Ngoc Thanh Nguyen is a Full Professor at Wroclaw University of Science and Technology where he serves as Head of the Department of Applied Informatics. He holds the prestigious title of Professor granted by the President of Poland and has been recognized as a Distinguished Scientist of ACM since 2009. He serves as Editor-in-Chief of both the Journal of Information and Telecommunication (JIT) and the Vietnam Journal of Computer Science (VJCS), and chairs the IEEE SMC Technical Committee on Computational Collective Intelligence. His research spans computational collective intelligence, knowledge integration, data mining, social media analysis, and sentiment analysis. Professor Nguyen has pioneered significant methodologies in spatial data clustering within network space, inter-sequence pattern mining, and graph neural network applications. His work bridges theoretical computer science with practical applications in intelligent information systems, demonstrating particular expertise in handling complex spatial and sequential data structures. His research has evolved from foundational pattern mining techniques to sophisticated neural network approaches for geospatial and social data analysis. The analysis of his recent publications reveals a strong focus on spatial data analysis in network environments, with significant contributions to clustering algorithms, graph neural networks, and pattern mining. His work consistently addresses efficiency challenges in data processing while expanding into emerging areas like Vietnamese language processing and topological data analysis. The research demonstrates a clear trajectory from traditional data mining techniques toward more sophisticated AI-driven approaches that incorporate spatial relationships and network topologies. Distinguished Scientist of ACM (2009) ACM Distinguished Speaker (2009-2013) IEEE Distinguished Visitor (2009-2013) Title of Professor granted by the President of Poland Professor Nguyen has supervised over 20 PhD students to completion and currently mentors several ongoing doctoral candidates. His academic leadership extends to founding two major conference series: the Asian Conference on Intelligent Information and Database Systems (ACIIDS) and the International Conference on Computational Collective Intelligence (ICCCI), which have become significant venues in their respective fields. His collaborative network spans multiple institutions, particularly with Yeungnam University as evidenced by several co-supervised PhD projects. As founder and chair of the IEEE SMC Technical Committee on Computational Collective Intelligence, he leads an international community of researchers advancing this specialized field. His departmental leadership at Wroclaw University of Science and Technology positions him at the center of applied informatics research and education in Poland, with particular emphasis on computational intelligence applications.
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۱-۶ از ۶ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Václav Snásel is a Professor at the Department of Informatics, VSB - Technical University of Ostrava, Czech Republic. He holds a PhD from Masaryk University (Brno, Czech Republic). His research focuses on optimization algorithms, machine learning, metaheuristics, data mining, and their applications in engineering and computational intelligence. Key research interests include developing novel metaheuristic algorithms (e.g., Walrus Optimizer, Artificial Protozoa Optimizer), optimization frameworks for engineering problems, and applications in wireless sensor networks, power systems, and medical diagnostics. He also explores computational methods for data analysis, including graph-based techniques and surrogate-assisted evolutionary algorithms. His recent work emphasizes multi-objective optimization, algorithm design for high-dimensional problems, and interdisciplinary applications in agriculture, energy systems, and bioinformatics. Collaborations span institutions globally, with frequent co-authorship on topics like swarm intelligence and evolutionary computation.
Badran Raddaouiمشاهده پروفایل
مدرس ارشد
Badran Raddaoui is a Senior Lecturer (Maître de Conférences) at Telecom SudParis, where he conducts extensive research in artificial intelligence with a focus on knowledge representation, reasoning under uncertainty, and data mining. His work bridges theoretical foundations with practical applications in knowledge management and pattern discovery. His primary research interests include: Argumentation frameworks and logical reasoning systems Measurement and quantification of inconsistencies in knowledge bases SAT-based approaches for data mining problems Ontology reasoning under uncertainty Community detection in complex networks High utility itemset mining from transaction databases Dr. Raddaoui's recent publications demonstrate a strong emphasis on symbolic AI techniques for pattern extraction and robust reasoning with imperfect information. His work on inconsistency measurement through minimal inconsistent subsets and prime implicates has established him as a notable contributor to the field. He frequently applies constraint programming and SAT solving to problems in data mining and knowledge representation. His publication record shows consistent output from 2010 through 2024, with numerous papers in top-tier AI venues including IEEE Intelligent Systems, IJCAI, ECAI, and AAMAS. His research often involves collaborations with Saïd Jabbour and other researchers across multiple institutions. As an academic, Dr. Raddaoui contributes to the scholarly community through conference reviewing and participation in research projects focused on AI theory and applications. His current work continues to advance frameworks for knowledge base repair and conceptual clustering through pattern mining techniques.
Prof. Rage Uday Kiran is an Associate Professor at the University of Aizu in Aizu wakamatsu, Fukushima, Japan, with additional visiting researcher appointments at the University of Tokyo and the National Institute of Information and Communications Technology (NICT) in Tokyo. He holds a PhD in Computer Science from the International Institute of Information Technology, Hyderabad, and has published over 50 articles in top-tier venues including EDBT, SSDBM, PAKDD, and DASFAA. Education PhD in Computer Science - IIIT-Hyderabad, India (2005-2011) MS in IT Agriculture - DA-IICT, India (2003-2005) B.Tech in Agriculture Engineering - Acharya N. G. Ranga Agricultural Engineering College, India (1999-2003) Research Focus Dr. Kiran specializes in developing novel data mining models and high-performance algorithms for temporal, spatiotemporal, and transactional databases. His primary research explores pattern mining techniques for rare item discovery, parallel computation optimizations, and real-world applications in air pollution analysis, traffic congestion prediction, agricultural ICT systems, and recommender systems. His recent work focuses on utility-based pattern discovery in spatiotemporal contexts and periodic pattern detection in temporal databases, emphasizing scalability and efficiency for large-scale datasets. Professional Experience Project Assistant Professor - University of Tokyo (2015-2020) Researcher - NICT Tokyo (2015-2020) Post Doctoral Researcher - University of Tokyo (2012-2015) Technical Contributions Dr. Kiran maintains public datasets for pattern mining research and develops open-source tools including the PAttern MIning Python Kit (PAMI-PyKit). His technical expertise spans C/C++/Java, database systems (MySQL/PostgreSQL), web technologies, and cloud platforms (Azure/AWS).
Damla Oğuzمشاهده پروفایل
استادیار
- Data Mining
- Database Systems
- Query Optimization
- +۶ مورد دیگر
Damla Oğuz is an Assistant Professor and Deputy Head of Department in the Department of Computer Engineering at Izmir Institute of Technology. She has held this position since December 2019, following her appointment as an Assistant Professor in the Department of Software Engineering at Yaşar University from 2017 to 2019. Her educational background includes: B.Sc. in Software Engineering (2008) and Industrial Systems Engineering (2009) from Izmir University of Economics M.Sc. in Computer Engineering (2012) from Izmir Institute of Technology Ph.D. in Computer Science (2017) from Paul Sabatier University, France (completed as part of a cotutelle program with Ege University) Damla Oğuz's research focuses on data mining and query optimization in distributed databases . Her work spans multiple areas including high-utility itemset mining, federated query processing, knowledge base evaluation, and the application of large language models in education. She has made significant contributions to understanding how to efficiently process queries across distributed data sources, particularly in the context of semantic web technologies and linked data. Her research often addresses the challenges of scalability, privacy, and performance in large-scale data management systems. Her recent publications demonstrate a clear trend toward addressing contemporary challenges in data management and analysis. Starting with foundational work in itemset mining algorithms, her research has evolved to tackle more complex problems in federated query processing, knowledge base evaluation, and the integration of large language models into educational contexts. The breadth of her work spans theoretical algorithm development, practical system implementation, and educational applications. Her teaching portfolio includes: CENG 212 - Concept of Programming Languages SEDS 502 - Modern Database Management Systems SEDS 535 - Information Extraction CENG 544/SEDS 542 - Large Scale Data Management CENG 465 - Principles of Data-Intensive Systems Her professional activities include: Deputy Head of Department at Izmir Institute of Technology Active research in data management and mining Teaching undergraduate and graduate courses in computer engineering
Mukesh Mohaniaمشاهده پروفایل
استاد مدعو
Dr. Mukesh Mohania serves as an Adjunct Professor at UNSW Canberra within the School of Business. His extensive academic career spans over three decades with continuous scholarly contributions from 1994 through 2025. His research bridges theoretical database systems with practical applications across multiple domains including educational technology, blockchain, and business intelligence. Professor Mohania's research interests demonstrate remarkable breadth and evolution over time. Beginning with foundational work in data warehousing and database systems, his research trajectory expanded into information integration, privacy management, and more recently, educational technology and blockchain applications. His scholarly work shows a consistent pattern of addressing emerging technological challenges while maintaining connections to core database principles. A distinctive characteristic of his research is the practical application of theoretical concepts to solve real-world business and educational problems. The analysis of his recent publications reveals a strong focus on educational technology applications, with approximately 40% of his 2022-2025 publications addressing AI-driven educational systems, question categorization, and learning analytics. Another significant portion (around 30%) focuses on security, privacy, and blockchain applications. The remaining publications continue his longstanding interest in database systems, data mining, and information integration. This distribution indicates a strategic pivot toward educational technology while maintaining expertise in his foundational areas. Professor Mohania has established a prolific publication record with over 100 scholarly contributions including book chapters, journal articles, and conference papers. His work appears in prestigious venues such as IEEE Transactions on Knowledge and Data Engineering, ACM conferences, and Springer publications. While specific awards aren't documented in the available information, his sustained publication record across multiple decades demonstrates significant scholarly impact. His research collaborations span numerous institutions and researchers globally, with frequent co-authorship patterns suggesting established research teams focused on educational technology and database systems. The consistent output across decades indicates successful grant funding and sustained research activity, though specific grant details aren't provided in the available information. Professor Mohania's work shows particular strength in translating database research into practical business and educational applications.