جستجوی استاد راهنمای دانشگاهی

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.

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.

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.

• 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

• 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.

Mohammad Kazemi Beydokhtiمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Natural Language Processing (NLP)
• Geospatial Domain
• +۵ مورد دیگر

Mohammad Kazemi Beydokhti is a Research Fellow (Level A) at RMIT University's Research & Innovation Capability department. He is also a PhD candidate with expertise in Machine Learning, NLP, and Geospatial Domain applications. His research focuses on qualitative spatial reasoning, GeoQA systems, knowledge graphs, and LLMs, with notable contributions to projects like Dynamic Vicmap (awarded the 'Innovation Award' by GCA) and the RMIT AWS Cloud Supercomputing Hub. He has collaborated with institutions like Utrecht University and has taught courses such as Advanced Imaging Technology (GEOM2112), Geospatial Programming with Python (GEOM2157), and Applied Geospatial Techniques (GEOM2450) as a tutor at RMIT from July 2021 to August 2024. His scientific contributions span geospatial question-answering systems, probabilistic spatial reasoning, and spatial-temporal modeling of seismic activity. Key projects include the DBSCAN-based seismic province analysis in Iran and ANP-OWA method applications in air quality monitoring station placement. Research Awards: Innovation Award (GCA) for Dynamic Vicmap project Labs/Teams: Involved in RMIT's AWS Cloud Supercomputing Hub and geospatial collaboration networks

Tim Frenchمشاهده پروفایل

دانشیار

• Logic
• Artificial Intelligence
• Knowledge Representation and Reasoning
• +۵ مورد دیگر

Tim French is an Associate Professor in the Department of Computer Science and Software Engineering at the University of Western Australia's School of Physics, Maths and Computing. He is affiliated with the UWA Oceans Institute and serves as Regional Contest Director for the South Pacific Programming Contest and Programme Chair for the Australasian Conference on Artificial Intelligence 2022. His research focuses on logic, artificial intelligence, knowledge representation, and reasoning about uncertainty in multi-agent systems, probabilistic reasoning in games, and industrial applications like automated planning and machine learning for complex processes. French holds a PhD in Computer Science (2007) and BSc in Computer and Mathematical Sciences (1999), both from UWA. His expertise spans algorithms, automated reasoning, formal methods in software, and temporal logic verification systems. He has led or contributed to 6 major research projects including the ARC Research Hub for Transforming Energy Infrastructure and the ARC Training Centre for Transforming Maintenance through Data Science. His research outputs include 118 publications covering topics like aleatoric logic for probabilistic reasoning, semantic knowledge extraction from industrial maintenance systems, and deep learning applications in wastewater treatment. He has developed novel methods for knowledge graph construction, state estimation in complex systems, and user interface design informed by work characteristics models. Key grants: 6 active/finished projects totaling $M+ funding Leadership roles: Programming contest director, conference chair Interdisciplinary focus: Combines formal logic with industrial automation challenges French's work contributes to UN Sustainable Development Goals through education and innovation in sustainable industrial processes and environmental systems modeling. His team has developed practical solutions for maintenance procedure digitization, wastewater plant optimization, and robust agent-based systems for uncertain environments.

Soon Lay Kiمشاهده پروفایل

دانشیار

• Applied Natural Language Processing
• Data Management
• Aspect-Based Sentiment Analysis
• +۹ مورد دیگر

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.

Alireza Ahadiمشاهده پروفایل

مدرس پاره‌وقت

• Data Science
• Machine Learning
• Educational Data Mining
• +۹ مورد دیگر

Dr Alireza Ahadi is a PartTime Lecturer at the Faculty of Engineering & Information Technology , University of Technology Sydney. He holds a PhD in Data Analytics from UTS (2018) and has previously worked as a postdoctoral researcher at Macquarie University (2019-2022) focusing on skills analytics and educational data mining. Education: PhD in Data Analytics (UTS, 2018), Master of Information Technology Extended (UTS, 2013), Bachelor in Computer Science (UTS, 2010). Current Role: Teaching IT and Science subjects at UTS with expertise in databases, programming languages, and data science. Research Interests center on data-driven educational solutions , including text mining in education, skills gap analysis between academia and job markets, code comprehension studies, and miRNA regulation in cancer research. His work integrates machine learning with curriculum analytics to improve educational outcomes and workforce alignment. Recent publications (15 most) reveal trends in applying NLP to educational data, eye-tracking analysis for programming behavior, and skills analytics engines for curriculum-job market mapping. His 2022 article on skills gap identification using Burning Glass data highlights his focus on real-world educational impact. Teaching Experience includes coordination and lecturing in core subjects like Database Fundamentals, Programming Languages, and Data Structures at UTS. He also explores educational data mining , optimization of analytics engines , and recommendation system design in pedagogical contexts.

• Information Retrieval
• Medical Informatics
• Natural Language Processing
• +۱۷ مورد دیگر

Guido Zuccon is a Professor at Queensland University of Technology (QUT), specializing in Information Retrieval and Medical Informatics. He is affiliated with QUT's ielab, contributing to research in health information systems, semantic web technologies, and quantum models for IR. His work emphasizes evaluation methodologies in medical search, clinical decision support, and healthcare informatics. Education: PhD in Information Retrieval (University of Glasgow, 2012). Research focuses on medical information extraction, ontology mapping, and user-centered IR systems. He has co-authored over 130 publications, including landmark studies on consumer health search, clinical query framing, and de-identification of health records. Key contributions include advancements in task-oriented search, diversity in rankings, and evaluation frameworks for health IR. His work bridges theoretical models (e.g., quantum probability ranking) with practical applications in healthcare and biomedical systems.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Dr Zheyuan Liu is a Research Fellow at the Australian Institute for Machine Learning within the Faculty of Sciences, Engineering and Technology at The University of Adelaide. His research focuses on machine learning, computer vision, and multimodal learning, particularly in areas like federated learning, semantic segmentation, and vision-language models. His research interests include: Federated fine-tuning of large language models Text-to-video generation with diffusion models Weakly supervised semantic segmentation techniques Multimodal image retrieval systems Audio-visual source localization Recent publications demonstrate expertise in cross-domain adaptation, contrastive learning, and re-ranking methods. He is eligible to supervise Masters and PhD students.

Trevor Cohnمشاهده پروفایل

استاد

• Natural Language Processing
• Machine Learning
• Probabilistic Modeling
• +۷ مورد دیگر

Trevor Cohn is a Professor at The University of Melbourne's Computing and Information Systems Department, with a dual appointment at Google Research Australia. His research focuses on Natural Language Processing (NLP), probabilistic machine learning, and machine translation. He leads the Natural Language Processing group and has held roles including Director of the ITTC on medtech and Local Chair for ACL 2018. His research interests include automatic language processing, handling uncertain data, structured prediction, and cross-lingual systems. Recent work addresses backdoor attacks, reward modeling, and fairness in AI. He teaches courses like Statistical Machine Learning and Web Search & Text Analysis, advising students such as Thinh Truong and Nitika Mathur (award recipients). His 15 most recent articles span topics like LLM-based planning systems, mitigation of adversarial attacks, and cross-lingual NLP challenges. His work emphasizes practical applications of NLP in multilingual contexts and robust AI systems.

• Stream Data Processing
• Semantic Query Optimization
• Multiple Query Optimization
• +۱۱ مورد دیگر

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.