جست‌وجوی استاد و پژوهشگر

Kerry Taylorمشاهده پروفایل

دانشیار

• Ontologies
• Semantic Web
• Linked Data
• +۵ مورد دیگر

Kerry Taylor is an Associate Professor (Data Science) at the School of Computing, Australian National University (ANU). She holds visiting roles at the University of Surrey (UK) and University of Melbourne. Her career spans 20 years at CSIRO, UN big data projects with ABS, and interdisciplinary research in data management, IoT, and semantic technologies. She lectures in data mining and convenes ANU's postgraduate applied data analytics programs. Education includes a BSc (Hons 1) in Computer Science from UNSW (1983) and a PhD in Computer Science and Technology from ANU (1996). She co-chaired the W3C/OGC Spatial Data on the Web working group (2015-2017) and serves on editorial boards for Knowledge-Based Systems and International Journal of Distributed Sensor Networks . Research focuses on ontologies, semantic web, machine learning in IoT, and spatial data systems. Active projects include government information frameworks, distributed IoT facilities, and sensor data integration. Her work emphasizes interdisciplinary applications of logic-based and semantic approaches to data challenges.

George Buchananمشاهده پروفایل

استاد

• Human-Computer Interaction
• Digital Libraries
• Information Retrieval
• +۵ مورد دیگر

Professor George Buchanan is a leading researcher in human-computer interaction and digital libraries at RMIT University . His work bridges information science, digital humanities, and health informatics, focusing on usability in sensitive contexts like healthcare and misinformation. Deputy Dean, Research at RMIT University Former Director, University of Melbourne iSchool Research Interests: Digital information interaction Health and aging informatics Disinformation analysis Mobile interface design Digital library systems Key Contributions: Developed mobile web usability benchmarks, spatial hypertext tools, and thermal feedback interfaces. Currently seeking PhD students for 2025 projects on digital browsing and view change dynamics. Awards: Over twenty best paper awards and Honorary Life Fellow of the Royal Society of Arts. Advising: Accepting Masters/PhD supervision in information interaction and digital health domains.

• Cybersecurity and privacy
• Privacy protection
• AI and machine learning
• +۴ مورد دیگر

Dr. Bo Liu is an Associate Professor in the School of Computer Science at the University of Technology Sydney (UTS), where he serves as a core member and director of the AI Security and Privacy (AISP) Research Lab at the Australian Artificial Intelligence Institute (AAII). With expertise spanning cybersecurity, privacy protection, AI and machine learning, and wireless communications, Dr. Liu has established himself as a leading researcher in the field of AI security and privacy. Dr. Liu earned his PhD from the Department of Electronic Engineering at Shanghai Jiao Tong University in 2010. His academic journey at UTS has progressed from Senior Lecturer (November 2019-December 2022) to his current position as Associate Professor (January 2023-present). Dr. Liu's research focuses on the critical intersection of artificial intelligence and security, particularly addressing emerging threats in the age of advanced AI systems. His work spans multiple dimensions of security and privacy, including deepfake detection, privacy-preserving data synthesis, AI model security, and fair machine learning. He has pioneered approaches to detect AI-generated content, protect visual privacy through de-identification techniques, and address the complex relationship between algorithmic fairness and privacy preservation. His publication record demonstrates significant contributions across multiple cutting-edge research areas, with particular emphasis on detecting and mitigating threats from generative AI systems. His recent work reveals a strong focus on deepfake detection across multiple modalities (images, video, and audio), privacy-preserving techniques for sensitive data, and the security implications of emerging AI architectures like Retrieval-Augmented Generation systems. Dr. Liu has secured substantial research funding, including as Lead Chief Investigator on multiple ARC Discovery and Linkage Projects, totaling over $3.5 million AUD. His industry collaborations include partnerships with the NSW Department of Planning and the Reserve Bank of Australia, demonstrating the practical applicability of his research. As an academic leader, Dr. Liu serves as Associate Editor for IEEE Transactions on Broadcasting and actively contributes to the academic community through conference organization, peer review for top-tier venues, and assessment for ARC grant schemes. He also teaches courses including Penetration Testing, Ethical Hacking and Offensive Security, and supervises Masters and PhD students in cybersecurity and privacy research.

• Graph Analytics
• Graph Mining
• Graph Algorithms
• +۳ مورد دیگر

Dr. Lijun Chang is an Associate Professor in the School of Computer Science at the University of Sydney. He holds an ARC Future Fellowship (2019–2022) and an ARC DECRA Fellowship (2015–2017). Previously, he was at the University of New South Wales. His research focuses on graph analytics, mining, algorithms, and network science. He teaches courses like INFO5011 (Competitive Programming), COMP5313 (Large Scale Networks), and COMP9120 (Database Management Systems), and coaches the USYD Programming Competition Teams. Education: B.Eng. in Computer Science & Technology from Renmin University of China; PhD from the Chinese University of Hong Kong. Research highlights include scalable graph processing systems (e.g., ScaleG), densest subgraph detection, and graph similarity search. He leads projects funded by ARC grants such as 'Advanced Search of Cohesive Subgraphs in Big Graphs' (2018) and 'Directionality-Aware Cohesive Subgraph Search' (2022). His work emphasizes efficient algorithms for large-scale networks and graph databases. Awards : ARC Future Fellow, ARC DECRA Fellow Students : Yu KONG, Rashmika MATHTHAKA GAMAGE, Mouyi XU Labs/Teams : Focuses on graph algorithms and systems research, contributing to open-source tools and large-scale network analysis.

• Business Process Management
• Process Mining
• Explainable AI
• +۵ مورد دیگر

Chun Ouyang is a Professor at Queensland University of Technology (QUT) in the School of Computer Science within the Faculty of Science. With an extensive publication record spanning over two decades from 2002 to 2025, Professor Ouyang has established themselves as a leading researcher in Business Process Management, Process Mining, and Explainable AI. Their work bridges theoretical foundations with practical applications across healthcare, finance, and industrial sectors. Professor Ouyang's research interests primarily focus on Business Process Management systems, Process Mining techniques, Explainable Artificial Intelligence, and Healthcare Process Analysis. Their work has evolved from foundational BPMN/BPEL translation research in the early 2000s to sophisticated process mining approaches in the 2010s, and most recently to cutting-edge Explainable AI applications in clinical and business contexts. They have developed novel methodologies for process querying, predictive process analytics, and XAI evaluation frameworks that have significantly advanced the field. Their research consistently emphasizes practical applicability while maintaining strong theoretical foundations, with publications in top-tier journals and conferences including IEEE Transactions, Springer journals, and major BPM conferences. Analysis of Professor Ouyang's recent publications (2023-2025) reveals a strategic research trajectory that integrates traditional process mining with modern AI techniques, particularly focusing on explainability and trustworthiness. Their work demonstrates a consistent pattern of addressing real-world challenges through rigorous methodological development, with increasing emphasis on healthcare applications, clinical decision support systems, and the ethical implications of AI deployment. The publications show strong interdisciplinary collaboration patterns, particularly with medical researchers and industry partners. Professor Ouyang has mentored numerous PhD students and early-career researchers who have gone on to establish themselves in the BPM and AI communities. Their research group at QUT has secured multiple competitive grants supporting innovative work in process analytics and AI. They maintain active collaborations with leading researchers globally, including Catarina Pinto Moreira, Arthur ter Hofstede, and Moe Wynn. Professor Ouyang leads the Process Analytics Research Group at QUT, which focuses on developing advanced techniques for business process analysis, prediction, and optimization. The group maintains strong industry connections with healthcare providers, financial institutions, and government agencies, ensuring their research has practical impact. Current projects include developing trustworthy AI systems for clinical decision support, cross-organizational process analysis frameworks, and next-generation process mining techniques for complex, distributed systems.

Guido Zucconمشاهده پروفایل

استاد

• Information Retrieval
• Health Search
• Formal Models of Search
• +۱۵ مورد دیگر

Guido Zuccon is a Professorial Research Fellow at the School of Electrical Engineering and Computer Science , The University of Queensland (UQ), where he leads the Information Engineering Lab (ielab) . He serves as the AI Director for the Queensland Digital Health Centre (QDHeC) and is an Affiliate Professor at the UQ Centre for Health Services Research . He was previously a Lecturer and Senior Lecturer at Queensland University of Technology and a Postdoctoral Fellow at CSIRO. His research spans Information Retrieval , Health Search , Formal Models of Search , and Health Data Science , with a strong focus on consumer health search, cohort identification, clinical decision support, and systematic review automation. He has pioneered work on search interaction, semantic models, and the evaluation of retrieval systems in health contexts. His recent publications highlight a strong trend toward leveraging large language models (LLMs) for zero-shot retrieval, federated search, dense retrieval, and query formulation. His work integrates advanced neural methods with practical applications in healthcare, including systematic review automation and clinical AI. He frequently publishes at top venues such as SIGIR, ECIR, and WSDM, often in collaboration with key researchers like Bevan Koopman, Shengyao Zhuang, and Harry Scells. ARC DECRA Fellow (2018–2020) Best Paper Awards at AIRS 2017, CLEF 2016, ALTA 2015, ECIR 2012 Best Reviewer Award at ECIR 2014 Principal Investigator on ARC Discovery Projects and MRFF grants Guido Zuccon actively supervises a large cohort of PhD students, primarily in areas related to neural information retrieval, health search, and systematic review automation. He has led significant research projects funded by the ARC, Google, Microsoft, GRDC, and CSIRO. He is a key organizer of international evaluation labs such as the CLEF eHealth Consumer Health Search task and the TREC 2019 Decision Track. He leads the ielab , a vibrant research group focused on information retrieval and data science, and contributes to major open-source initiatives like Big Brother , a tool for logging user interactions in web studies.

• Data Science
• Machine Learning
• Natural Language Processing
• +۱۲ مورد دیگر

Xiuzhen Jenny Zhang is a Professor of Data Science at RMIT University , affiliated with the School of Computing Technologies. Her research bridges artificial intelligence, machine learning, and social media analysis, with a focus on text mining and trustworthy data science. Her recent publications highlight expertise in point-of-interest recommendation , misinformation detection , multi-task learning , and transformer-based NLP . Key trends include applications of large language models for social good, fairness in recommendation systems , and adversarial learning for robustness. Best Paper Award at TrustCom’12 Best Short Paper Award at ADCS’2009 She leads the Text And LanguagE (TALE) research group and has supervised over 20 PhD students. Research grants include Australian Research Council and Victoria state government funding.

• Cybersecurity
• Data Security and Privacy
• Context-Aware Access Control
• +۵ مورد دیگر

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.

Xiaoyang Wangمشاهده پروفایل

مدرس ارشد

• Graph Processing
• Graph Neural Networks
• Spatial Data Processing
• +۱۰ مورد دیگر

Xiaoyang Wang is a Senior Lecturer in the School of Computer Science and Engineering (CSE) at the University of New South Wales (UNSW). He holds a Bachelor's and Master's degree in Computer Science from Northeastern University, China, and earned his PhD from CSE UNSW. Dr. Wang's research focuses on database systems with a special emphasis on query processing and data mining on large-scale graph, spatial, and streaming data. His expertise extends to data-driven machine learning, smart contract analysis on blockchain, and FinTech with financial network analysis. His work spans Graph Processing, Graph Neural Networks, Spatial Data Processing, AI for Databases (AI4DB), Database for AI (DB4AI), and FinTech applications. His publication record shows significant contributions to the field with 7 book chapters, 56 journal articles, 61 conference papers, 7 edited conference proceedings, and 4 conference abstracts. Recent publications (2022-2025) demonstrate his strong research trajectory in advanced graph processing techniques, neural network applications, and innovative database approaches. Key themes include hierarchical contrastive learning, robust attack frameworks, temporal graph processing, influence maximization, knowledge graph-enhanced reasoning, and rumor mitigation. Dr. Wang actively recruits PhD students interested in pursuing research in related fields and encourages current undergraduate and master's students at UNSW to contact him about research opportunities. He maintains an active research agenda with practical implications for industries dealing with large-scale network data, financial technology applications, and data-intensive systems. He can be reached at xiaoyang.wang1@unsw.edu.au and is located in Engineering building K17-501D at UNSW.

• Database Management
• Spatial-Textual Data Processing
• Heterogeneous Data Usability
• +۶ مورد دیگر

Professor Zhifeng Bao is a faculty member at RMIT University's School of Computing Technologies. His research focuses on enhancing data usability across heterogeneous domains, including structured, unstructured, and spatial-temporal data. His work spans database management, keyword search optimization, social network analysis, and spatio-textual data processing. He coordinates the course COSC1169: Intranet and Internet Data Engineering and supervises PhD/Masters students in projects such as trajectory data processing, data asset valuation, and edge computing optimization. Research interests emphasize improving data accessibility and efficiency through methodologies like query relaxation, visual analytics, and provenance tracking. His recent projects include cost-effective edge node placement, traffic accident risk prediction, and differentially private federated learning. Teaching and supervision activities highlight a commitment to bridging theory and practical data engineering challenges.

Robin Carpentierمشاهده پروفایل

پژوهشگر ارشد

• Data Privacy
• Information Management
• Hardware Security
• +۳ مورد دیگر

Robin Carpentier is a Research Fellow at the School of Computing, Macquarie University . His work focuses on Data Privacy , Information Management , and Hardware Security , particularly in developing secure personal data management systems using Trusted Execution Environments (TEE) and SGX technology. Research Interests : Secure data processing with third-party code Privacy-preserving computation frameworks Hardware-based security for databases Dimensionality challenges in text privacy Resource-constrained privacy-preserving methods Recent Publications Trends : Robin's research over the past decade has explored data leakage mitigation, TEE-optimized database operations, and privacy-preserving mechanisms for large-scale data applications. His 2024 work extends these principles to secure AI/LLM interactions and advanced text privacy techniques.

• Computational Logic
• Knowledge Representation
• Artificial Intelligence
• +۵ مورد دیگر

Professor Kewen Wang is a faculty member in the School of Information and Communication Technology at Griffith University , where he has been actively working in computational logic, knowledge representation, and their applications in artificial intelligence for over 30 years. His research has led to the development of novel logics, computer languages, and systems for knowledge representation and reasoning. Broad research areas: Computational Logic, Knowledge Representation, Artificial Intelligence, Programming, Knowledge Graphs, Explainable AI, Ontology Reasoning, Rule Learning Key contributions: RLvLR (scalable rule learner for KGs), TyRuLe (typed rule learning), Drewer (Datalog+- query engine), ALBERT+CCR (CommonsenseQA model), auction design algorithms His research has been widely cited in top venues like Artificial Intelligence , JAIR , ACM Transactions on Computational Logic , and conferences AAAI (Area Chair 2022-2025), IJCAI , KR . He has secured six ARC Grants (five Discovery, one Linkage) and smaller grants from NICTA, CSIRO, and Griffith University. Editorial roles: Area Chair for AAAI (2022-2025), Associate Editor for Journal of Web Semantics , TGDK Supervision: Has supervised over 15 PhD students including Hong Wu, Peng Xiao, and Pouya Omran Teaching: Courses in Intelligent Systems, Data Structures, Discrete Mathematics, and Robotics

Natasha Fernandesمشاهده پروفایل

مدرس ارشد

• Data Privacy
• Differential Privacy
• Privacy-Preserving Machine Learning
• +۳ مورد دیگر

Natasha Fernandes is a Senior Lecturer in the School of Computing at Macquarie University. She holds a PhD in Computing from Macquarie University and École Polytechnique (France), and an undergraduate degree in Pure Mathematics and Computer Science from the University of Sydney. Her roles include involvement in the Data Horizons Research Centre and Future Communications Research Centre, alongside professional casual appointments in academic computing. Her research focuses on the mathematical foundations of data privacy, particularly differential privacy and its applications in natural language processing and machine learning. She develops privacy-preserving systems and tools using quantitative information flow techniques rooted in information theory. Key areas include privacy analyses for financial systems (e.g., Open Banking), API privacy, and optimizing utility in privacy pipelines. Fernandes has led or contributed to five research projects, including work on privacy analyses for financial transaction protocols and UAV-based machine learning systems. She received the 2021 John Makpeace Bennett Award for her doctoral research on differential privacy in metric spaces. Her academic journey combines industry experience as a backend software engineer with rigorous academic contributions, spanning over 21 peer-reviewed publications and collaborations across cybersecurity and privacy engineering domains.

Yan Wangمشاهده پروفایل

استاد

• Trust Management
• Recommender Systems
• Social Networks
• +۷ مورد دیگر

Yan Wang is a Full Professor in the School of Computing at Macquarie University, Australia. He obtained his PhD from Harbin Institute of Technology (HIT), China, and has been at Macquarie since 2003 following a postdoctoral fellowship at the National University of Singapore. His research focuses on trust management, recommender systems, social networks, and services computing, with a strong emphasis on applications in cyber security and data analytics. Wang has published extensively in top-tier venues like AAAI, IJCAI, and IEEE Transactions, and has led over 25 research projects, including current initiatives like Trust-Oriented Data Analytics in Online Social Networks (DP230100676) and Combating Fake News on Social Media (2020–2023). He has received prestigious awards such as the IEEE TCSVC Outstanding Service Award (2017) and the Vice-Chancellor’s Excellence in Research Supervision Award (2014). He serves on editorial boards of journals like IEEE Transactions on Services Computing and has organized major conferences including IEEE ATC2013 and IEEE CLOUD2017. His work bridges theoretical advancements in machine learning and practical challenges in social computing, edge computing, and federated learning. Affiliations: School of Computing, Data Horizons Research Centre, Frontier AI Research Centre Key Projects: Trust-Oriented Data Analytics in Online Social Networks (2023–2026) Combating Fake News on Social Media (2020–2023) Research Themes: Recommender systems, graph learning, fake news detection, and trust management in distributed systems. Wang’s recent work explores causal representation learning, cross-domain recommendations, and privacy-preserving techniques in edge computing environments.

• Data Management
• Social Media Analysis
• User Behavior Analysis
• +۴ مورد دیگر

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.