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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.

• Cybersecurity and privacy
• Privacy protection
• AI and machine learning
• +4 more

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.

• Business Process Management
• Process Mining
• Explainable AI
• +5 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.

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

Dr. Shixun Huang is a Lecturer in the School of Computing and Information Technology at the University of Wollongong, Australia. He holds a PhD from RMIT University and specializes in data mining, machine learning, and optimization algorithms for high-dimensional data problems. His research develops efficient algorithms for data discovery, similarity search, and network analysis. Current projects focus on optimized data acquisition strategies for machine learning, cost-effective labeling for graph neural networks, and cardinality estimation in high-dimensional databases. Methodologically, he combines combinatorial optimization with machine learning techniques. Dr. Huang supervises graduate research on diffusion models for medical imaging, graph prompt learning, and image captioning systems. His honors include multiple best paper awards at top database conferences and recognition for teaching excellence (College Top Course Award at RMIT). Recent publications address dataset distinctiveness maximization (WWW 2025), high-dimensional similarity search (VLDB 2025), and edge computing optimization (2024). Earlier foundational work established new approaches for influence maximization in social networks and temporal graph representation learning.

• Data Mining
• Machine Learning
• Algorithm Design
• +3 more

Toby Walsh is a Scientia Professor of Artificial Intelligence at the University of New South Wales (UNSW), holding multiple prestigious positions including Laureate Fellow, research group leader at Data61, adjunct professor at QUT, external Professor at Uppsala University, honorary fellow at Edinburgh University, and Associate Member of the Australian Human Rights Institute at UNSW. His research spans the breadth of artificial intelligence with particular focus on constraint programming, computational social choice, and fair division. Professor Walsh has made significant contributions to the theoretical foundations of AI, especially in areas related to preference representation, voting systems, and mechanism design. His work bridges theoretical computer science with practical applications in social choice and resource allocation problems. In recent years, he has expanded his research to address pressing ethical issues in AI, including privacy concerns, facial recognition technology, and AI's impact on intellectual property law. Walsh's publication record demonstrates consistent high-impact contributions across decades, with recent work showing increasing attention to societal implications of AI technologies. His research portfolio includes both theoretical advances in constraint satisfaction and practical applications in fair division algorithms, reflecting his ability to connect foundational research with real-world problems. NSW Premier's Prize for Excellence in Engineering and ICT Humboldt Award Research Excellence Award of the Association for Constraint Programming IJCAI Donald E. Walker Distinguished Service Award Professor Walsh has served the academic community extensively through editorial roles as Editor-in-Chief of the Journal of Artificial Intelligence Research and AI Communications, and on the editorial boards of the Journal of the ACM, Journal of Automated Reasoning, and Constraints journal. He has been actively involved in major AI conferences as Program and Conference Chair for Constraint Programming, Automated Reasoning, and Artificial Intelligence venues. His leadership extends to professional organizations where he has served as Secretary of the Association for Constraint Programming and Editor of CP News. As research group leader at Data61, Walsh oversees a team focused on advancing AI research with practical applications. His group has contributed significantly to constraint programming and computational social choice, developing algorithms and theoretical frameworks that have influenced both academic research and practical implementations in resource allocation systems.

• Artificial Intelligence
• Constraint Programming
• Computational Social Choice
• +6 more

Wei Liu is an Associate Professor in Machine Learning and Director of the Future Intelligence Research Lab at the University of Technology Sydney's School of Computer Science. He holds a PhD in Machine Learning from the University of Sydney and maintains active roles as a senior IEEE member and area chair for top AI conferences including KDD, AAAI, and ICDM. Education: PhD in Machine Learning, University of Sydney His research focuses on adversarial machine learning, generative AI, cybersecurity, and multimodal learning, with particular emphasis on AI security, robustness of algorithms, and model fairness. Liu's work addresses critical challenges in developing next-generation AI systems that can withstand cyber attacks while maintaining performance with multi-modal data and balanced outcomes despite data imbalances. Analysis of his recent publications reveals a strong trend toward securing large language models against novel attack vectors while advancing multimodal learning techniques. His work spans both theoretical contributions in adversarial frameworks and practical applications in cybersecurity, transportation, and industrial systems. Scientific Awards: 3 Best Paper Awards Most Influential Paper Award at PAKDD Nominee for NSW Premier's Prizes for Early Career Researcher (2017) Liu actively supervises numerous PhD students working on adversarial attacks, robust AI models, and agricultural applications. He has secured substantial funding including ARC Discovery Projects, government grants, and industry partnerships with organizations including Agriwebb, CSIRO Data61, and AVEVA. His Future Intelligence Research Lab specifically targets three emerging challenges: AI security against cyber attacks, robustness with multi-modal data, and model fairness with imbalanced datasets. The Future Intelligence Research Lab produces next-generation AI algorithms addressing AI security vulnerabilities, multi-modal robustness challenges, and fairness issues in real-world deployment scenarios, with multiple representative papers demonstrating practical applications in each domain.

• Adversarial Machine Learning
• Generative AI
• Cybersecurity
• +6 more

Soon Lay Ki is an Associate Professor at the School of Information Technology, Monash University Malaysia, where she also serves as Associate Head (Graduate Research) since November 2018. Her academic journey began with roles at Multimedia University (MMU), where she was a Senior Lecturer and Deputy Dean (Research and Innovation) from 2016 to 2018. PhD in Web Engineering, Soongsil University, Korea Master of Science in Database, Universiti Putra Malaysia Bachelor of Computer Science, Universiti Putra Malaysia Her research centers on applied natural language processing and data management , with a focus on analyzing domain-specific and social media content. Her work spans aspect-based sentiment analysis , cyberbullying detection , misinformation detection , and relation extraction from conversational texts. Recently, her research has expanded into digital health , particularly emotion-aware mental health chatbots and emotion detection via video data. The most recent articles highlight a strong trend in AI for social good , including legal reasoning, mental health, accessibility, and public health. Her publications appear in high-impact journals and conferences such as Artificial Intelligence and Law , IEEE Transactions on Dependable and Secure Computing , and ACL-affiliated workshops. She has received notable scientific awards, including: ITEX'24 Silver Award for 'MOBOT' mental health chatbot (2024) Silver Medal at Malaysia Technology Expo 2023 for the same innovation The Incubator Grant: Bolster Category (2023) Dr. Soon has graduated seven PhD and three Master’s students, one of whom received the MMU Best Master Thesis Award in 2015. She leads multiple research grants, including FRGS-funded projects and industry collaborations with Telekom Malaysia and Intel . She is currently a Chief Investigator or Primary Chief Investigator on six active projects, including WHinc, WAge, and Epsilon, often in collaboration with Monash Australia and SEACO. She is part of key research teams such as the Action Lab at Monash University Australia and the South East Asia Community Observatory (SEACO) , contributing to inclusive research infrastructure and public health data access initiatives.

• Applied Natural Language Processing
• Data Management
• Aspect-Based Sentiment Analysis
• +9 more

Professor Seokhee Hong is a distinguished academic at the School of Computer Science, The University of Sydney. With prestigious appointments including an ARC Future Fellowship (2013-2016) and Humboldt Fellowship (2013-2014), she has emerged as a leading researcher in graph drawing and visual analytics. Her research focuses on developing scalable algorithms for information visualization of massive complex networks, with applications spanning security analytics, computational biology, and software engineering. Key contributions include the creation of open-source visual analytics software GEOMI and foundational work in 2.5D graph visualization. 2013-2016: ARC Future Fellowship 2008-2012: ARC Research Fellowship 2013-2014: Humboldt Fellowship Recent publications demonstrate her ongoing innovation in graph drawing algorithms, network fairness, and computational geometry. She has served on editorial boards of key journals and as program chair for major conferences in her field. 2006: CORE Chris Wallace Award 2012: Eureka Prize Finalist Multiple Graph Drawing Competition wins Her work bridges theoretical algorithm development with practical applications, addressing challenges in biological network analysis, dynamic graph visualization, and cluster-preserving layouts. With over 140 publications and significant research funding, she continues to shape network visualization research.

• Graph Drawing
• Information Visualization
• Visual Analytics
• +3 more

Professor Chengfei Liu is a faculty member at Swinburne University of Technology, leading the Web and Data Engineering research group and serving as focus area leader for Knowledge and Data Intensive Systems in the SUCCESS Centre. His research focuses on web data management, advanced database systems, graph data, and workflow models. He has held academic positions at the University of South Australia, University of Technology Sydney, and the University of Queensland's DSTC. His work spans data management, distributed systems, and information systems, with notable contributions to cohesive subgraph discovery and efficient algorithms. Research interests include keyword queries, uncertain data, graph databases, and artifact-centric workflows. His awards include the Vice-Chancellor's Research Excellence Award (2007) and multiple best paper/demo awards at top conferences. Professor Liu supervises numerous PhD students and holds grants from the Australian Research Council (ARC), including projects on dynamic networks, heterogeneous graphs, and information resilience. He has authored over 297 publications, with recent work addressing topics like Bitcoin transaction prediction, federated subspace clustering, and multimodal eating disorder detection. His professional roles include program committee memberships at SIGMOD, ICDM, and CIKM, reflecting his influence in data science and database research.

• Data Management
• Database Systems
• Graph Data Management
• +4 more

Dr. Troy Lee is an Associate Professor of Quantum Cryptography at the Centre for Quantum Software and Information within the Faculty of Engineering and Information Technology at the University of Technology Sydney . His research focuses on quantum algorithms, computational complexity, and graph theory, with particular emphasis on quantum-classical separations and query complexity. Education: Not explicitly mentioned Research Areas: Quantum algorithms, computational complexity, graph theory, quantum cryptography, and Boolean function analysis Teaching: Supervised the course Data Structures and Algorithms in 2022 Grants: Currently involved in quantum algorithm design and defense optimization projects (2025-2028, 2021-2024) His recent publications highlight advancements in quantum query complexity, graph algorithms, and exact learning techniques. Notably, his work includes quantum speedups for graph connectivity problems and improved bounds for Fourier-sparse function learning. Dr. Lee maintains active collaborations across theoretical computer science and quantum computing domains, contributing to both foundational and applied research in quantum software development.

• Quantum Cryptography
• Data Structures and Algorithms
• Quantum Algorithms
• +3 more

Sharma Chakravarthy is a Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA) since January 2000. He is an ACM Distinguished Scientist, IEEE Senior Member, and Fulbright Specialist, recognized for his contributions to stream data processing, active databases, and graph mining. He established the Information Technology Laboratory (IT Lab) and an NSF-funded Distributed and Parallel Computing Cluster at UTA, and has supervised 15 PhD theses and 85 MS theses. His research spans semantic query optimization, scalability in graph mining, social network analysis, and multimedia databases. Education: B.E. in Electrical Engineering (Indian Institute of Science), M.Tech (IIT Bombay), M.S. and Ph.D. (University of Maryland, College Park) Prior affiliations: University of Florida (10 years), Computer Corporation of America (CCA, 3 years), Xerox Advanced Information Technology (1 year) Research Interests : His work focuses on adapting map/reduce paradigms for scaling graph mining algorithms to large networks, machine learning applications in Q-A social networks, and InfoSift—a classification system for text, email, and web data. He also explores stream data processing across domains like video analysis. Scientific Awards : 2003 Creative Outstanding Researcher (UTA) 2002 Department-Level Senior Outstanding Researcher ACM Distinguished Scientist IEEE Senior Member Fulbright Specialist Inclusion in Who's Who Among South Asian Americans and Who's Who Among America's Teachers He co-organized the 13th ACM International Conference on Distributed Event-Based Systems (DEBS 2013) and has authored over 200 refereed papers/book chapters. He has given tutorials on graph mining, active/real-time databases, and heterogeneous databases globally.

• Stream Data Processing
• Semantic Query Optimization
• Multiple Query Optimization
• +11 more

Luke Mathieson is a Senior Lecturer and Deputy Head of School (Teaching and Learning) in the School of Computer Science at the University of Technology Sydney. His academic career spans theoretical computer science with a focus on computational complexity and its applications. Dr. Mathieson's educational background includes a PhD in Theoretical Computer Science from Durham University, a Masters and Postgraduate Diploma in Higher Education from Macquarie University, and dual Bachelor's degrees in Computer Science (Honors) and Science (Chemistry) from the University of Newcastle Australia. His research interests are centered on parameterized complexity and its applications, extending to various areas of complexity theory, algorithmics, quantum computing, graph theory, and related mathematics. A major theme of his research is the complexity of graph editing problems, a topic in which he specializes. His recent work bridges theoretical complexity with practical applications in AI education, network science, and quantum computing. Dr. Mathieson has taught an extensive range of computer science subjects, particularly focusing on the theory of computation, computational complexity, and algorithmics. At UTS, he teaches or has taught subjects including Data Structures and Algorithms, Applications Programming, Computing Science Studio, Theory of Computing Science, Programming, and Advanced Algorithms. He serves as the Course Director for the Bachelor of Science in Information Technology suite of degree programs and the Course Coordinator for the IT Core. Senior Lecturer, University of Technology Sydney, School of Computer Science (2022-present) Lecturer, University of Technology Sydney, School of Computer Science (2021-2022) Scholarly Teaching Fellow, University of Technology Sydney, School of Computer Science (2017-2021) Research Associate, University of Newcastle Australia, Centre for Information Based Medicine (2014-2017) Adjunct Lecturer, Macquarie University, Department of Computer Science (2014) Postdoctoral Fellow, Macquarie University, Department of Computer Science (2011-2013) Research Associate, University of Newcastle Australia, School of Electrical Engineering and Computer Science (2010-2011) His research demonstrates consistent productivity across theoretical computer science with notable contributions to parameterized complexity and network controllability. Recent publications show an expanding scope incorporating quantum computing applications and educational technology innovations. The QB-suite: a framework for quantum algorithm design and benchmarking (2024-2027) National Industry PhD Program: Improving biosecurity through livestock history recording (2024-2028) Random Number Generation and Analytics for Client Understanding (2018-2019) He maintains active research collaborations across multiple institutions and is affiliated with the Faculty Centre for Quantum Software and Information (QSI) at UTS, reflecting his growing involvement in quantum computing research.

• Parameterized Complexity
• Computational Complexity
• Algorithmics
• +6 more

Shlomo Geva is an Adjunct Professor in the School of Computer Science at Queensland University of Technology's Faculty of Science. His research focuses on information retrieval systems, particularly in specialized areas including XML search engines, text search engines, link discovery, and document computing. His academic work spans multiple disciplines within computer science, with particular emphasis on information retrieval technologies and their applications. Professor Geva's research interests include clustering algorithms, cross language information retrieval, focused information retrieval, information retrieval systems, link discovery mechanisms, search engine technologies, text indexing and retrieval methods, and XML indexing and retrieval techniques. His work demonstrates a consistent focus on improving the efficiency and effectiveness of information access systems across various data formats and domains. His recent publications reveal a trend toward applications of information retrieval techniques in diverse fields including remote sensing, bioinformatics, and data stream processing. The research shows an evolution from traditional information retrieval problems toward more specialized applications requiring advanced clustering algorithms and efficient data processing techniques for large-scale datasets. Professor Geva has successfully supervised numerous doctoral students whose research topics include indoor environment mapping by robots, cross-language information retrieval, natural language query interfaces for XML, evolvable hardware, and autonomous robot behavior systems.

• Information Retrieval
• XML Search Engines
• Text Search Engines
• +8 more

Du Huynh is an Associate Professor at the School of Physics, Mathematics and Computing, Department of Computer Science and Software Engineering, The University of Western Australia (UWA). She holds a 0.8 FTE appointment and has served as Graduate Research Coordinator since 2013. Her research focuses on Computer Vision, Machine Learning, Object Detection, and Video Analytics with applications in mineral processing, intelligent transportation systems, and medical imaging. She has secured over $3M in ARC grants and is a Chief Investigator in the Australian Centre for Quantum Growth ($18M funding). Education: BSc (Hons) and PhD in Computer Science from UWA. Prior to UWA, she lectured at Murdoch University for five years. Research Highlights Developed algorithms for automated mineral ore analysis, pedestrian trajectory prediction, and surgical instrument detection. Received three Best Paper Awards (ICPR2014, Kenneth Clarke Journal 2019, AusDM2022) and multiple award nominations. Editorial roles include PLOS One, Journal on Artificial Intelligence, and guest editorships for special issues in CVIU and Electronics. Grants & Projects Lead investigator in quantum computing algorithms for real-time optimization ($3M ARC funding). Collaborations with industry (e.g., Main Roads WA) on traffic video analytics and drone-based prediction systems. Academic Contributions Developed courses in Computer Vision, Machine Learning, and Java Programming. Supervised numerous HDR students and served on technical committees for WACV, ICCV, and DICTA conferences.

• Computer Vision
• Machine Learning
• Object Detection
• +7 more

David Taniar is an Associate Professor in the Department of Software Systems & Cybersecurity at Monash University's Faculty of Information Technology. He holds a PhD in Computer Science from Victoria University (1997), and prior degrees from Swinburne University of Technology. His research focuses on computation theory, distributed systems, health informatics, and AI applications in healthcare. Taniar has led/co-investigated over 15 projects including digital health interventions, eco-friendly navigation systems, and healthcare data analytics. Current projects include a 2023-2026 initiative on pathology requesting practices in Emergency Departments. He has published 508+ works, spanning medical image analysis, spatial data processing, and cybersecurity. Awards include the 2021 Good Design Award for a hand hygiene management system. Education: Doctor of Philosophy, Computer Science, Victoria University (1997) Master of Applied Science, Computer Science, Swinburne University (1992) Graduate Diploma, Computer Science, Swinburne University (1990) Research Focus: Combines AI/ML with domain-specific challenges in healthcare, transportation, and distributed systems. Recent work addresses disease detection via medical imaging (e.g., glaucoma, diabetic ulcers), spatiotemporal data optimization for eco-routing, and ED data utilization. Key Projects: A digital health intervention to improve pathology requests in Emergency Departments (2023-2026) Eco-friendly navigation using big spatiotemporal data (2023) Actionable clinical data systems for quality care accreditation (2021-2025) Awards & Activities: Recipient of 2021 Good Design Award for healthcare innovation Editor-in-Chief of International Journal of Business Data Communications and Networking (2007) Chief Examiner for multiple IT courses at Monash

• Computation Theory and Mathematics
• Distributed Computing
• Information Systems
• +7 more