Marco Viviani is a researcher affiliated with University of Milano-Bicocca (Italy) and University of Torino (Candiolo, Italy), focusing on information retrieval , health informatics , and social media credibility . His work spans multi-dimensional relevance assessment generative AI applications misinformation detection privacy-aware systems Research highlights include ROMCIR workshops on reducing online misinformation, Web2Vec for structural content analysis, and LLM reasoning capabilities evaluation. Key collaborations with Gabriella Pasi, Rishabh Upadhyay, and Marinella Petrocchi appear across 103 publications from 2004-2025. His work integrates machine learning and knowledge graphs for healthcare applications, with recent focus on blockchain social media dynamics and privacy-utility trade-offs . He contributes to ECIR WI/IAT CLEF eHealth MDAI conferences and journals like IEEE Access and Frontiers in Artificial Intelligence .
Dr. Monica Lestari Paramita is a Lecturer in Data Science at the School of Information, Journalism and Communication , University of Sheffield. She obtained her BSc in Computer Science (2006) from the University of Indonesia, followed by an MSc (2008) and PhD (2019) in Information Management from the University of Sheffield. BSc in Computer Science, University of Indonesia (2006) MSc in Information Management, University of Sheffield (2008) PhD in Information Management, University of Sheffield (2019) Dr. Paramita's research focuses on bias and transparency in information retrieval , with a particular interest in designing bias-aware search engines and advancing multilingual information access . Her work explores cross-lingual similarity in Wikipedia, aiming to develop methods for measuring such similarities and understanding their implications for diverse user groups. Her recent publications address topics like algorithmic bias , user awareness , and multilingual NLP . These works span areas including AI ethics , digital scholarship , and cultural heritage access , often integrating participatory design and search engine evaluation . Dr. Paramita contributes to teaching modules such as INF113 - Data-Driven Organisations , INF214 - Using Data for Responsible Decision Making , and postgraduate courses on information retrieval and data science. She also co-leads the Shef.AI Interest Group on Fairness, Accountability, Transparency, and Ethics (FATE) in AI.
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.
Yannis Velegrakis is a Professor in the Department of Information and Computing Sciences at Utrecht University, where he holds the chair of Very Large Data Management. He leads the Data Intensive Systems research group and is the program leader for the Master’s in Data Science . He is also a part-time faculty member at the University of Trento and a Principal Investigator at the Archimedes AI and Data Science hub, Athena Research Center . Education: PhD in Computer Science, University of Toronto MSc in Computer Science, University of Crete BSc in Computer Science, University of Crete His research focuses on Big Data Management & Analytics , Knowledge Discovery , Graph Management , Data Integration , and Data Quality . His work combines theoretical rigor with practical system development, emphasizing human-centered approaches to data exploration and analysis. He has pioneered methods in example-based search, entity resolution, and dynamic graph analysis. His recent publications reflect a strong trend in knowledge graph exploration , data quality assessment , and dynamic network mining . These works often integrate machine learning with database systems to support interactive and intelligent data discovery. His research bridges foundational data management with applied AI, particularly in the context of real-world data challenges. Scientific Leadership and Awards: PC Chair, ICDE 2024 General Chair, VLDB 2013 PC Chair, EDBT 2021 He has advised numerous researchers and contributed to major projects in data integration and semantic search. His secondary activities include visiting positions at IBM Almaden , AT&T Research Labs , UC Santa Cruz , and University of Paris-Saclay . He has secured funding through collaborative research initiatives and has served on program committees of top international conferences. He leads the Data Intensive Systems group, which develops innovative tools for managing and exploring large-scale, heterogeneous datasets. The group emphasizes systems that support intuitive, example-driven interaction with complex data, aligning with the principles of human-centered AI.
Rodrigo Simões serves as a Guest Assistant at the Department of Information Science and Technology (ISTA) and as a Research Assistant at ISTAR-Iscte - Research Center in Information Sciences, Technologies and Architecture, both part of ISCTE—University Institute of Lisbon. He teaches Programming and Big Data Processing courses for the Bachelor's in Data Science program and contributes to software engineering research with practical urban applications. His academic qualifications include: PhD in Information Sciences and Technologies (expected 2028) from ISCTE—University Institute of Lisbon Master's degree in Computer Engineering (2024) from ISCTE—University Institute of Lisbon Bachelor's degree in Computer Engineering (2018) from New University of Lisbon Rodrigo's research focuses on applying Software Language Engineering to urban management platforms and smart cities. His expertise spans data visualization, geotemporal analysis, and tourism digital transformation, developing practical tools to address overtourism challenges for municipal authorities and tourism SMEs. His technical background combines academic research with over three years of industry experience in software development for Portugal's Agency for Administrative Modernization. His publication portfolio demonstrates consistent focus on tourism crowding management and visualization, with recent work examining pedestrian carrying capacity using OpenStreetMap data and developing digital transformation frameworks for tourism management. These publications appear in reputable conferences including ENTER International eTourism Conference and Joint International Conference on Digital Arts, Media and Technology, showing growing citation impact across Scopus and Google Scholar. Rodrigo actively participates in significant research initiatives including the EUROSTIT project where he develops chatbots for querying smart tourism tools, and the European RESETTING project focused on tourism digital transformation. His Master's thesis developed a geotemporal crowding visualization platform specifically designed to help tourism SMEs mitigate overtourism effects through data-driven decision making.
Leili Lind is an Adjunct Associate Professor at Linköping University's Department of Biomedical Engineering (IMT), part of the Division of Biomedical Engineering (MT). Her research focuses on health informatics, telemedicine, and chronic disease management, with a particular emphasis on COPD and heart failure. She leads projects integrating digital technologies like telemonitoring systems to improve patient outcomes and reduce hospitalizations in elderly populations. Key research areas include predictive modeling using small datasets, symptom tracking via digital tools, and semantic interoperability in healthcare systems. Lind has published extensively on telehealth applications, including studies demonstrating reduced hospital readmissions through telemonitoring interventions. Her work bridges biomedical engineering with clinical practice, emphasizing practical solutions for multimorbidity management and patient-centered care. Notable contributions include advancements in digital pen-based telemonitoring systems and rdf stream processing for healthcare data. Lind collaborates with interdisciplinary teams to develop innovative tools for early detection of disease exacerbations and improving quality of life for chronically ill patients.
Nate Chambers is a Professor in the Department of Computer Science at the United States Naval Academy (USNA), where he also serves as Co-Director of the Center for High Performance Computing. His research spans natural language processing, machine learning, and the understanding of events and commonsense reasoning in large language models. He has received significant recognition, including the 2020 Faculty Award for Excellence in Research at USNA and a Best Paper Award at the WNUT Workshop. Education Ph.D. in Computer Science, Stanford University (2011) M.S. in Computer Science, University of Rochester (2003) B.S. in Computer Science, University of Rochester (2002) Research Interests Nate Chambers focuses on advancing the field of natural language processing through innovative applications of machine learning. His work delves into how large language models can understand and reason about events, temporal dependencies, and commonsense knowledge. He is particularly interested in adversarial inputs to these models and their implications for cybersecurity and ethical AI, especially in combating human trafficking . Teaching and Mentorship At USNA, Nate teaches courses such as Natural Language Processing and Data Science and Programming . He is actively involved in curriculum development, having contributed to the new Data Science major. He welcomes undergraduate students interested in research topics ranging from large language model semantics to information extraction. Leadership and Service Chair of the USNA Committee on Generative AI Organizer for the Summer Faculty Workshop on Generative AI Chair of the Data Science Electives Committee
Pasquale De Meo is a Professor at the Department of Computer Engineering, Modeling, Electronics and Systems at Mediterranea University of Reggio Calabria, Italy. With over 178 publications spanning from 2003 to 2025, he has established himself as a leading researcher in social network analysis, trust modeling, and complex systems. His work frequently appears in top-tier journals including IEEE Transactions, Expert Systems with Applications, and Knowledge-Based Systems. De Meo's research focuses on social network analysis, trust modeling, criminal network analysis (particularly Sicilian Mafia operations), graph theory, and machine learning applications to networks. His work combines theoretical network science with practical applications in security, recommendation systems, and data mining. He has pioneered approaches for identifying key nodes in criminal networks, developing trust prediction models, and creating robust community detection algorithms. His recent publications (2023-2025) demonstrate a strong trend toward integrating quantum-inspired methods with traditional network analysis, developing advanced recommendation systems that address cold-start problems, and applying deep learning techniques to complex network structures. His work bridges theoretical computer science with practical security applications, particularly in fraud detection and criminal network disruption. De Meo has collaborated extensively with researchers including Giacomo Fiumara (61 papers), Domenico Ursino (57 papers), Giovanni Quattrone (40 papers), and Emilio Ferrara (37 papers), forming a productive research network focused on complex systems and network science.
Philipp Cimiano is a Professor at Bielefeld University, Germany , with a prolific research record in Artificial Intelligence, Semantic Web, Natural Language Processing, Knowledge Graphs, Explainable AI, Clinical Decision Support Systems, Ontology Engineering, and Federated Learning . His work bridges theoretical AI concepts with practical applications in healthcare and robotics. Key research themes include dialogue-based XAI for user understanding, federated learning for healthcare data privacy, and LLM-driven robotics for embodied commonsense reasoning. Recent publications analyze dynamic explanatory interactions , perspectivized argumentation frameworks , and benchmarks for robot manipulation using large language models. His collaborations span institutions in Germany and Europe, with frequent co-authorship on topics like counterfactual generation , stakeholder group analysis , and lexicalization in QALD systems .
Dr. Tolga Berber is an Assistant Professor at the Faculty of Science, Karadeniz Technical University . With a PhD in Computer Engineering from Dokuz Eylül University, his academic career spans over 20 years, including roles as Deputy Head of Department (2013–2023) and extensive research in Computer Sciences, Artificial Intelligence, and Medical Informatics .
Professor Kung Chen serves as a Professor in the Department of Management Information Systems at National Chengchi University (NCCU) in Taipei, Taiwan. With over two decades of academic experience, he has established himself as a leading researcher in blockchain technology, financial technology, and cybersecurity. His work bridges theoretical computer science with practical business applications, particularly in the financial sector. Ph.D. in Computer Science, Yale University (1989-1994) M.S. in Computer Science, National Taiwan University (1985-1987) B.S. in Computer Science, National Taiwan University (1981-1985) Professor Chen's research primarily focuses on blockchain applications, cybersecurity solutions, and data privacy technologies. His work explores practical implementations of blockchain in financial systems, secure data management frameworks, and privacy-preserving protocols. He has made significant contributions to smart contract development, consensus algorithms, and blockchain integration with IoT systems. His research demonstrates strong interdisciplinary collaboration across computer science, business, and policy domains. Analysis of Professor Chen's recent publications reveals a strong focus on blockchain technology (45%), cybersecurity (25%), and IoT systems (15%). His work shows a clear evolution from foundational research in aspect-oriented programming toward applied financial technology solutions. The interdisciplinary nature of his publications spans computer science, medical informatics, and public policy domains, reflecting his ability to connect technical innovations with real-world applications. Senior Excellent Teacher Award (20 years service) Senior Excellent Teacher Award (10 years service) Distinguished Professor at National Chengchi University Special Outstanding Talent Award from National Science Council Excellent Research Award for Internationalization (multiple years) Type A Research Award from National Science Council Professor Chen has secured substantial research funding as Principal Investigator for numerous projects totaling millions of dollars, primarily from Taiwan's National Science and Technology Council and industry partnerships. His grants focus on blockchain applications in finance, cybersecurity solutions, and academic network infrastructure. He has led major initiatives including the Financial Technology Innovation Operations Research Center and various blockchain laboratory development projects across multiple institutions. Professor Chen is affiliated with several research laboratories at NCCU, including the Lab of Intelligent Finances and Smart Contracts, Blockchain and Financial Technology Innovation Lab, and the E-Business Lab. His research teams typically include interdisciplinary members from computer science, business, and policy backgrounds, reflecting the applied nature of his work. He frequently collaborates with industry partners, particularly in the financial sector, to ensure practical relevance of his research findings.
Peter Gjøl Jensen is an Associate Professor in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. His research spans formal methods, artificial intelligence, and practical applications in energy systems. He is affiliated with multiple research groups including Distributed, Embedded and Intelligent Systems, AI for the People, and Artificial Intelligence and Machine Learning. His research interests focus on Model Checking , Formal Verification , Reinforcement Learning , and applications to Cyber-physical Systems . He has developed expertise in applying theoretical computer science to solve real-world problems, particularly in energy optimization and heat pump control systems. His work bridges the gap between formal methods and practical engineering applications. His recent publications show a clear trend toward applying AI and formal verification techniques to energy systems and cyber-physical applications. The research spans theoretical foundations of model checking and reaches into practical implementations for heat pump control, autonomous systems, and environmental management. His work often involves the UPPAAL and Stratego frameworks for verification and synthesis. Dr. Jensen actively supervises PhD students, including Andreas Holck Hoeg-Petersen on the "Explainable and Causally Enforced Reinforcement Learning" project. His research has attracted media attention, particularly for intelligent heat pump control systems that provide significant energy savings. He is involved in multiple research projects including "Explainable and Causally Enforced Reinforcement Learning" (ongoing) and "BEO-COVID: Decision Support for Evaluation and Optimization in UPPAAL" (completed in 2020).
Myriam Bras is a Professor at the Cognition, Languages, Ergonomics (CLLE) research center at the University of Toulouse - Jean Jaurès. She serves as Co-head of the Languages and Language team and Co-head of the OCRE theme focused on Occitan, Romance Languages, and European Languages. Her work bridges theoretical linguistics with practical applications for language preservation and education. Professor Bras specializes in semantics, pragmatics, and discourse analysis, with particular expertise in the Occitan language. Her research encompasses temporal structures of discourse, semantics of time across lexicon, grammar and discourse, and situated linguistics focusing on language comparison in educational settings. She has developed significant computational resources for Occitan including the BaTelÒc database and TALòc tools, addressing the critical need for documentation of this UNESCO-classified endangered language. Her recent publications (2023-2025) demonstrate a strong focus on Occitan language processing, discourse coherence analysis, and developing linguistic resources for under-resourced languages. These works reveal consistent patterns of interdisciplinary collaboration across computational linguistics, language documentation, and educational applications, particularly through European-funded projects like LINGUATEC and LINGUATEC-IA. Professor Bras has secured substantial research funding through multiple European and national projects including LINGUATEC (2018-2021), LINGUATEC-IA (2024-2026), DiViTal (2021-2024), and RESTAURE (2015-2018). Her work demonstrates significant commitment to both theoretical linguistics and practical applications for language preservation, particularly for minority languages of France. She leads the development of critical language resources including the CorpusArièja (a collection of 72 texts in Occitan with dialectal and spelling variation), the ParCoLab Parallel Corpus, and Loflòc (a morphological lexicon for Occitan using Universal Dependencies). These resources support both academic research and practical language revitalization efforts.
Ariel Deardorff is the Director of Data Science & Open Scholarship at the University of California, San Francisco (UCSF) Library . She leads a team focused on empowering researchers through open research practices and reproducible data-driven science. Education: University of Washington - BA (2010) University of British Columbia - MLIS (2014) UCSF - Diversity, Equity, and Inclusion Champion Training (2021) Her research interests span open science , data management , reproducible research workflows , and health information systems . She explores how libraries can enable open research practices and develop data infrastructure. Ariel's publications (2014-2021) show concentration in library science , biomedical research , and data visualization , with a focus on computational reproducibility , open scholarship , and health data systems . Her work includes methodological approaches like protocol-driven algorithms and low-cost computing for research education.
Jacques CHABIN serves as a Lecturer at the University of Orleans, affiliated with the LIFO (Laboratoire d'Informatique Fondamentale d'Orléans) research laboratory. His academic role centers on advancing database theory and semantic technologies within the institution's computer science research ecosystem. His research spans database systems with emphasis on semantic web architectures, graph database evolution, and formal language applications. Key investigations include RDF/S schema management, null value handling in incomplete databases, and context-driven querying systems for urban graph analysis. Recent work addresses privacy-preserving techniques in semantic networks and consistency maintenance during database evolution. Analysis of his 15 most recent publications reveals persistent focus on graph-based data management systems. The 2020-2024 output demonstrates progression from foundational XML schema evolution toward contemporary property graph challenges, with consistent attention to data consistency, constraint enforcement, and practical tool development like DataFix for database repair. As an active member of LIFO, Chabin contributes to the laboratory's mission in fundamental computer science research. His collaborative work with researchers including Mirian Halfeld-Ferrari and Nicolas Hiot reflects integration within the laboratory's database systems research group focused on theoretical and applied data management challenges.
