Nikolay Yakovets is an Assistant Professor at the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). His research focuses on databases and data-intensive systems with specialization in graph data management, query processing optimization, and high-performance data engineering. He leads academic courses including Data Modeling, Database Technology, and Engineering Data-Intensive Systems. Education background includes: PhD in Computer Science from York University (2017) MSc and BSc in Computer Science from York University Research expertise spans: Foundational database technologies Graph query optimization algorithms Cardinality estimation methods Knowledge graph construction High-performance data processing architectures Publication trends show consistent focus on graph database systems, with recent works advancing property graph management, cardinality estimation techniques, and ontology-driven knowledge graphs. Research demonstrates strong emphasis on practical system efficiency and theoretical foundations. Active in student supervision with 41 supervised works recorded. No awards mentioned in source materials.
Dr. Boudewijn van Dongen is a Full Professor at Eindhoven University of Technology's Department of Mathematics and Computer Science, chairing the Process Analytics group. His research focuses on conformance checking and process mining , particularly through alignment-based techniques. Develops ProM and CPN Tools frameworks Collaborates with Philips Research and Vanderlande Leads IEEE Task Force on Process Mining Research Interests : Specializes in aligning observed data with process models, addressing multi-case dependencies , privacy-aware mining , and stream processing . Project Highlights : • Strategic partnership with Philips on health data science • Logistics optimization with Vanderlande Industries • Foundational work in alignment theory and behavioral diagnostics
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.
J.C. Scholtes is an Extra-ordinary Professor of Text Mining at the Department of Knowledge Engineering, Faculty of Science and Engineering, University of Maastricht. He is also a Senior Research Fellow at the Dutch School for Information and Knowledge Systems (SIKS), a Board Member at IPRally, and a Venture Partner at ENDEIT Capital. M.Sc. in Computer Science from Delft University of Technology Ph.D. in Computational Linguistics from University of Amsterdam His research expertise spans Natural Language Processing, Machine Learning, and Artificial Intelligence, with applications in legal, medical, business, and regulatory domains. He focuses on text mining, machine translation, question-answering systems, and information extraction from unstructured data. Recent publications (2024–2025) emphasize context-aware machine translation, misinformation detection in recommendation systems, healthcare data analysis, and food science applications. Key themes include integrating deep learning architectures (e.g., Transformers), optimizing search and translation efficiency, and leveraging hybrid human-machine approaches. He has collaborated widely in industry and academia, notably deploying e-discovery software for institutions like the UN War Crimes Tribunals and FBI-ENRON. His career history includes leadership roles at ZyLAB (1987–2021) and prior service in the Royal Dutch Navy.
Chuan Meng is a researcher transitioning to a postdoctoral position at the University of Edinburgh's Natural Language Processing Group in September 2025. He obtained his PhD in Artificial Intelligence from the University of Amsterdam (UvA) in June 2025, supervised by Maarten de Rijke and Mohammad Aliannejadi. His research focuses on Information Retrieval and Natural Language Processing with Large Language Models , particularly in conversational agents, model/data-efficient neural ranking, and automatic evaluation techniques like query performance prediction. Education: PhD in Artificial Intelligence, University of Amsterdam (2025) MS in Computer Science and Technology, Shandong University (2021) BS in Electronic Commerce, Shandong Normal University (2018) His research interests span conversational search optimization, proactive agent development, knowledge-grounded dialogue systems, and query performance prediction using LLM-generated judgments. Recent work includes UniConv (unified retrieval/response generation), SOLID (intent-aware dialog generation), and QPP++ 2025 workshop organization. Key scientific contributions include 440+ Google Scholar citations (H-index: 13) and publications in premier venues like SIGIR , ACL , EMNLP , and TOIS . He has served as program committee member for SIGIR 2025/2024, ACL 2023, and other top conferences. As teaching assistant , he contributed to Information Retrieval and Natural Language Processing courses at UvA and Shandong University. His administrative roles include IRLab LinkedIn manager, webmaster for IRLab website, and seminar chair at UvA.
Dr. Corine Meppelink is an Assistant Professor of Persuasive Communication at the University of Amsterdam's Faculty of Social and Behavioural Sciences. Her research focuses on online information processing, health literacy, and digital inequalities, with a particular emphasis on how individuals interact with health and political information in digital environments. She holds a position at the Department of Communication Science and is affiliated with the Persuasive Communication Area. Her work bridges communication theory, computational methods, and applied health communication, addressing challenges in misinformation, algorithmic persuasion, and data donation ethics. Research Interests: Dr. Meppelink examines how literacy levels, digital skills, and cognitive processes influence online information seeking and processing. Key themes include the impact of health literacy on medical decision-making, the role of political attitudes in search behavior, and the design of effective digital health communication strategies. Her studies often employ mixed methodologies, including eye-tracking, computational social science, and experimental designs. Her recent work explores algorithmic personalization effects on political searches, the mental health implications of health communication, and the efficacy of warning tools against vaccination misinformation. She collaborates with interdisciplinary teams to address societal challenges in digital health literacy and algorithmic transparency. No scientific awards are explicitly listed, but her contributions are recognized through active peer-reviewed publications and academic engagements. Advising and Grants: While specific grant details are not provided, her publications suggest involvement in funded research projects on health communication and digital media. No formal advisee names are listed here, but her role as an Assistant Professor likely involves graduate supervision.
Prof. A.D. Pimentel holds a full professorship at the Informatics Institute of the University of Amsterdam, leading the Parallel Computing Systems (PCS) group within the Systems and Networking Lab. His research focuses on multi-core and multi-processor systems, emphasizing performance, energy efficiency, dependability, and productivity in system design and runtime management. He earned his PhD and MSc in Computer Science from the University of Amsterdam in 1998 and 1993, respectively. Current roles: Chair of PCS group, Board member of Advanced School for Computing and Imaging (ASCI), and ICT Research Platform Nederland (IPN) Teaching: Courses on Multi-core Processor Systems, Embedded Software, and Architecture Research interests span edge AI, sustainable computing, and system-level modeling. Recent work includes innovations in energy-efficient scheduling, thermal management in 3D-stacked systems, and adaptive CNN inference at the edge. Over 25 years of contributions to embedded systems design space exploration and hardware/software co-design have been recognized through awards like the IEEE CEDA Outstanding Service Award (2025). Awards: IEEE DATE Fellow (2025), NWO Knowledge & Innovation Covenant grant lead Active in conference organization, serving as General Chair for Embedded Systems Week (2026) and Design Automation and Test in Europe (DATE 2024). Engages in cross-disciplinary projects like improved secure semiconductor evaluation (ISSE) and energy labeling for digital services.
H. (Hans) Philippi is an Assistant Professor at Utrecht University, affiliated with the Department of Algorithmic Data Analysis under the Faculty of Science. His research focuses on database systems, bioinformatics, distributed systems, and algorithms. He can be reached at h.philippi@uu.nl and is located in Room BBL464 of the Buys Ballot building in Utrecht. Philippi's work spans multiple areas, including leveraging database technology for bioinformatics challenges like sequence alignment (e.g., BLAST emulation) and exploring distributed database architectures. His earlier research also involved object-oriented systems evaluation, reflecting a longstanding interest in software design and systems optimization. His publications highlight contributions to both theoretical and applied database systems, with a strong emphasis on algorithmic approaches to data analysis. No notable scientific awards have been mentioned in the records. Philippi has not listed any current or past advisees or grants in the provided information. His work is primarily conducted within the Algorithmic Data Analysis group, part of Utrecht University's broader AI & Data Science initiative.
Jerry Spanakis is an Assistant Professor at Maastricht University with dual affiliations: the Department of Advanced Computing Sciences (Faculty of Science and Engineering) and the Maastricht Law+Tech Lab (Faculty of Law). His roles include leading the EU Horizon project VOXReality, researching for NSMD/HumanAds/RegTech4AI initiatives, and serving as a technical expert for the European Commission’s e-enforcement academy. He coordinates MaastrichtNLP (NLP research group) and participates in the Open Science Community Maastricht. Education: PhD in Computational Intelligence (2007–2012) from the National Technical University of Athens, School of Electrical & Computer Engineering. Research Focus: Social Machine Learning: Developing responsible AI systems for societal challenges, including interpretable models for consumer protection and regulatory compliance. Computational Social Media: Analyzing social media data to detect online harms (e.g., misleading ads, content moderation failures) and model user behavior. Structuring Unstructured Data: Semantic organization of legal texts, social media, and multimodal data for applications in law, aviation, and public health. Publication Trends: Jerry's recent work (2023–2025) emphasizes NLP innovations for legal and regulatory domains, multilingual information retrieval, and ethical AI frameworks. Key themes include Large Language Model applications in law, influencer marketing compliance, and cross-lingual neural machine translation. Scientific Awards: None reported. Advising & Grants: Jerry supervises 7 PhD candidates and 100+ Master’s/Bachelor’s students in NLP, machine learning, and social computing. He leads the €2.8M EU project VOXReality (voice-driven XR interactions) and contributes to NWO/Philips grants on mental health analytics. Current grants focus on: AI-driven legal process automation (RegTech4AI) Dark pattern detection in e-commerce (NSMD) Influencer marketing transparency (HumanAds) Labs & Teams: Jerry founded MaastrichtNLP, a university-wide NLP research group, and co-leads the Law+Tech Lab, which develops computational tools for legal compliance. His teams collaborate with Deloitte, the European Commission, and healthcare institutions on applied AI projects.
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.
K. Psarakis is a researcher in the field of Data-Intensive Systems, focusing on cloud computing, stream processing, and distributed dataflows. Their work bridges theoretical and practical challenges in cloud-native applications and scalable systems. Institution: Affiliated with Data-Intensive Systems Research Focus: Cloud transaction management, autoscaling, geospatial data platforms, and fault tolerance Research Trends Recent publications like Styx and CheckMate highlight innovations in transactional stateful functions and checkpointing protocols. Psarakis also contributes to geospatial data federation (e.g., Topio ) and schema matching techniques ( Valentine ). Collaborations Collaborates with researchers such as G. C. Christodoulou, M. Fragkoulis, and A. Katsifodimos on projects involving open-source platforms and cloud-native systems.
Dr. Muhammad Hammad is a postdoctoral researcher at Eindhoven University of Technology (TU/e), affiliated with the Department of Mathematics and Computer Science within the School of Software Engineering and Technology. He holds a PhD in Computer Science from TU/e (2022), focusing on visualization and learning applications of code clones. His research interests span code clone analysis, software development tools, deep learning, visualization techniques, and digital twins for smart systems. Hammad has contributed to innovative projects like Clone-Writer and Clone-Seeker, which enhance code development efficiency through clone utilization. His work addresses challenges in API security, predictive analytics for water management, and improving software maintenance practices. Research contributions include systematic reviews on harmful API use repair techniques, digital twin-based smart water management systems, and advancements in code clone detection and visualization. His interdisciplinary approach bridges software engineering with environmental applications, contributing to UN Sustainable Development Goals related to clean water and responsible consumption. Hammad has published widely in reputable journals like IEEE Access and Computer Science Review, with a focus on practical, industry-relevant solutions. Notable press coverage includes a hospital waste monitoring system collaboration. While no grants or formal advisees are highlighted in the provided data, his work emphasizes collaborative research and impactful technological solutions. His lab/team contributions include pioneering tools for code analysis and environmental resource management systems.
Ronald de Wolf is a Senior Researcher at CWI (Dutch Centre for Mathematics and Computer Science) and a part-time Full Professor at the University of Amsterdam's Institute for Logic, Language and Computation (ILLC). He leads the Algorithms and Complexity group at CWI and is a core member of QuSoft and the Amsterdam TCS ecosystem. His academic journey includes studying computer science and philosophy at Erasmus University Rotterdam, followed by a PhD in quantum computation and communication complexity from the University of Amsterdam and CWI (2001), advised by Harry Buhrman and Paul Vitányi, and a postdoctoral fellowship at UC Berkeley. Research Focus: De Wolf's primary expertise lies in quantum computing and complexity theory, with significant contributions to quantum algorithms, communication complexity, and quantum machine learning. His work bridges theoretical computer science with practical quantum applications, exploring areas like quantum speedups for optimization problems, graph algorithms, and linear algebra. He maintains active interests in error-correcting codes, data structures, and theoretical machine learning foundations. Publication Trends: His recent research focuses on quantum advantage in optimization (graph sparsification, matrix problems), quantum communication complexity bounds, and quantum machine learning techniques. Work frequently establishes fundamental limits through lower bounds while developing novel quantum algorithms for practical computational challenges. Awards and Honors: Gödel Prize (2023) ACM STOC 10-year Test of Time Award (2022) STOC'12 Best Paper Award (2012) Cor Baayen Award (2003) ERC Consolidator Grant (2013) NWO TOP-grant (2013) Vidi Innovational Research Grant (2008) Veni Innovational Research Grant (2005) Academic Leadership: De Wolf has supervised 10+ PhD students (e.g., Yanlin Chen, András Gilyén, Srinivasan Arunachalam) and coordinates the NWO Gravitation program 'Quantum Software Consortium'. He secured significant funding including ERC and multiple NWO grants. As coordinating editor of Quantum and former editor for SIAM Journal on Computing, he shapes research dissemination. He regularly organizes major conferences (QIP, STOC, FOCS) and leads EU projects (QALGO, QAIP). Labs and Teams: He directs the Algorithms and Complexity group at CWI, collaborating extensively within QuSoft – the Dutch research center for quantum software. His team explores quantum algorithms, complexity theory, and their intersection with machine learning and optimization.
Daniele Bonetta is an Assistant Professor in the Department of Computer Science at Vrije Universiteit Amsterdam and holds an ancillary role as a Medewerker (Employee) at Eindhoven University of Technology since June 2020. His primary affiliation is with the Faculty of Science, where he contributes to the Network Institute as well. His research focuses on optimizing virtual machines, parallel programming models, and dynamic compilation techniques, with a particular emphasis on multicore systems and distributed computing environments. Bonetta has also been involved in teaching advanced courses such as Advanced Network Programming and contributes to the Accelerator-Centric Computing Ecosystems program. His research interests are centered around improving the performance of managed runtimes, including virtual machine optimization, dynamic taint analysis, and efficient data processing in polyglot environments. He has explored topics such as speculative optimizations for JSON data access, columnar array storage transformations, and scalable solutions for virtual memory oversubscription. His work frequently addresses challenges in distributed systems, cloud computing, and cross-language program analysis. Bonetta’s recent publications (2023-2025) highlight advancements in transparent scale-out mechanisms for virtual memory, automated supernode generation in interpreters, and dynamic query engines embedded in polyglot runtimes. His contributions to the field include both theoretical frameworks and practical implementations, often leveraging the GraalVM and Truffle frameworks for polyglot execution. While no formal awards are listed, his extensive publication record (47+ outputs) demonstrates significant scholarly impact. His teaching portfolio includes courses on network programming and systems architecture, reflecting his dual focus on both theoretical research and applied computer science education.
Dr. Michael Cochez is an Assistant Professor in the Department of Computer Science at Vrije Universiteit Amsterdam, with a secondary appointment in Artificial Intelligence. His research focuses on knowledge graph embeddings, graph neural networks, and neuro-symbolic systems. He has published over 70 works and contributed to datasets like KGloVe and Inductive WN18RR. Research Interests: Machine Learning, Knowledge Representation, Graph Theory Applications, Explainable AI, and Bioinformatics Integration. His work bridges theoretical advancements with practical applications in industry and healthcare. Recent Trends in Publications: Emphasis on scalable knowledge graph systems, causal reasoning in economic forecasting, and neuro-symbolic frameworks for complex queries. Active in organizing workshops on DL4KG and industry knowledge graph scaling. Awards: None listed. Grants: Not specified. Advising: No students listed but contributes to courses like Deep Learning and Machine Learning for Graphs. Labs/Teams: Involved in projects like Graph-Massivizer (sustainable data center modeling) and Graph-Scrutinizer (massive analytics tools). Ancillary Activity: Consultancy in Abcoude since 2022.
