جست‌وجوی استاد و پژوهشگر

Antonietta Lanzaمشاهده پروفایل

استادیار

• Man-Machine Interaction
• Machine Learning
• Knowledge Acquisition
• +۴ مورد دیگر

Antonietta Lanza is an Assistant Professor of Computer Science at the Department of Informatics, University of Bari, Italy. She earned her degree in Information Science from the same university in 1978 and has been a faculty member since 1984. Her research spans man-machine interaction, machine learning, and knowledge acquisition with applications in technical document processing and geospatial data interpretation. Education : Degree in Information Science (University of Bari, 1978) Affiliations : Department of Informatics, University of Bari Her research focuses on integrating machine learning with geospatial analysis, particularly for wind speed forecasting, topographic map interpretation, and cadastral data processing. She has developed algorithms for spatio-temporal modeling and spatial clustering of structured data. Recent publications primarily address wind power prediction, spatio-temporal lazy learning, and cluster-based time series modeling. Applications span renewable energy, environmental planning, and maritime AIS data analysis. She actively contributes to academic associations AI*IA (Italian Association for Artificial Intelligence) IAPR-IC (International Association of Pattern Recognition - Italian Chapter) and has created software like PS_package , a tool for line-drawing segmentation.

Chao Huangمشاهده پروفایل

استادیار

• Large Language Models
• Graph Learning
• Recommender Systems
• +۳ مورد دیگر

Chao Huang is an Assistant Professor at the Department of Computer Science and Institute of Data Science at the University of Hong Kong (HKU). As the director of the Data Intelligence Lab@HKU, his research focuses on Large Language Models (LLMs), LLM Agents, Graph Learning, Recommender Systems, and AI for Smart Cities. He holds a PhD from the University of Notre Dame. Education: PhD in Computer Science from University of Notre Dame (USA). Research Interests: His work bridges machine learning with practical applications, including: Developing advanced LLM frameworks like GraphGPT and UrbanGPT Creating automated research tools (AutoAgent, AI-Researcher) Designing recommendation systems with LLM integration (RLMRec, LLMRec) Exploring spatio-temporal AI for urban challenges Notable Achievements: Over 11,000 Google Scholar citations (h-index 55), multiple top conference awards (WWW/SIGIR/KDD), and open-source projects with thousands of GitHub stars. His work has been recognized as most influential/pioneering in major AI conferences. Labs/Teams: Leads the Data Intelligence Lab, collaborating on projects like LightRAG, MiniRAG, and VideoRAG. The lab emphasizes open-source contributions with repositories on GitHub. Grants/Advising: Supervises PhD/MPhil students and offers research internships. Active in recruiting motivated researchers and students through HKU's programs.

Eduard Dragutمشاهده پروفایل

دانشیار

• Databases
• Information Retrieval
• Information Extraction
• +۸ مورد دیگر

Eduard Dragut is an Associate Professor in the Department of Computer and Information Science at Temple University's College of Science and Technology. He received his Ph.D. in Computer Science from the University of Illinois at Chicago, M.S. from the University of Iowa, and B.S. in Computer Science from the University of Bucharest. His research spans databases, information retrieval, extraction, cleaning, and integration, with particular focus on opinion mining, web data management, and cyber-infrastructure for science. His educational background includes a Ph.D. from University of Illinois at Chicago, M.S. from University of Iowa, and B.S. from University of Bucharest. His research interests cover multiple areas including databases, information retrieval, extraction, cleaning, and integration; opinion mining and retrieval; web data management; and cyber-infrastructure for science. Current research activities focus on record linkage and fusion, deep learning for entity recognition, sentiment analysis, and news mining. His recent publications demonstrate a consistent focus on information extraction, data integration, and sentiment analysis across various domains including climate science, social media, and scientific literature. His work increasingly bridges traditional database research with machine learning applications, particularly in the areas of entity recognition, knowledge graph construction, and comment analysis. Professional Service Chair SIGMOD/PODS PhD Symposium 2016, San Francisco, USA Chair ICDE PhD Symposium 2015, Chicago, USA PC Member: SIGMOD 2016, SIGMOD 2015, EMNLP 2015, DASFAA 2015, INFOCOM 2015, NAACL-HLT 2015, WWW 2014, COLING 2014, DASFAA 2014, ADBIS 2014 Referee for TKDE, TWEB, TSC, TiiS, ISJ, TKDD, JWS, WWWJ Served on NSF CISE/IIS panels in 2016 Professor Dragut has advised multiple graduate students including Andrew Thomas Schneider (PhD Fourth Year), Lihong He (PhD Second Year), and Abdullah Aljebreen (PhD First Year). His teaching portfolio includes courses such as Data-Intensive and Cloud Computing, Principles of Data Management, and Topics in Computer Science: Information Retrieval. He has taught consistently from Fall 2013 through Spring 2025, demonstrating his commitment to education in the field of computer science. He co-authored the book 'Deep Web Query Interface Understanding and Integration' with Weiyi Meng and Clement Yu, contributing significantly to the field of web data integration.

Yixiang Fangمشاهده پروفایل

دانشیار

• Data Management
• Data Mining
• Artificial Intelligence
• +۸ مورد دیگر

Yixiang Fang is an Associate Professor at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. Previously, he was a Research Associate at the School of Computer Science and Engineering, the University of New South Wales (UNSW) during 2018-2020, working with Prof. Xuemin Lin. He received his PhD from the Department of Computer Science at the University of Hong Kong (HKU) in 2017, advised by Prof. Reynold Cheng, and was a visiting scholar at Nanyang Technological University in 2016. Dr. Fang's research focuses on data management, data mining, and artificial intelligence over big data, particularly big graph data. His work centers on community search, densest subgraph discovery, path/connectivity computation, and LLM + Graph (including graph-based retrieval augmented generation). He has made significant contributions to the field, particularly in cohesive subgraph search and densest subgraph discovery, with his works on community search being well-recognized by leading IT companies like Huawei and ByteDance. His research has resulted in numerous publications in top-tier conferences and journals including SIGMOD, VLDB, ICDE, NeurIPS, WWW, AAAI, IJCAI, TODS, VLDBJ, and TKDE. Notably, his SIGMOD 2020 paper was selected as one of the Best Papers and was awarded the 2021 ACM SIGMOD Research Highlight Award. 2025 CUHK-Shenzhen Teaching Achievement Award (Excellent Prize) 2021 ACM SIGMOD Research Highlight Award Best Paper Award, International Conference on Spatial Data and Intelligence (SpatialDI) 2021 Presidential Young Fellow at CUHK-Shenzhen, 2021 One of the Best Papers, SIGMOD 2020 CCF-Ant Research Fund (2025) Dr. Fang has advised numerous students including PhD candidates, MPhil students, and undergraduate researchers. He has received research funding from Huawei Cloud, CCF-Huawei Populus euphratica Fund, and ByteDance. Currently, he serves as an Editorial Board Member for Information Processing & Management (IPM) and has held leadership roles in major conferences including serving as PC co-chair for LLM+Graph@VLDB2025 workshop. He is also the coach of the CUHK-Shenzhen Programming Contest Team.

Daniele Querciaمشاهده پروفایل

استاد

• Responsible AI
• Responsible Software Systems Design
• Human-Computer Interaction
• +۱۲ مورد دیگر

Daniele Quercia is a Full Professor in the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, where he is also a member of the Interdepartmental Center SmartData@PoliTO – Big Data and Data Science Laboratory. He is affiliated with the College of Computer, Film and Mechatronics Engineering and also contributes to the College of Environmental and Land Engineering. His work bridges computer science, AI, and societal impact. His research focuses on responsible software systems design and responsible AI , with deep engagement in human-computer interaction, data visualization, and ethical implications of AI systems. His work spans interdisciplinary domains, including urban informatics, public health, and social media analysis. He leads the DBDM - Database and Data Mining Group and teaches courses such as Engineering AI Systems, Data Science and Machine Learning for Engineering Applications, and Crafting Tech: Conceiving, Designing, and Communicating Tech Ideas. The most recent publications (2020–2024) reflect a strong trend in AI ethics , urban well-being , and social data analysis . His work appears in top-tier venues like CHI, RecSys, ICWSM, MobileHCI, and WWW, demonstrating a consistent focus on human-centered computing and real-world applications of AI. Themes include AI regulation, health-promoting urban recommendations, fake news, and wearable biofeedback. His scientific contributions are complemented by active mentorship and collaboration with researchers such as Marios Constantinides, Ke Zhou, Sanja Šcepanovic, and Giovanni Quattrone. He has secured teaching and research roles across diverse engineering programs and leads PhD-level instruction in Computer and Control Engineering. He is a member of key research groups and centers at PoliTO, including the DBDM group and SmartData@PoliTO, which focus on cutting-edge data science, AI, and responsible technology development.

Arapi Polyxeniمشاهده پروفایل

مدرس

• Technology Enhanced Learning
• eLearning Systems and Applications
• Digital Libraries
• +۶ مورد دیگر

Arapi Polyxeni is a Lecturer and permanent laboratory teaching staff member at the Technical University of Crete , affiliated with the Electronic and Computer Engineering Department under the School of Electrical and Computer Engineering . She serves as the technical coordinator of the SoftNet Laboratory and contributes to research projects through collaborations with institutions like the Institute of Mathematics and Informatics, Bulgarian Academy of Sciences . Education: Ph.D. in Informatics (2017), Institute of Mathematics and Informatics, Bulgarian Academy of Sciences M.Sc. in Electronic and Computer Engineering (2011), Technical University of Crete Diploma in Electronic and Computer Engineering (2003), Technical University of Crete Her research focuses on Technology Enhanced Learning, eLearning Systems, Digital Libraries, and Semantic Interoperability . She has led technical coordination for European R&D projects like Open Discovery Space and Natural Europe , emphasizing collaborative learning and cultural heritage digitization. Her work spans multimedia systems, database design, and service-oriented architectures. Arapi Polyxeni has received notable recognition, including the Technical Chamber of Greece award for best Diploma Thesis in Electronic Engineering (2003) . She has contributed to over 20 publications, addressing topics such as AR/VR applications in robotics education, metadata management for natural history museums, and pedagogy-driven personalization frameworks. She teaches Operating Systems and Structured Programming laboratory courses. Her technical expertise extends to systems integration, digital library interoperability, and real-time collaborative learning environments like CoLearn and Octopus .

• Machine Learning Techniques
• Knowledge Mining
• Knowledge Mining in Distributed Environments
• +۶ مورد دیگر

Maria Chalkidi is an Associate Professor at the Department of Digital Systems within the School of Information and Communication Technologies at the University of Piraeus. She contributes significantly to the academic community through her research and teaching in computer science and digital systems. She received her B.A. from the Department of Computer Science of the University of Piraeus in 1997, followed by a Master's Degree in Information Systems and a PhD from the Athens University of Economics and Business (AUEB) in 1999 and 2003, respectively. Dr. Chalkidi's research spans several key areas including Machine Learning Techniques, Knowledge Mining in Distributed Environments (P2P Systems, Sensor Networks), and WWW & Databases with specialization in Web Document Clustering and Web Content Management. Her work integrates cognitive approaches with database technologies to address complex information management challenges. Among her notable achievements is the prestigious Marie Curie Outgoing International Fellowship awarded by the European Commission for 2004-2005, which supported her postdoctoral research at the University of California, Riverside's Department of Computer Science & Engineering. She has actively participated in national and European research programs, working with the DB-NET research group at AUEB. Dr. Chalkidi has served on numerous program committees for International Conferences in Knowledge Mining and Machine Learning, contributing to the advancement of these fields globally.

Aris Anagnostopoulosمشاهده پروفایل

استاد

• Stochastic Analysis of Dynamic Processes
• Modeling and Mining in Social Networks
• Data Mining and Algorithms for the WWW
• +۳ مورد دیگر

Aris Anagnostopoulos is a Professor at the Department of Computer, Control, and Management Engineering (Dipartimento di Ingegneria Informatica, Automatica, e Gestionale) at Sapienza University of Rome since April 2012. His academic journey includes a Marie-Curie fellowship at Sapienza University and a postdoctoral position at Yahoo! Research in Santa Clara, CA. His educational background includes: Ph.D. in Computer Science, Brown University, Providence, RI Sc.M. in Applied Mathematics, Brown University, Providence, RI Sc.M. in Computer Science, Brown University, Providence, RI Diploma in Computer Engineering and Informatics, University of Patras, Patras, Greece Professor Anagnostopoulos's research focuses on the design and analysis of algorithms with applications in data mining and data science. His work spans stochastic analysis of dynamic processes, social network modeling and mining, WWW algorithms, randomized and approximation algorithms, information retrieval, and information security. His research has evolved to address contemporary challenges in federated learning, knowledge graphs, and ethical AI considerations in recommendation systems. His recent publications demonstrate a strong trend toward addressing real-world applications of data science and machine learning, particularly in healthcare, social media analysis, and privacy-preserving techniques. His work shows increasing interdisciplinary collaboration, especially with medical researchers, while maintaining strong theoretical foundations in algorithm design. Among his notable scientific awards are: Google Focused Research Award (1 of 6 PIs), 1M USD Junior Fellow, School for Advanced Studies, Sapienza University of Rome Personal research grant, Swedish Research Foundation, 200K euro, 2011 (declined) Best Poster Award, 4th International Conference on Web Search and Data Mining (WSDM 2011) Marie Curie International Incoming Fellowship, 160K euro, 2010 Paris Kanellakis Fellowship, Brown University Runner Up, Best Paper Award, 14th International World Wide Web Conference 2005 (WWW 2005) Professor Anagnostopoulos serves as the academic responsible for mobility (RAM) for the Data Science master's program and has developed comprehensive teaching materials for data science education. He teaches courses including Social Networks and Online Markets, Algorithmic Methods of Data Mining, Data Mining, and Algorithm Design. His teaching approach emphasizes both theoretical foundations and practical applications, with extensive use of AWS and Python-based tools to prepare students for industry certification.

Marie-Christine ROUSSETمشاهده پروفایل

استاد

• Knowledge Representation
• Information Integration
• Ontology-based data access
• +۷ مورد دیگر

Marie-Christine ROUSSET is a Professor of Computer Science at the University of Grenoble Alpes (UGA) in France, where she is a member of the LIG (Laboratoire d'Informatique de Grenoble) in the SLIDE group. Previously affiliated with Paris-Saclay (LRI), she has established herself as a leading researcher in Knowledge Representation and Information Integration. She holds the distinguished position of Senior member of the Institut Universitaire de France (IUF) (2011-2016, renewed for 2016-2021) and serves as co-responsible for the chair Explainable and Responsible AI within MIAI Grenoble Alpes. Her research focuses on ontology-based data access, logic-based mediation between distributed data sources, query rewriting using views, data linkage, and distributed reasoning for the Semantic Web. She skillfully combines artificial intelligence and database techniques to address complex information integration challenges, with applications spanning biomedical informatics, educational technology, and trustworthy AI. Her work demonstrates consistent innovation from foundational research to practical implementations, as evidenced by her co-authorship of the book 'Web Data Management' published by Cambridge University Press. Professor ROUSSET's recent publications (2019-2022) reveal a growing emphasis on data privacy, RDF graph anonymization, and interactive ontology engineering, while maintaining her strong contributions to semantic web technologies and knowledge representation. Her research shows increasing attention to trustworthy AI concerns, aligning with her leadership roles in relevant projects. Scientific Recognition Senior member of Institut Universitaire de France (IUF) (2011-2016, renewed for 2016-2021) Junior member of Institut Universitaire de France (IUF) from 1997 to 2002 Chevalier de l'Ordre National du Merite (July 11, 2011) EurAI Fellow (nominated ECCAI Fellow in 2005) Best Paper Award at AAAI'96 for 'Verification of Knowledge Bases based on Containment Checking' Professor ROUSSET maintains an active role in the scientific community through editorial work and organizational leadership. She serves on the Editorial Board of Communications of the ACM (CACM) and has held significant roles including PC chair of EGC 2019, Workshops co-Chair of WWW 2018, and Area Chair of IJCAI 2017. Her consistent service on program committees of major international conferences demonstrates her standing in the field. Her laboratory, the SLIDE group within LIG, focuses on semantic web technologies, knowledge representation, and data integration. The group maintains strong connections with the international research community and participates in collaborative projects addressing cutting-edge challenges in artificial intelligence and data management, with particular emphasis on trustworthy and explainable AI systems.