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Matteo Magnani is a Professor in the Division of Computing Science at the Department of Information Technology, Uppsala University. He leads the Uppsala University Information Laboratory and is a founding member of the Uppsala University Computational Social Science Lab. His research spans network science, artificial intelligence, data science, and computational social science, with a focus on social data mining and multilayer networks. PhD in Computer Science, University of Bologna, 2006 Graduated with honours in Information Sciences, University of Bologna, 2002 Studies in Computer Science at University of Marne la Vallée and Imperial College London Matteo Magnani's research interests include social network analysis, multilayer and probabilistic networks, community detection, visual analytics, and the application of AI to digital media and climate communication. His work bridges computer science and social sciences, particularly in analyzing online discourse and digital intermediaries. He has contributed significantly to the understanding of network structures, uncertainty in networks, and the ethical dimensions of algorithmic analysis. His recent publications highlight trends in fairness in community detection, visual saliency in network layouts, emotional reactions to climate visuals online, and deep learning applications in social media. Topics frequently involve YouTube, Twitter, and online public debates, using advanced network and machine learning methods. Rotary Prize for best student of the Science Faculty Best Paper Award Funniest Presentation Award Best Poster Award Pedagogical Prize from UTN Distinguished University Teacher (Sweden) Docent title (Sweden) Magnani has supervised numerous students and collaborated widely, particularly with Luca Rossi, Alexandra Segerberg, and Davide Vega. He has secured funding from major sources including VR, H2020, STINT, and MIUR. He leads active research labs focused on information systems and computational social science, fostering interdisciplinary collaboration and innovation in network-based research.

• Network Science
• Social Network Analysis
• Computational Social Science
• +10 more

Marina Papatriantafilou is an Associate Professor in the Department of Computer Science and Engineering at Chalmers University of Technology and University of Gothenburg. Her research focuses on distributed computing, fault-tolerance, parallel algorithms, and concurrency control. She has contributed to methods for fault-tolerant distributed systems, visualization tools for distributed algorithms, and scalable overlay networks. Her academic roles include teaching advanced courses on distributed systems, computer communication, and operating systems. She advises graduate students in areas like distributed algorithms and parallel computing. Key research interests include lock-free synchronization, memory reclamation, and self-stabilizing systems. She has authored over 100 publications in top-tier conferences and journals, with recent work on data streaming frameworks, energy-sharing optimization, and vehicular network processing. Professional involvement includes roles in program committees for conferences like OPODIS, SWAT, and SSS, plus membership in research evaluation boards for Swedish and European funding agencies. She pioneered educational tools like the Lydian environment for distributed algorithm visualization.

• Distributed Computing
• Distributed Algorithms
• Fault-Tolerance
• +8 more

Olaf Hartig is a Senior Associate Professor at Linköping University's Department of Computer and Information Science (IDA), affiliated with the Database and Information Techniques (ADIT) division. He is also an Amazon Scholar collaborating with the Neptune graph database team. His research focuses on data management, semantic web technologies, graph databases, and distributed data systems. Hartig holds a PhD from Humboldt-Universität zu Berlin and is a Docent at Linköping University. He has received numerous awards, including the SWSA Distinguished Dissertation Award and eight best paper awards, and was selected as a Wallenberg Academy Fellow in 2024. Education: PhD in Computer Science (Humboldt-Universität zu Berlin), Docent (Linköping University). Research interests span query processing for Linked Data, federated systems, RDF and GraphQL semantics, and knowledge graph construction. He leads research groups in Database and Web Information Systems and Semantic Web Technologies at IDA. Key achievements include pioneering traversal-based query execution, developing Triple Pattern Fragments, and contributions to standards like RDF* and SPARQL*. His work has been recognized through grants, patents (e.g., on graph acceleration techniques), and leadership roles in conferences like ISWC and ESWC. Teaching: Course leader for database technology courses (TDDD12, TDDD37) and advanced topics like big data analytics and bioinformatics databases. Active in curriculum design and interdisciplinary education. Labs/Teams: Database and Web Information Systems Group, Semantic Web Research Group, Sports Analytics Group (IDA) Grants: Wallenberg Academy Fellowship, Swedish Research Council funding

• Database Systems
• Semantic Web
• Data Management
• +5 more

Amin Jalali is an Associate Professor of Computer and Systems Sciences at Stockholm University, specializing in business process modeling, analysis, and management. He is affiliated with the Department of Computer and Systems Sciences within the Faculty of Social Sciences, where he serves as a board member and manages three graduate courses: Business Process Design and Intelligence, Business Process and Case Management, and Data Warehousing. Institution: Stockholm University Department: Department of Computer and Systems Sciences (DSV) Research Groups: Natural Language Processing Research Group and PRECIS (Process, Requirements, Enterprise, Capability, Information Systems modelling) His research focuses on business process analysis through model-based and data-driven techniques, with particular emphasis on process simulation, process mining, event knowledge graphs, and object-centric process mining. He has led numerous research projects across healthcare, education, and finance domains. His work includes significant contributions to the development of NLP methods involving large language models, with focus on privacy, explainability, and domain adaptation. Jalali's research output shows a strong trend toward practical applications of process mining techniques, particularly in healthcare contexts like drug-drug interaction analysis and elderly care. More recently, his work has expanded into blockchain applications for fraud detection, motor imagery signal classification, and advanced object-centric process mining approaches. His publications span both theoretical contributions to business process management frameworks and practical implementations in real-world settings. He has extensive industry experience in designing and implementing Business Intelligence and Big Data Analytics solutions, which informs his academic work and teaching approach. His research has been published consistently from 2012 through 2024, demonstrating sustained scholarly productivity in his field. Dr. Jalali has contributed significantly to the development of tools and libraries for process mining, including the dfgcompare library for process variant analysis and implementations for object-centric process mining. His work bridges academic research with practical applications, particularly evident in healthcare projects like the DDIs-Graph system for identifying drug-drug interactions.

• Business Process Management
• Process Mining
• Object-Centric Process Mining
• +6 more

Christoph Egger is an Assistant Professor at Chalmers University of Technology in the Department of Computer Science and Engineering, where he works with the Security & Privacy Lab and the Crypto Team. Prior to this position, he was a Marie-Curie Fellow at Institut de Recherche en Informatique Fondamentale (IRIF) from fall 2022 to 2024, researching connections between cryptography and complexity theory. His educational background includes: PhD: "On Abstraction and Modularization in Protocol Analysis" Master's: "An implementation of global caching for the alternation-free coalgebraic μ-calculus" Bachelor's: "Analysing and attacking the I2P Network Database" Dr. Egger's research focuses on cryptography and its connections to computational complexity, statistical privacy, and formal methods. His work spans multiple areas including cryptographic foundations (random oracles, key agreement protocols), privacy-enhancing technologies (ring signatures, information flow techniques), and practical applications in genomic data security. He develops both theoretical frameworks and practical tools like CryptoZoo for cryptographic proof visualization, bridging the gap between theoretical cryptography and real-world security challenges. His recent publications demonstrate a strong focus on cryptographic foundations and privacy technologies, with significant contributions to ring signatures, key agreement protocols, and genomic data security. His work bridges theoretical cryptography with practical applications in blockchain, anonymous communications, and healthcare data management, showing consistent productivity across multiple high-impact venues in security and privacy. Dr. Egger has served on program committees for prestigious conferences including IEEE Computer Security Foundations Symposium (CSF), Proceedings on Privacy Enhancing Technologies (PETS), and Conference on Applied Cryptography and Network Security (ACNS). He currently advises PhD students Lucia Lavagnino at Chalmers and Kirthivaasan Puniamurthy at Aalto University. Previously, he advised Master's students including Julian Brost and Kirthivaasan Puniamurthy. His Marie-Curie Fellowship was cofunded by EU H2020 Marie Sklodowska-Curie Action and FSMP Comunity Service. Dr. Egger is an active member of the Security & Privacy Lab and Crypto Team at Chalmers. He is also a founding member of the FAUST CTF team and has been a Debian Developer for over a decade, contributing to various Free Software projects including the Linux kernel and Git version control system, demonstrating his commitment to both academic research and practical software security.

• Cryptography
• Computational Complexity
• Statistical Privacy
• +7 more

Wagner Ourique De Morais is a Lecturer at the Academy of Information Technology , Halmstad University, with a focus on Smart Home Systems and Healthcare Technology for elderly populations. Doctoral Thesis : Architecting Smart Home Environments for Healthcare: A Database-Centric Approach (2015) Key Research Areas : Ambient Assisted Living (AAL), Graph Neural Networks for Clinical Prediction, IoT-Edge-Cloud Offloading, and Wearable Health Monitors His work spans 15+ years , including publications on Zero-Day Attack Detection in Vehicular Networks (2025), Graph Neural Networks for Clinical Risk Prediction (2024), and Smart Medication Organizers (2017). Research trends emphasize sensor integration , context-aware computing , and security in smart environments . He contributes to projects like the "Safe at Night" initiative and has developed middleware for heterogeneous sensor networks (2008). Current teaching and research at Halmstad University prioritize technology-enabled elderly care and cyber-physical system design .

• Ambient Assisted Living (AAL)
• Smart Home Systems
• Healthcare Technology
• +3 more

Mohamed Faouzi Atig is a Professor in Computer Systems at the Department of Information Technology, Uppsala University. His career spans roles as Senior Lecturer (2018-2021), Associate Senior Lecturer (2014-2018), and Researcher (2012-2018) at the same institution. He obtained his Doctoral Degree in Computer Science from the University of Paris Diderot-Paris 7 (2010) and a Master in Engineering from Tunisia Polytechnic School (2005). Current Role: Professor in Computer Systems Institution: Uppsala University Research Focus: Model checking, verification of infinite-state systems, weak memory models, automata theory, string constraints, concurrent program analysis His research explores formal methods for concurrent programs, weak memory models (TSO/PSO/POWER), automata theory for verification, and SMT solvers for string constraints. Recent work integrates graph neural networks with word equation solving and advances stateless model checking techniques. Key article trends include Weak Memory Model Verification (TSO, PSO, POWER) Stateless Model Checking Algorithms String Constraint Solvers (TRAU, Norn) Timed Automata and Multi-Pushdown Systems Mohamed Faouzi Atig has led collaborations on fence insertion procedures, timed pushdown automata, and database-driven system verification. His contributions are recognized through publications in top-tier conferences and journals.

• Model Checking
• Program Verification
• Concurrency
• +7 more

Sonia-Florina Horchidan is a doctoral researcher at the KTH Royal Institute of Technology in Stockholm, Sweden. She is affiliated with the Division of Software and Computer Systems within the School of Electrical Engineering and Computer Science (EECS), working under the supervision of Associate Professor Paris Carbone in the Data Systems Lab . Her academic activities include teaching assistant roles for courses in distributed systems and data storage paradigms. Research Focus: Graph Databases Machine Learning on Graphs Stream Processing Federated Learning with Privacy Guarantees Approximate Query Processing via ML Serverless Streaming Graph Analytics Scientific Contributions: Her recent publications at venues like EDBT, VLDB, and AI4DB explore intersections between graph data management and machine learning. Key themes include ML inference optimization in streaming systems, privacy-preserving health data forecasting, and innovative graph query execution models. Honors & Service: Co-authored Best Paper at DEEM 2022 Best Paper Award at VLDB 2023 PhD Workshop Program Committee Member, aiDM 2024 Reproducibility Committee Member, ACM SIGMOD/PODS

• Graph Databases
• Machine Learning on Graphs
• Stream Processing
• +3 more

Haibo Li is a Full Professor of Media Technology at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and Digital Futures Faculty. His roles include leading the Media Lab in MID, directing Master’s programs in Media Technology, and teaching courses like Generative AI for Media Technology and Video Technology. He holds a Technical Doctorate from Linköping University (1993) and received the Nordic Best PhD Thesis Award (1994) and Docent title in Image Coding (1997). Research focuses on digital media technologies, including image/video compression, human-computer interaction, and AI-driven media systems. He pioneered the Digital Media Lab at Umeå University and co-founded UCIT. His work spans blind image deblurring, thermal comfort sensing, and multimodal emotion recognition. Over 250 publications and six patents highlight contributions to multimedia, AI, and signal processing. Key projects include developing contactless thermal measurement systems for smart buildings, real-time gesture recognition for wearable devices, and advanced neural networks for image restoration. Awards include the Nordic Best Thesis and leadership in EU projects. He chairs conferences and actively participates in MPEG standardization efforts.

• Media Technology
• Digital Signal Processing
• Image and Video Compression
• +5 more

Georgios J. Fakas is an Associate Professor (Docent) in the Department of Information Technology at Uppsala University, Sweden, where he leads the Uppsala DataBase Laboratory (UDBL). He has held academic positions at prestigious institutions including the Hong Kong University of Science and Technology, EPFL, UMIST, University of Cyprus, and Manchester Metropolitan University, where he served as a Senior Lecturer. Education: Ph.D. in Computation, UMIST, Manchester, UK (1998) M.Phil. in Computation, UMIST, Manchester, UK (1996) B.Sc. in Computation, UMIST, Manchester, UK (1995) His research spans big data, keyword search on structured and graph data, spatial databases, web and geo-social search, workflow management, and data summarization . He also explores applications in educational technologies, distance learning, and health informatics. His work emphasizes efficient, user-centric data access and intelligent querying paradigms. The 15 most recent publications highlight a sustained focus on keyword search, object summarization, spatial and semantic data, and ranking , published in top venues like SIGMOD, VLDBJ, IEEE TKDE, and PVLDB. His work integrates algorithmic innovation with practical database applications. Scientific Awards: International Sabbaticals grant from Teknat (2023), hosted by Boston University Fellowship 'Hosting of Experienced Researchers from Abroad' from RPF, Cyprus (2009) ERCIM Fellowship (2001), hosted by EPFL, Switzerland He is actively involved in PhD supervision and grant leadership. He currently serves as Principal Investigator for an eSSENCE-funded project on Data-intensive analytics for discovering critical minerals using Machine Learning , in collaboration with industry partners. He has supervised multiple PhD students to completion, including Georgios Kalamatianos, Khalid Mahmood, Zhi Cai, and Ben Cawley. He is a frequent reviewer for leading journals and serves on program committees of major conferences such as ACM SIGMOD and PVLDB. He leads the Uppsala DataBase Laboratory (UDBL) , a research group focused on advanced data management systems, where he mentors students and drives innovation in database technologies.

• Big Data
• Keyword Search
• Spatial Databases
• +9 more

Diego Calvanese is a Visiting Professor at Umeå University's Department of Computing Science and holds a full professorship at the Free University of Bozen-Bolzano, Italy. His research focuses on AI for data management, including virtual knowledge graphs (VKG), ontology-based data access (OBDA), and formal methods like description logics. He is part-time at Umeå, balancing roles with his primary position in Italy. Calvanese has received prestigious awards including the AAAI Classic Paper Award (2021), EurAI Fellow (2015), and ACM Fellow (2019). He supervises three doctoral students at Umeå and has authored over 350 publications, with an h-index of 71. His work emphasizes data integration, geospatial systems, and ethical AI applications. Key Roles: Associate Programme Chair (IJCAI 2025), Programme Chair (IJCAI-ECAI 2026), Head of AI for Data Management Research Group Research Interests: Knowledge representation, graph data management, explainable AI, and telemonitoring systems like reCOVeryaID. His research group develops tools like Ontop, a VKG system enabling seamless data access across heterogeneous sources. Current projects include geospatial data integration and temporal OBDA frameworks. Calvanese has served on over 150 program committees and editorial boards, including Artificial Intelligence and JAIR. His work bridges technical advancements with societal impacts, addressing AI's role in healthcare, climate, and democracy.

• Artificial Intelligence for Data Management
• Knowledge Representation and Reasoning
• Virtual Knowledge Graphs
• +5 more

Dr. Danupon Na Nongkai is an Associate Professor in the Division of Theoretical Computer Science at KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science. His research focuses on theoretical computer science, with specialization in graph algorithms for dynamic and distributed environments. Supported by prestigious grants including the Swedish Research Council's VR Young Researcher Grant (2015) and the European Research Council's Starting Grant (2016), his work spans approximation algorithms, communication complexity, game theory, verification, theoretical databases, quantum algorithms, and social network analysis. PhD in Algorithms, Combinatorics, and Optimization (ACO) from Georgia Tech (2011) Co-winner of Principles of Distributed Computing Doctoral Dissertation Award (2013) Research Interests: Dr. Na Nongkai investigates fundamental problems in graph theory and algorithm design, particularly in dynamic, distributed, and quantum computing paradigms. His research connects theoretical computer science with practical applications in network processing, optimization, and complexity analysis. Article Trends: Recent publications demonstrate expertise in near-linear time algorithms for shortest paths, cut problems, and connectivity in diverse computational models. Key themes include cross-paradigm optimization (dynamic/static, distributed/parallel, quantum), expander decomposition applications, and submodular function analysis. Scientific Recognition: FOCS 2022 Best Paper Award ERC Starting Grant recipient VR Young Researcher Grant PODC Doctoral Dissertation Award Advising & Grants: As a principal investigator, Dr. Na Nongkai has mentored numerous researchers through collaborative publications with 85 co-authors. His group's funding includes competitive national and European grants for cutting-edge algorithm research.

• Theoretical Computer Science
• Graph Algorithms
• Dynamic Computing
• +6 more

Ahmad Ghazawneh is a Senior Lecturer at the School of Information Technology , Halmstad University. His research explores the intersection of digital innovation platforms, blockchain technology, and fintech, emphasizing transformative impacts on financial systems and digital economies. Email: ahmad.ghazawneh@hh.se Research Focus: Dr. Ghazawneh investigates blockchain-based financial ecosystems, knowledge graph integration in healthcare, and platform dynamics across multiple domains including mobility systems and social media affordances. Scientific Trends: Recent publications demonstrate expertise in federated health data systems, token-based blockchain ecosystems, conversational AI for healthcare, and sustainable mobility platforms. His work bridges theoretical platform economics with practical implementations.

• Digital Innovation Platforms
• Blockchain Technology
• Fintech
• +3 more

Klara Stokes is an Associate Professor in Mathematics at the Department of Mathematics and Mathematical Statistics, Umeå University. She holds a qualification as a Recognised University Teacher. Her research focuses on incidence geometry, combinatorial structures, and their geometric realizations, with an emphasis on symmetries and motions. She also explores algebraic and geometric graph theory, finite field geometry, and applications in coding theory, cryptography, data privacy, and kinematics. Her affiliations include membership in the Discrete Mathematics research group. Key research themes involve incidence geometries with trialities, projective rigidity of configurations, and privacy-preserving graph database models. She collaborates on topics such as rigidity circuits, Assur graphs, and geometric decoding of subspace codes. Publications highlight her work on flag transitive geometries, pebble game algorithms for sparse posets, and structural rigidity analysis. Her research bridges pure mathematics and applied areas like data privacy through probabilistic models and clustering techniques. Stokes has contributed to foundational work on pentagonal geometries, minimal genus graph embeddings, and combinatorial configurations. She remains active in advancing geometric and combinatorial methods with interdisciplinary applications.

• Incidence Geometry
• Combinatorics
• Graph Theory
• +7 more

Piotr Rudol is a Researcher at Linköping University's Department of Computer and Information Science (IDA), part of the Artificial Intelligence and Integrated Computer Systems (AIICS) division. His work focuses on advancing artificial intelligence, robotics, and their applications in collaborative systems, autonomous navigation, and UAV technology. Research interests include: Robotics mission planning and coordination Unmanned aerial vehicle (UAV) systems for search and rescue RDF graph synchronization for collaborative robotics Computer vision for object detection and geolocation Multi-agent systems integration His recent articles emphasize autonomous systems, sensor fusion, and distributed robotics frameworks. Notable contributions include frameworks for UAV-based human detection and collaborative mission planning systems. Piotr collaborates with senior researchers like Patrick Doherty and Mariusz Wzorek, contributing to projects funded through institutional research programs. He is actively involved in the IDA department's research initiatives, bridging theoretical AI advancements with practical robotic implementations.

• Artificial Intelligence
• Robotics
• Computer Vision
• +5 more