Professor Alsayed Algergawy is the substitute for the Chair of Data and Knowledge Engineering at the University of Passau since April 2023. His work bridges semantic web technologies and machine learning to enable heterogeneous data integration across domains. Current focus areas: schema/ontology alignment, entity resolution, knowledge graph construction Active in DFG Collaborative Research Center AquaDiva (data lifecycle management) Domain applications: biodiversity, agriculture, energy His team develops hybrid strategies combining rule-based systems with ML techniques to extract value from both structured and unstructured data sources.
Dr. Jason J. Corso is a Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan . His research focuses on high-level computer vision , video understanding , and the intersection with human language and robotics . His work emphasizes Bayesian approaches to segmentation and recognition, with applications spanning biomedicine and recreational video analysis . He is particularly known for contributions to video object segmentation , activity recognition , and vision-language frameworks . Scientific awards include: NSF CAREER award (2009) ARO Young Investigator award (2010) Google Faculty Research Award (2015) DARPA CSSG grant He also leads major projects like YouCook2 dataset , Video2Text.net , and LIBSVX framework.
Pierre-Henri Paris is an Associate Professor (Maître de Conférences) at Paris-Saclay University since September 2024. Previously, he worked as a Postdoctoral Researcher at Telecom Paris (Institut Polytechnique de Paris) from September 2020 to August 2024. His academic journey includes a PhD in Artificial Intelligence from Sorbonne University and CNAM (Conservatoire National des Arts et Métiers) completed in 2020. Education: PhD in Artificial Intelligence, 2020, Sorbonne University and CNAM M.Sc. in Artificial Intelligence, 2016, CNAM M.Sc. in Mathematics, 2008, CY Cergy Paris University (incomplete) Pierre-Henri Paris's research focuses on the intersection of artificial intelligence, knowledge representation, and natural language processing. His work particularly emphasizes knowledge graphs, entity linking, and data quality. He has made significant contributions to projects like YAGO 4.5, which enhances knowledge bases with cleaner, logically consistent structures, and MAFALDA, a benchmark for fallacy classification. His research often bridges theoretical foundations with practical applications, particularly in how knowledge can be effectively represented, extracted, and utilized in complex systems. His recent publications reveal a strong focus on knowledge graph enhancement, semantic representation, and natural language understanding. The work on YAGO 4.5 demonstrates his commitment to creating more robust knowledge bases, while MAFALDA shows his interest in the intersection of language understanding and logical reasoning. His research trajectory indicates a consistent exploration of how structured knowledge can be integrated with linguistic analysis to create more intelligent systems. Advising: PhD students: Simon Coumes (2022-), Chadi Helwe (2022-2024), François Amat (2022-) Master's students: Syrine El Aoud (2021), Ayoub Mountassir (2013-2015) Bachelor's students: Khalil Halloul (2013-2014) Pierre-Henri Paris is actively involved in teaching at Paris-Saclay University, where he instructs courses including Introduction to Machine Learning, Introduction to Neural Networks, Algorithms for Data Science, Databases, and Data Warehousing. His teaching reflects his research expertise, providing students with both theoretical foundations and practical applications in artificial intelligence and data science.
Dr. Teresa Wang is a Senior Lecturer in Data Science at Monash University's Faculty of Information Technology, specializing in entity/user modeling, relational/structural machine learning, and graph/network analysis. She holds a Ph.D. from the University of Queensland and degrees from Nanjing University. Currently, she directs the Master of Data Science Program and teaches courses like FIT5201 Machine Learning. Her research focuses on social, e-commerce, and health data modeling, with notable projects including the Knowledge Enriched Approach for Effective Personalization (2025–2027) and collaborations on AI in Mental Health and Site Safety. Dr. Wang has co-authored over 59 publications, emphasizing areas like ontology matching and multimodal data analysis. She actively supervises PhD students and contributes to initiatives like the CSIRO Next Generation Graduates Program for clean energy and sustainability. Education: Ph.D. in Computer Science (2017), University of Queensland Master of Computer Science (2013), Nanjing University Bachelor of Software Engineering (2010), Nanjing University Research Interests: Entity modeling, spatio-temporal data analysis, graph mining, recommender systems, and health/medical records mining. She explores applications in social media, e-commerce, and healthcare sectors. Projects: "Knowledge Enriched Approach for Effective Personalization" (2025–2027) "AI for Clean Energy and Sustainability" (2023–2027) "CSIRO Next Generation Graduates Program: AI in Mental Health" (2023–2027) "Large-scale multimodal knowledge management" (2022–2025) Grants & Collaborations: Engaged with CSIRO, Crank Group, and Pola Practice Pty Ltd. Her work aligns with UN SDGs in education and sustainable energy systems. Labs/Teams: Part of the Monash Energy Institute and Monash Data Futures Institute, contributing to interdisciplinary AI and energy research.
David Chalmers is a University Professor of Philosophy and Neural Science at New York University and co-director of the Center for Mind, Brain, and Consciousness. He is also an Honorary Professor of Philosophy at the Australian National University and co-director of the PhilPapers Foundation. His work bridges philosophy, cognitive science, and emerging technologies. Research Interests: Chalmers is best known for his work on the 'hard problem of consciousness'—the challenge of explaining subjective experience. His research spans philosophy of mind, metaphysics, epistemology, philosophy of language, and the foundations of physics and AI. He actively explores the implications of virtual reality, simulation theory, and large language models for philosophy and consciousness studies. Recent Research Trends: His recent publications focus on AI consciousness, the ethical treatment of AI systems, the simulation hypothesis, and the nature of thought in language models. These works reflect a growing engagement with artificial intelligence and digital metaphysics, positioning philosophy at the forefront of technological inquiry. Scientific Awards: While no specific awards are listed in the provided text, Chalmers is widely recognized as one of the most influential contemporary philosophers, particularly in philosophy of mind. Advising and Grants: He mentors students and postdocs, though specific names are not listed. His leadership in the Center for Mind, Brain, and Consciousness and the PhilPapers Foundation suggests active grant-funded research and academic collaboration. Labs and Teams: He co-directs the Center for Mind, Brain, and Consciousness at NYU and the PhilPapers Foundation , both of which support research, publications, and global philosophical discourse in philosophy of mind and related fields.
Jiaoyan Chen is a Senior Lecturer (Associate Professor) in the Department of Computer Science at The University of Manchester. She previously held roles as a Lecturer at Manchester, a Senior Researcher at the University of Oxford, and a Postdoctoral Fellow at Heidelberg University. Education: PhD and Bachelor's in Computer Science and Technology from Zhejiang University (2016 and 2011), with a visiting PhD stint at Zurich University's Department of Informatics. Research Interests: Integrating knowledge graphs and ontologies with machine learning and large language models (LLMs), focusing on semantic embeddings, knowledge curation, and explainable AI systems. Publication Trends show emphasis on ontology embeddings (e.g., OWL2Vec*), LLM evaluation with knowledge graphs, and hybrid neural-symbolic reasoning. Her work bridges structured knowledge and modern AI through projects like OntoEm and ConCur . Current Research Team includes postdoctoral researchers, PhD students, and externally co-supervised associates. She actively recruits PhD candidates in areas like Retrieval-Augmented Generation and LLM Explainability , with projects funded by EPSRC and international consortia. Grants & Leadership: EPSRC New Investigator Award (2023-2026) Manchester-Melbourne-Toronto Research Fund (2024-2026) EPSRC ConCur Project (2021-2025) Professional Service: Associate Editor, Transactions on Graph Data and Knowledge EPSRC Peer Review College member OAEI Track Co-organizer at ISWC
Asunción Gómez Pérez is a Spanish computer scientist and Full Professor at the Technical University of Madrid (UPM) . She currently serves as Vice-Rector for Research, Innovation and Doctoral Studies at UPM and holds a seat at the Real Academia Española . She has authored over 300 publications and accumulated 20,000 citations. Education : PhD in Computer Science (UPM, 1993), MBA (Comillas Pontifical University) Leadership Roles : Director of the Department of Artificial Intelligence (2008–2016), Academic Director of AI Master’s/PhD programs (2009–2016), Executive Director of UPM’s Artificial Intelligence Lab (1995–1998) Her research focuses on Semantic Web and Ontology Engineering , with applications in knowledge representation, machine-machine communication, and multilingual data integration. She pioneered methods for ontology validation, metadata licensing, and AI-driven social inclusion. Key publication trends include: Ontology evaluation frameworks (e.g., OOPS!) Linked Data quality models and validation tools Multilingual and cross-lingual AI applications Interoperability solutions for smart cities and healthcare Machine Learning for social exclusion prediction Ontology-driven library and lexicography systems Scientific Awards Fellow of the European Academy of Sciences Ada Byron Prize She has led projects like the NeOn Methodology for ontology development and contributed to the European framework for linked data rights (LD Terms). Her work bridges theoretical research with practical implementations in AI and Semantic Technologies.
Prof. Dr. Heiko Paulheim is a Professor of Data Science and currently serves as University Vice President at the University of Mannheim. He leads the Data and Web Science Group (DWS), which focuses on Web Data Mining, Knowledge Graphs, and Semantic Web technologies. His research group contributes to open source knowledge graphs like DBpedia and develops new knowledge graphs such as WebIsALOD and DBkWik. As of October 1, 2024, he has limited teaching capacity due to his vice presidential duties. Prof. Paulheim's research interests span Knowledge Graphs, Semantic Web, Web Data Mining, Machine Learning, and Natural Language Processing. His work particularly focuses on knowledge graph refinement, embedding techniques (notably RDF2vec), and applications in various domains including news recommendation, biomedical informatics, and environmental monitoring. His group develops practical tools like the RapidMiner Linked Open Data Extension and RDF2vec for knowledge graph applications. His recent publications demonstrate a strong focus on knowledge graph embeddings, with particular attention to RDF2vec variants, applications in news recommendation systems, biomedical data integration, and spatio-temporal knowledge graphs for environmental monitoring. His work bridges theoretical advances in knowledge representation with practical applications across multiple domains. Among his notable achievements are a nomination for the Best Paper Award at CAiSE 2025 and securing an Open Science Grant for the SpatialBenchRAG project. His research has significant impact in both academic and industrial contexts, with multiple papers accepted at top conferences like ISWC and ESWC. Prof. Paulheim has supervised numerous PhD students including Alexander Brinkmann and Michael Schlechtinger, and has led several research projects including the DFG Project Mine@LOD, State of BW Project SyKoW², and BMBF Project DS4DM. His group maintains strong industry connections with partners like SAP AG, Daimler AG, and IDS.
Natalia Villanueva-Rosales is an Associate Professor in the Department of Computer Science at The University of Texas at El Paso (UTEP). As Co-Principal Investigator at the NSF-funded Cyber-ShARE Center of Excellence, she leads the iLink Research Group focusing on semantic technologies and smart city initiatives. Ph.D. in Computer Science, Carleton University (2011) M.Sc. in Artificial Intelligence, University of Edinburgh (2005) B.Sc. in Computer Science & Statistics, Universidad Panamericana & CINVESTAV-IPN (2002) Her research bridges Semantic Web technologies with Smart Cities applications, particularly in Water Sustainability and Senior Mobility . Key projects include ontology-based frameworks for freight performance data integration and community-driven smart mobility solutions. Recent publications demonstrate her interdisciplinary approach across Environmental Informatics (2022-2025) and Urban Mobility (2019-2022). She holds editorial and leadership roles in semantic science initiatives while actively mentoring through the ACM-W WICS student group. 2019 HEENAC Education Award 2019 NCWIT Undergraduate Research Mentoring Award Her NSF grants include IRES-1658733 for US-Mexico Smart Cities collaboration and OAC-1835897 for the SWIM water sustainability project. The iLink Research Group under her leadership develops ontological frameworks for cross-domain data integration and trust establishment in collaborative environments.
Prof. Jacco van Ossenbruggen is a Full Professor in Intelligent Information Systems at Vrije Universiteit Amsterdam (VU), affiliated with the Network Institute. He serves on the Management Board of ODISSEI, a national research infrastructure for social sciences and economics. His academic background includes a PhD in Computer Science (2001) from VU’s Faculty of Science, focusing on hypermedia processing. Research Interests: His work centers on cultural AI, FAIR data principles, ontology engineering, and semantic web technologies. Key areas include inclusive cultural heritage metadata, bias mitigation in AI systems, and knowledge discovery via linked data. Recent projects involve leveraging large language models (LLMs) for metadata enrichment and ontology construction. Key Contributions: He leads initiatives like the Cultural AI Lab, exploring AI applications for cultural heritage. His research bridges technical innovations (e.g., semantic integration of restricted-access data) with societal impacts (e.g., ethical AI frameworks for public-sector applications). Developed frameworks for evaluating entity alignment in knowledge graphs Pioneered FAIR-aligned data management plans for scientific communities Designed tools like Alter Heritage for collaborative metadata curation Grants & Projects: Principal Investigator of the ODISSEI Portal project (2020–2024), advancing open data infrastructures. Active in funding initiatives promoting reproducible research and ethical data practices. Labs/Teams: Cultural AI Lab at VU, focusing on AI-driven solutions for cultural heritage preservation and accessibility.
Markus König is a Professor of Informatics in Civil Engineering at Ruhr University Bochum, where he has been researching and teaching since October 2009. His work focuses on Building Information Modeling (BIM), digital construction technologies, and civil engineering informatics, with significant contributions to the development and implementation of digital methods in German construction industry. Dr. König earned his degree in civil engineering with a focus on applied computer science at Leibniz University Hannover, where he also completed his doctorate on cooperative building planning at the Institute for Building Informatics. He subsequently held a junior professorship for Theoretical Methods of Project Management at Bauhaus University Weimar before joining Ruhr University Bochum. His research spans multiple cutting-edge areas including Building Information Modeling (BIM), construction process simulation, tunneling informatics, infrastructure asset management, and the application of artificial intelligence and computer vision in civil engineering. As chair of the Building Informatics Working Group from 2012-2016, he played a key role in developing the first national BIM curriculum for German universities and serves as editor of the book 'Building Information Modeling: Technological Foundations and Industrial Practice.' Analysis of his recent publications reveals a strong trend toward semantic technologies, digital twins, automated compliance checking, and the integration of AI in construction processes. His work increasingly focuses on information containers, ontology development, and the application of large language models to infrastructure data, reflecting the evolving landscape of digital construction. Dr. König's significant contributions to digital construction have been recognized with prestigious awards: Lower Saxony-Bremen Construction Industry Award (2017) for 'services in the development and introduction of digital construction in Germany' Konrad Zuse Medal (2020) While specific details about his advising and grant activities aren't explicitly mentioned in the provided text, his extensive publication record with numerous co-authors suggests active supervision of doctoral students and research staff. His involvement in multiple collaborative research projects is evident from his publication history. At Ruhr University Bochum, Professor König leads a research group focused on civil engineering informatics, with particular emphasis on BIM, digital construction technologies, and their application across the building lifecycle. His team appears to work at the intersection of computer science and civil engineering, developing innovative solutions for construction process optimization, infrastructure management, and digital transformation of the AEC industry.
Prof. Liqiu Meng serves as Chair of Cartography and Visual Analytics at the Technical University of Munich (TUM). He specializes in advanced geospatial research, digital cartography, and human-technology collaboration frameworks. Current Faculty at TUM Chair of Cartography and Visual Analytics Research Focus: His work bridges cartographic theory with cutting-edge technology, covering topics like 3D urban modeling, AI ethics visualization, geovisual analytics, and spatiotemporal data interpretation. Urban Morphology Analysis AI Ethics Cartography Geovisual Analytics 3D City Data Integration Location-Based Service Design Publications: Recent works (2025-2024) demonstrate expertise in explainable AI for urban analysis, multi-agent systems for geospatial interaction, and advanced spatial modeling techniques. Contact: liqiu.meng@tum.de | contact.lfk@ed.tum.de
Dr. Sebastian Hellmann is a senior researcher at the Institute of Computer Science , University of Leipzig , affiliated with the Business Information Systems department. He leads the Knowledge Integration and Language Technologies (KILT) Competence Center at InfAI and serves as executive director and board member of the DBpedia Association . His work spans semantic technologies, linked data, and knowledge graphs, with significant contributions to data curation, FAIR data principles, and natural language processing. PhD in Computer Science (2014) at University of Leipzig Contributed to open-source projects like DBpedia, NLP2RDF, and OWLG Author of over 80 peer-reviewed publications (h-index 21, 4300+ citations) Sebastian's research focuses on Knowledge Graphs , Ontology Management , and Data Interoperability , as evidenced by his publications and projects. Recent work includes ClassRank for knowledge graph summarization, DBpedia Databus for dataset management, and the Open Energy Ontology for energy systems analysis. His projects often bridge semantic web technologies with practical applications in data quality, integration, and user-centric tools. Selected publications highlight his expertise in Linked Data , Ontology Archiving , and Agile Knowledge Engineering . He actively participates in academic-industry collaborations through EU H2020 projects like ALIGNED and FREME , as well as the Smart Data Web initiative. His work with DBpedia, Wikidata, and the Semantic Web community underscores his commitment to advancing machine-readable knowledge representation.
Dr. Tim Oates is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County . His research spans machine learning, artificial intelligence, and brain-machine interfaces, with a focus on weakly supervised methods, human-in-the-loop reinforcement learning, and grounded policy development for robotics. Ph.D., Computer Science, University of Massachusetts, Amherst, 2000 M.S., Computer Science, University of Massachusetts, Amherst, 1997 B.S., Computer Science and Electrical Engineering, 1989 Current research threads include: Developing non-invasive brain injury severity assessment via medical time series Modeling human brain development through computational frameworks Designing algorithms for autonomous robotic learning Recent publications highlight AI security mechanisms (backdoor detection via tensor decomposition, matrix factorization) Medical applications (3D artery reconstruction, skin lesion diagnosis, EEG denoising) Neuro-symbolic integration (holographic representations, language-guided reinforcement learning) Mathematical reasoning (schema-based problem solving, subitizing algorithms) Contact: oates@cs.umbc.edu | Office: 336 Information Technology and Engineering (ITE) Building
Sylvie Delacroix is the Inaugural Jeff Price Chair in Digital Law and Director of the Centre for Data Futures at King's College London. She is also a Visiting Professor at Tohoku University, focusing on bridging theory and practice in ethics and public policy initiatives. Her research spans Data & machine ethics Ethical agency and habit Social sustainability of data ecosystems Participatory infrastructure for AI systems Uncertainty communication in LLMs Legal theory and digital law Recent publications address LLM interfaces as transitional spaces for democratic revival and sustainable data rivers in generative AI. Her work has attracted significant grant funding from the Patrick J. McGovern Foundation, Wellcome Trust, and Omidyar Network. Scientific awards include the Leverhulme Prize Peter Birks 2d Prize for Outstanding Legal Scholarship Montgomery Fellowship at Dartmouth College She supervises PhD research on large language models and uncertainty communication, as well as habit in ethical lives. Her policy contributions include advising the Law Society of England and Wales on algorithmic justice systems.
