Dr. Nyamsuren Enkhbold is a Lecturer in Computer Science (Software Engineering) at the School of Computer Science and Information Technology, University College Cork. He holds a PhD from the University of Groningen (Cognitive Modeling), an MSc from KAIST (Computer Science), and a BSc from Huree University (Computer Science). His research focuses on intelligent software development at the intersection of AI, Cognitive Science, and GIS, with notable contributions to geo-analytical question-answering systems and EU-funded projects like RAGE (serious games) and QuAnGIS (GIS workflow automation). Current interests include GenAI applications via fine-tuning and prompt engineering. He teaches CS3062 - Computing in Society CS6403 - Case Studies in Computing Entrepreneurship and previously contributed to courses at Utrecht University and KAIST. His work has been supported by a School of Computer Science Starter Fund (10k). Research highlights include developing reusable AI components for serious games and advancing GIS systems that automatically generate workflows from user queries. His publications span cognitive modeling, spatial semantics, and NLP-driven geo-analytical tools.
Jorge Lobo is an ICREA Research Professor at the Department of Information and Communication Technologies at Universitat Pompeu Fabra (UPF) and holds a Visiting Professor appointment at Imperial College London's Department of Computing. He holds a Ph.D. in Computer Science from the University of Maryland (1990), an M.S., and B.E. from Simon Bolivar University. His career includes roles at IBM Research, Bell Labs, and academic positions at the University of Illinois at Chicago. His research focuses on AI, Network & Distributed Systems Management, Security/Privacy, and policy-driven systems. He pioneered policy-based network management, developing the PDL language and contributing to policy enforcement frameworks in telecommunication networks. His work spans policy languages (XACML, PMAC), role mining algorithms, and security policy analysis. He holds 7 patents in policy technologies and has authored two books with over 100 refereed publications. Key contributions: Policy-based systems, neuro-symbolic learning, privacy-preserving frameworks, and declarative distributed computing Notable achievements: ACM Distinguished Scientist, IBM Identity Management Product implementations Recent publications (2020-2024) emphasize neuro-symbolic learning, federated learning frameworks, differential privacy, and AI-driven security solutions. His work bridges formal logic with machine learning to create interpretable systems for complex challenges in network management and policy enforcement. Labs/Teams: Active in UPF's Artificial Intelligence and Machine Learning group. Collaborates internationally on policy-aware systems and declarative networking architectures.
Dr. M. M. El-Gayar is a faculty member at Mansoura University, affiliated with the Faculty of Computer and Information and the Department of Computer Science. He is also associated with New Mansoura University, reflecting his active role in Egypt's evolving academic landscape in computer science and engineering. His research spans key domains in modern computing, focusing on Artificial Intelligence , Data Mining , Deep Learning , and their applications in healthcare , cybersecurity , and the Internet of Things (IoT) . His work integrates computational intelligence to solve real-world problems such as disease diagnosis, medical image analysis, and secure digital systems. The recent publications highlight a strong trend toward applying hybrid and deep learning models in medical diagnostics, including breast cancer and heart disease prediction, as well as semantic analysis for pneumonia detection in pandemic contexts. His IoT research emphasizes optimization for device localization, while cybersecurity efforts focus on phishing mitigation. These interdisciplinary efforts reflect a cohesive research vision centered on intelligent systems for societal benefit. Dr. El-Gayar has received research support from prestigious organizations including the United States Agency for International Development and the British Council , underscoring the international relevance and impact of his work. He has contributed to multiple high-impact journals such as IEEE Access , Computers, Materials and Continua , and the International Journal of Electrical and Computer Engineering , demonstrating consistent scholarly output and collaboration.
Gábor Bergmann is a researcher at Budapest University of Technology and Economics, Hungary, and a member of the MTA-BME Lendület Research Group on Cyber-Physical Systems. He actively contributes to the fields of model-driven engineering and incremental computing, with a focus on efficient query evaluation and program analysis. His research interests include: Model-Driven Engineering Incremental Computing Query Evaluation Program Analysis using Datalog Static and Whole-Program Analysis Cyber-Physical Systems The recent publications of Gábor Bergmann demonstrate a strong trend in incremental computation techniques applied to program and model analysis, particularly leveraging Datalog and lattice-based frameworks. His work focuses on optimizing performance and scalability of analyses through change-driven evaluation, with applications in model synchronization, inter-procedural analysis, and EMF-based systems. He has served on program committees, particularly in the Foundations Track of the MODELS conference series, indicating active engagement in the academic community. While no formal advising or grant information is available from the provided text, his involvement in a Lendület research group suggests participation in funded research projects. He has not received any explicitly mentioned scientific awards in the provided data.
Gerhard Weikum is a leading professor and researcher in the field of databases and information systems at the Max Planck Institute for Informatics, part of the Max Planck Society in Saarbrücken, Germany. He heads the Databases and Information Systems department, where his work bridges foundational database research with cutting-edge applications in knowledge graphs, information retrieval, and natural language processing. His research interests include knowledge graph construction and curation, data integration, question answering, conversational search, and the integration of large language models with structured knowledge. He has made significant contributions to the development of methods for creating comprehensive and reliable knowledge bases from web and text sources, with a focus on scalability, quality, and usability. The recent publications highlight a strong trend towards hybrid AI systems that combine the strengths of knowledge graphs and large language models, particularly in the domains of conversational search, recommendation systems, and complex question answering over heterogeneous data. His work emphasizes efficiency, robustness, and faithfulness in AI-generated responses. ACM Web Conference WSDM CIKM Advances in Information Retrieval Journal of Web Semantics IEEE TKDE EMNLP He has advised numerous PhD students and researchers who have gone on to make their own contributions in areas such as knowledge base completion, data curation, and question answering. His scientific work is highly collaborative, involving researchers from various institutions and industrial partners. Gerhard Weikum is a central figure in the database and semantic web research communities, with a sustained record of high-impact publications and leadership in advancing the field of information systems.
Prof. Dr. Matthias Grabmair is a Professor of Legal Tech at the TUM School of Computation, Information and Technology, part of the Technische Universität München (TUM). His research focuses on advancing computational methods for legal analysis, particularly leveraging natural language processing (NLP) to enhance legal document summarization, judgment prediction, and AI-driven legal systems. He leads the JUSMOD organization and has contributed to datasets like ECtHR-PCR for precedent understanding. Key research areas include automated legal reporting (e.g., LexGenie), cross-jurisdictional analysis, and ethical AI applications in privacy policies (PrivaT5). His work emphasizes improving judicial decision-making transparency through models like Hiculr for rhetorical role labeling and Chronoslex for temporal generalization. Prof. Grabmair also explores adversarial robustness in legal AI systems and transfer learning across legal domains. Publications span 2005–2025, with recent emphasis on generative AI for legal texts, explainable AI in judgment prediction, and curriculum learning for legal document analysis. His research bridges computational methods with legal practice, addressing challenges in fairness, reliability, and scalability of AI applications in law.
Ryen W. White is a Partner Research Director and Deputy Lab Director at Microsoft Research in Redmond, leading the LEAP (Language, Learning, Audio, Privacy) research area. He also serves as an Affiliate Full Professor at the University of Washington. His research focuses on multi-agent systems (AutoGen), action engines, and computational health. He has contributed to Microsoft products like Bing, Xbox, and Azure. Recognized as an ACM Fellow (2021), he leads roles in ACM SIGIR and ACM Transactions on the Web. He chairs conferences such as CHIIR and has received awards including the Tony Kent Strix Award (2022) and multiple SIGIR Best Paper awards. His PhD from the University of Glasgow (2005) was awarded the BCS Distinguished Dissertation Award. Education: PhD in Computing Science, University of Glasgow (2005) Key Roles: Vice Chair of ACM SIGIR, Editor-in-Chief of ACM Transactions on the Web Research Interests: Exploratory search, human-computer interaction, and generative AI in search systems. His work bridges theoretical contributions with practical applications in healthcare, gaming, and enterprise tools. Recent focus areas include task intelligence frameworks and ethical AI integration. Awards: SIGIR 2023 Test of Time Award, SIGIR 2022 Test of Time Award, SIGCHI Academy Membership Leadership in organizing conferences like SIGIR, CHIIR, and WWW. Co-authored influential books on information access and search systems. Advised PhD committees at top institutions and continues to drive interdisciplinary research collaborations.
Ana Cleveland is a Regents Professor and Sarah Law Kennerly Endowed Professor at the University of North Texas (UNT), directing the Health Informatics Program. She holds leadership roles in Discovery Park and has expertise in health informatics, medical librarianship, and disaster information management. Education: Ph.D. and M.S. from Case Western Reserve University, B.S. from University of Texas. Her research focuses on health information-seeking behaviors, medical literature indexing, and academic library innovation. She leads the UNTIIA Lab and participates in TREC clinical trial research initiatives. Key areas include disaster informatics, health sciences librarianship, and precision medicine information systems. Her work bridges digital health resources with real-world applications in public health and clinical practice. Notable contributions include evaluating AI-driven medical data tools, analyzing pandemic response strategies, and advancing educational programs for health information professionals. Her research spans over four decades with a focus on improving healthcare information accessibility and quality.
Laurence Hirsch is a Senior Lecturer in the Computing department at Sheffield Hallam University since 2009. His research focuses on evolutionary algorithms, text classification, fog computing, and crisis management. He has contributed to advancements in document clustering using genetic programming and developed decision support systems for cloud migration in SMEs. His work spans interdisciplinary areas including healthcare technology, transportation safety, and organized crime detection. Key research interests include: Evolutionary search techniques for information retrieval Interpretable machine learning models Fog computing applications in surgical environments Social media analytics for disaster response Cloud adoption strategies for small businesses Notable publications include groundbreaking work on evolved search query classifiers (2004–2021), fog computing in anaesthesia monitoring (2024), and crisis management systems leveraging crowdsourced data (2016–2017). His research bridges theoretical computer science with practical applications in healthcare, transportation, and public safety domains. He has collaborated on multi-disciplinary projects involving SME cloud migration decision tools (CMDSSI system) and formal concept analysis for crime detection. His work emphasizes human-centric approaches to technology, including motorcycle safety perceptual countermeasures and argumentative learning tools.
Giuseppe Pirro is an Associate Professor at the Department of Computer Science, Sapienza University of Rome. His research focuses on Semantic Web technologies, graph databases, and adversarial social network analysis. Specializes in RDF(S) inference, ontology alignment, and knowledge graph analytics Develops methods for community deception in networks and privacy-preserving data publishing Active in temporal query languages and web cartography Research trends include: Advancing graph embedding techniques for knowledge representation Exploring intersection of differential privacy and neural networks Designing novel algorithms for community detection and network deception Improving query languages for semantic web and temporal data Scientific awards: ERC PE6_7 grant ERC PE6_10 grant KET Big data & computing recognition
Marcelo Arenas is a Professor at the Department of Computer Science and the Institute for Mathematical and Computational Engineering at the Pontifical Catholic University of Chile. He is a Fellow of the Association for Computing Machinery (ACM), former director of the Millennium Institute for Foundational Research on Data, and co-founder of the Center for Semantic Web Research. His Ph.D. in Computer Science was obtained from the University of Toronto in 2005. Research interests include data management , applications of logic in computer science , and Semantic Web technologies. He has published extensively on topics such as SPARQL query complexity , graph database systems , and incomplete database theory , with notable works like MillenniumDB and Foundations of Data Exchange . Scientific accolades: 2016 SWSA Ten-Year Award for "Semantics and Complexity of SPARQL" IBM Ph.D. Fellowship (2004) Nine Best Paper Awards across PODS, ISWC, ICDT, ESWC, WWW, and NeurIPS His work on approximate counting algorithms (e.g., FPRAS for #NFA) and explainable AI frameworks has influenced database theory, while serving on program committees for ICDT 2015, ISWC 2015, and PODS 2018 demonstrates leadership in the field. Current projects focus on probabilistic explanations for decision trees and temporal regular path queries in knowledge graphs.
Professor Ansgar Scherp is a leading academic in Data Science and Big Data Analytics at Ulm University , Germany. His career spans multiple institutions including University of Essex, University of Stirling, and Kiel University, where he led the EU Horizon 2020 MOVING project. He holds a habilitation thesis on Semantic Media Management and has pioneered research in combining symbolic and statistical methods for data analysis. Current: Professor for Data Science & Big Data Analytics (Ulm University) Previous: Professor for Knowledge Discovery (Kiel University), Juniorprofessor roles (Mannheim, Koblenz-Landau) Skills: Semantic Web, Graph Mining, Text Analytics, Multimedia Systems His research interests focus on integrating Information Retrieval, Data Mining, Machine Learning, and Semantic Web technologies. He has developed innovative methods like HCF-IDF for semantic profiling and SchemEX for schema-level indexing of Linked Data. His work addresses challenges in media companies' digital transformation and large-scale Knowledge Graph analysis. Scientific awards include: Best Paper Award, Symposium on Document Engineering (2021) Best Student Paper Award, Symposium on Document Engineering (2019) ACM SIGMM Rising Stars Symposium Speaker (2016) Double Winner of Billion Triple Challenge (2008, 2011) klickTel Award (2013) He has supervised 12 PhD theses and over 15 Master's theses , with students now working at Google, Capgemini, and academic institutions. His email addresses are ansgar.scherp@uni-ulm.de and mail@ansgarscherp.net .
Robert A. Jenders, MD, MS, FACP, FACMI is a Professor of Medicine at Charles R. Drew University of Medicine and Science (CDU) and Co-Director of its Center for Biomedical Informatics. He also rejoined the faculty of the University of California, Los Angeles (UCLA) in September 2012, with clinical and teaching responsibilities at Harbor-UCLA Medical Center. His academic affiliations span CDU, UCLA, and past appointments at Columbia University, Cedars-Sinai Medical Center, and Georgetown University Hospital. Dr. Jenders holds a BS in computer science from Marquette University, an MD from the University of Wisconsin-Madison, and an MS in computer science from Northeastern University. He completed medical training and a National Library of Medicine fellowship in medical informatics at the Harvard-MIT Division of Health Science and Technology. His research focuses on clinical decision support, knowledge representation, and health information technology standards, particularly in the context of electronic health records. He has made significant contributions to the development and evolution of the Arden Syntax, FHIR, and SNOMED CT as tools for encoding clinical knowledge. His work bridges biomedical informatics, public health, and clinical medicine, emphasizing interoperability and data standardization. The trends in his recent publications highlight a sustained focus on evaluating and advancing health IT standards such as FHIR, DMN, and Arden Syntax for clinical knowledge representation. His research emphasizes interoperability, rule-based decision support, and the integration of standardized terminologies into real-world health systems. Co-author of Improving Outcomes with Clinical Decision Support , named 2005 Book of the Year by HIMSS Fellow, American College of Physicians (FACP) Fellow, American College of Medical Informatics (FACMI) Dr. Jenders has served in leadership roles in major health IT organizations, including as co-chair of the HL7 Clinical Decision Support Work Group since 1998, editor of the AMIA clinical informatics subspecialty exam, and member of scientific program committees for AMIA symposia. He has received no explicit mention of grants in the provided text but has been involved in policy-relevant work referenced in patents and clinical guidelines. He has advised or collaborated with researchers such as Sheba George and David Martins. He has contributed to editorial boards and served as a reviewer for leading journals like the International Journal of Medical Informatics . Dr. Jenders has been instrumental in the Center for Biomedical Informatics at CDU and has contributed to national efforts in immunization registries, laboratory data exchange, and computer-assisted coding through roles in HL7, AHIMA, CCHIT, and the Office of the National Coordinator for Health IT.
Prof. Dr. Michael Elberfeld is a faculty member at the Technical University of Central Hesse , affiliated with the Department of Mathematics, Natural Sciences and Computer Science . His research focuses on theoretical computer science, particularly in computational and parameterized complexity, logic in computer science, and algorithmic meta-theorems. Education : Doctorate in Theoretical Computer Science from the University of Lübeck (2012); Diploma (MSc) in Computer Science from the University of Lübeck (2007). His work explores the intersection of graph theory , logic , and space-bounded computation , with groundbreaking contributions to problems on bounded tree-width structures and order-invariant logics . He has developed logspace algorithms for graph canonization and studied succinctness tradeoffs in logical formalisms. Key publications include analyses of parameterized space complexity , algorithmic meta-theorems , and applications to bioinformatics like haplotype inference and network orientation. His research is supported by the European Commission through grants 648276 and P 28699 . He collaborates extensively with researchers such as Martin Grohe , Pascal Schweitzer , and Till Tantau , bridging theoretical logic with practical applications in staff rostering and biological network analysis .
Hakan GÜLDAL is an Assistant Professor at the Faculty of Education, Trakya University, with a research focus on educational technology and data mining applications in education. He holds degrees in Computer Engineering (BSc, MSc, PhD) and teaches courses in Database Management Systems and Programming Languages . BSc, MSc, PhD in Computer Engineering from Trakya University Assistant Professor since 2018 Specializes in machine learning for educational analytics His research explores technology acceptance models, cloud-based learning systems, and classification algorithms applied to educational datasets. Recent work examines chatbots as educational tools and predictive modeling of student performance. Publications span 2010-2024, with a focus on Cloud computing in education Machine learning for student assessment Learning management system analysis He contributes to international conferences and journals in educational technology. Teaching includes foundational courses in database systems and programming languages, reflecting his technical background.
