جست‌وجوی استاد و پژوهشگر

Hassan Habibi Gharakheiliمشاهده پروفایل

دانشیار

• Programmable Computer Networks (SDN)
• Data-Driven Inference Systems (AI)
• Critical Networked Infrastructures (IoT)
• +۳ مورد دیگر

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.

• Wireless Communications
• Network Security
• Machine Learning
• +۲ مورد دیگر

Professor Teng Joon Lim is Deputy Dean and Associate Dean (Education) in the Faculty of Engineering at University of Sydney. His research in electrical engineering focuses on wireless communications, network security, and machine learning applications. Recent work develops deep learning approaches for RF power amplifier linearization and adversarial defense in IoT networks. His teaching includes scientific methods education and digital communication systems.

Nan Yeمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Statistics
• Optimization
• +۴ مورد دیگر

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.

• Multimedia Indexing and Search
• Social Data Analysis
• Knowledge Discovery
• +۳ مورد دیگر

Professor Helen Huang is a faculty member in the School of Electrical Engineering and Computer Science at The University of Queensland, where she serves as the Data Science Discipline Leader. She holds a BSc from Tsinghua University's Computer Science Department and a PhD from The University of Queensland's School of ITEE (2001 and 2007, respectively). Her research focuses on multimedia indexing, social data analysis, and knowledge discovery, with over 200+ publications in top-tier venues. Dr. Huang is an Associate Editor for The VLDB Journal, ACM Transactions on Information Systems, and Pattern Recognition Journal, and a member of the VLDB Endowment Board of Trustees. Her awards include the 2016 Chris Wallace Award for breakthroughs in Computer Science, the 2014 Women in Technology Research Award, and the 2018 Excellence in Higher Degree Research Supervision Award. She leads projects in data science, AI-driven systems, and interdisciplinary applications such as Auslan learning and material science modeling. Her work spans domains like recommender systems, domain adaptation, and graph learning, with contributions to both theoretical advancements and real-world applications. Dr. Huang’s publications reflect expertise in machine learning, computer vision, and data science, addressing challenges in multimedia, social networks, and interdisciplinary collaborations. She has advised numerous students and led grants in AI, domain adaptation, and educational technologies. Her leadership roles and editorial contributions highlight her impact on the academic community.

Richard Thomasمشاهده پروفایل

مدرس ارشد

• Software Engineering
• Cyber Security
• Curriculum and Pedagogy
• +۴ مورد دیگر

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 Gallagherمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Optimization
• +۶ مورد دیگر

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.

Hui Maمشاهده پروفایل

مدرس ارشد

• Electrical Asset Management
• Power Systems
• High Voltage Engineering
• +۵ مورد دیگر

Dr. Hui Ma is a Senior Lecturer in Power & Energy Systems at the University of Queensland, affiliated with the School of Electrical Engineering and Computer Science. His expertise spans Electrical Asset Management, Power Systems, and High Voltage Engineering. He holds a PhD from the University of Adelaide and has been at UQ since 2008. Education: B.Eng and M.Eng from Xi'an Jiaotong University; M.Eng (Research) from Nanyang Technological University; PhD from the University of Adelaide. Research focuses on improving electricity network visibility through modeling, sensing, and signal processing, alongside data mining for renewable-integrated grids. Key areas include transformer condition monitoring, insulation diagnostics, and smart grid technologies. He edits IEEE Transactions on Power Delivery and participates in IEEE Smart Grid initiatives. Recent work addresses PV forecasting, low-frequency grid instability, and transformer aging analysis. His publications emphasize machine learning applications in energy systems and fault diagnosis. Collaborations involve Australian utilities and industry partners.

• Predictive Analytics
• Recommendation Systems
• Graph Learning
• +۴ مورد دیگر

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 Baiمشاهده پروفایل

مدرس ارشد

• Renewable Energy Integration
• Power Systems
• Smart Grid Technology
• +۴ مورد دیگر

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 Eghbalمشاهده پروفایل

دانشیار مدعو

• Distributed Energy Resources Integration
• Intelligent Grids
• Renewable Energy Systems
• +۲ مورد دیگر

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.

Jessica Leeمشاهده پروفایل

مدرس

• Cognitive Psychology
• Learning and Memory
• Associative Learning
• +۳ مورد دیگر

Jessica Lee is a Lecturer in the School of Psychology at the University of Sydney, focusing on cognitive processes related to learning and generalization. Her research integrates experimental, psychophysiological, and computational methods to explore how low-level associative mechanisms interact with higher-order reasoning. She holds a PhD from the University of Sydney and previously worked as a Postdoctoral Research Fellow and ARC DECRA Fellow at the University of New South Wales. Education: BSc, Honours, and PhD in Psychology (University of Sydney); Postdoctoral Research Fellow (UNSW, 2016–2020); ARC DECRA Fellow (UNSW, 2021). Research interests include individual differences in learning styles, rule-based generalization, and the neural underpinnings of associative processes. Her work has been recognized with awards such as the APS Rising Star and ARC DECRA. Key grants include 2023 New Academic Hire Startup Funding and 2020 DECRA award. She contributes to open science initiatives via GitHub repositories (e.g., allergist-task, Bayesian-curve-fitting) and collaborates with organizations like R-Ladies Sydney. Media coverage highlights her work on punishment insensitivity and caffeine withdrawal.

• Marine Carbonate Sedimentology
• Climate Change
• Ocean Sustainability
• +۳ مورد دیگر

Dr. Claire Reymond is a Postdoctoral Research Fellow at the School of Life and Environmental Sciences, The University of Sydney, specializing in marine carbonate sedimentology, climate change, and ocean sustainability. She holds a PhD in Marine Palaeoecology from the University of Queensland (2011) and a BSc in Conservation and Biodiversity from Macquarie University (2005). Her research integrates geological records and modern environmental data to inform marine management strategies. Reymond has authored over 30 peer-reviewed publications in top-tier journals like Science Advances , Global Change Biology , and Earth-Science Reviews . She led the 2022 Special Issue on Tropical Large Benthic Foraminifera for the Journal of Earth Science . Current projects include Australian marine jurisdiction studies with Geosciences Australia and the Sustainable Ocean Science initiative by the Sydney Environment Institute. Her research interests span marine carbonate dynamics, tropical island stabilization, and climate change impacts on marine ecosystems. She is affiliated with the Sydney Institute of Marine Science and the EU Ocean Governance COST Action. Awards include the SEI Collaborative Grant (2023) and the IOI Ocean Impact Award (2022). Labs/Teams: Member of the Geocoastal Research Group and the Sydney Environment Institute. Collaborates internationally on projects such as coral reef resilience and tropical foraminifera ecology.

• Astrophysics
• Cosmology
• Gravitational Lensing
• +۳ مورد دیگر

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.

• Hydrology
• Catchment Management
• Sustainable Water Management
• +۴ مورد دیگر

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.

• Logistics
• Supply Chain Management
• Transportation
• +۴ مورد دیگر

Andrew Collins is a Senior Lecturer in Logistics and Supply Chain Management at the University of Sydney Business School's Institute of Transport and Logistics Studies (ITLS), serving as discipline coordinator for the Global Logistics specialization in the Master of Commerce program. He holds a BSc (Computer Science), BA (Philosophy) from UNSW and a PhD from the University of Sydney, where he earned the Eric Pas Dissertation Prize for his doctoral work on discrete choice models. His research focuses on behavioral influences in logistics, supply chain management, and transport systems, particularly exploring how decision-making processes affect environmental outcomes and consumer choices. Key areas include attribute nonattendance in choice models, sustainability impacts of collection/delivery points, and the role of information presentation in travel decisions. Recent work examines equitable distribution of bicycle infrastructure benefits, privacy implications of IoT technologies, and autonomous vehicle adoption trends. He has published over 40 peer-reviewed articles and presented at major conferences such as the International Conference on Travel Behavior Research. Collins has secured research funding through grants like the 2005 'Stated Choice Approach to Measure Social Costs of Aircraft Noise' and 2004 'Disaggregate Assessments of Population Exposure to Aircraft Noise' projects. His teaching expertise spans logistics strategy, supply chain analytics, and transport economics.