georgia and an Agricultural Engineering degree from Wageningen University in Soil Conservation. His research focuses on developing sustainable water management strategies, particularly in agricultural contexts, leveraging interdisciplinary approaches. Key areas include groundwater dynamics, catchment functionality, and the integration of remote sensing and machine learning for hydrological modeling. Recent work emphasizes climate adaptation, afforestation impacts, and water market functionality in Australia and globally. Grants : Includes leadership in Horizon 2020-funded projects on remote sensing for agricultural water management and GRDC-funded initiatives on frost and heat management analytics. Labs/Teams : Directs the DARE Centre and collaborates with international teams in Uruguay, Fiji, and Italy, advancing SWAT model applications and hydrological forecasting. Publications span hydrological processes, climate extremes, and eco-hydrological interactions, with a focus on scaling models for policy and decision-making. His work bridges academic research with practical solutions for environmental sustainability and resource management.