Arno Siebes is Professor of Algorithmic Data Analysis in the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. His research focuses on data mining methodologies, particularly pattern mining and Minimum Description Length (MDL) principles. Key research areas include: Developing efficient algorithms for pattern discovery Applying MDL to data characterization Creating interpretable models for complex datasets Addressing challenges in data science education Recent publications demonstrate applications in diverse domains including mobility analysis, genomic screening, and pandemic response. His work combines theoretical foundations with practical implementations for knowledge discovery.
Mel Chekol is an Assistant Professor at Utrecht University, affiliated with the Data Intensive Systems group within the Science faculty. He holds a PhD from INRIA Rhône-Alpes and a double MSc from Vienna University of Technology and Free University of Bozen-Bolzano. His research focuses on knowledge graphs, spatio-temporal data integration, probabilistic inference, and scalable machine learning applications. Previously, he worked at institutions including INRIA Nancy Grand Est, University of Mannheim, and the National Institute of Informatics in Tokyo. Key research interests include reasoning in knowledge graphs, temporal data modeling, and applying language models to enhance knowledge representation. He contributes to projects like the Utrecht Platform for Applied Data Science and has collaborated on frameworks such as the EXMO and WAM teams. His work emphasizes practical applications of AI and data science in governance and sustainability. Mel has published extensively in venues like VLDB Journal, ISWC, and AAAI, focusing on topics like rule learning, temporal knowledge graphs, and scalable inference systems. His research bridges theoretical advancements with real-world data challenges.
Matthijs van Leeuwen is an Associate Professor and Director of Education at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University. He leads the Explanatory Data Analysis group and is affiliated with the university-wide SAILS AI research program. Academic rank: Associate Professor Institution: Leiden University Research group: Explanatory Data Analysis University affiliation: SAILS AI research program His research focuses on exploratory data mining with emphasis on explainability for domain experts. Key areas include pattern discovery, anomaly detection, and human-in-the-loop systems. He applies information-theoretic concepts like Minimum Description Length (MDL) and develops interactive methods for real-world applications in life sciences, social sciences, manufacturing, and healthcare. Recent publications demonstrate his work in explainable AI through diverse subgroup discovery, graph anomaly detection, and medical data analysis. Notable contributions include synthetic health record generation, wearable sensor data interpretation, and hemoglobin deferral prediction models. Scientific Awards : Senior Teaching Qualification (SKO) certificate (2022) As Director of Education for LIACS' Master's programs, he plays a leadership role in academic training while maintaining active research partnerships across multiple domains including manufacturing, aviation, and medical informatics.
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.
