Ana Ozaki is an Associate Professor at the Department of Informatics, University of Bergen, Norway. Her research focuses on the intersection of Artificial Intelligence (AI), knowledge representation and reasoning, and learning theory. She explores formal methods to analyze learnability, complexity, and reducibility in logical frameworks like description logics. Ozaki leads projects on ontology learning from neural networks, knowledge graph embeddings, and theoretical guarantees for machine learning systems. She has organized international conferences like AIB 2022 and DL 2024, and serves on editorial boards for Journal of Machine Learning Research and Journal of Web Semantics . Ozaki supervises PhD and master’s students in topics ranging from query-based learning to traffic prediction with graph neural networks. Her work emphasizes bridging theoretical foundations with practical AI applications, including ethics in autonomous systems and formal verification of neural networks.
Ernesto Jimenez-Ruiz is a Researcher at the University of Oslo affiliated with the Centre for Scalable Data Access (SIRIUS) and Logic and Intelligent Data group. He holds a PhD in Computer Science from University Jaume I of Castellon and specializes in semantic technologies and ontology engineering. His research spans bio-medical information processing, ontology reuse/alignment, and semantic web technologies for data analytics. Current projects include Artificial Intelligence for Data Analytics (AIDA) at The Alan Turing Institute, where he serves as Senior Research Associate. Recent publications (2024-2025) focus on neurosymbolic AI systems, knowledge graph construction from tabular data, and ontology alignment techniques. His work demonstrates strong emphasis on practical applications of semantic technologies. Dr. Jimenez-Ruiz has developed several tools including LogMap for ontology matching and BootOX for relational-to-ontology mapping. He teaches Semantic Technologies (INF3580/INF4580) and supervises PhD students in ecotoxicological effect prediction using knowledge graphs.
Mayank Raikwar serves as a Postdoctoral Fellow in the Department of Informatics at the University of Oslo, where he is an active member of the Networks and Distributed Systems research group. His work focuses on advancing blockchain technologies and cryptographic protocols within distributed computing environments, with significant contributions to DAG-based distributed ledger systems and privacy-preserving mechanisms. Dr. Raikwar's research centers on blockchain security and distributed consensus , with particular expertise in DAG architectures, cryptographic primitives, and privacy-enhancing technologies. His investigations span theoretical foundations and practical implementations, addressing critical challenges in scalability, attack resilience, and secure data management. Recent work extends to healthcare applications through secure medical image retrieval frameworks, demonstrating interdisciplinary applicability of cryptographic techniques. Analysis of his publication trajectory (2019-2025) reveals consistent leadership in blockchain security research. His work frequently appears in top-tier venues including IEEE conferences and ACM proceedings, with significant contributions to understanding consensus protocols, randomness generation, and privacy models. The evolution of his research demonstrates increasing sophistication in addressing real-world implementation challenges while maintaining rigorous security guarantees. Within the University of Oslo's research ecosystem, Dr. Raikwar maintains active collaborations with Professor Danilo Gligoroski and Professor Roman Vitenberg. His current projects focus on next-generation blockchain architectures and privacy frameworks, positioning him at the forefront of distributed systems innovation.
Anniken Susanne Thoresen Karlsen is an Associate Professor in the Department of ICT and Science at the Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU). She has extensive experience in research and teaching, with a focus on digital transformation, software-intensive systems, and emerging technologies. Karlsen holds leadership roles including deputy chair of the interdisciplinary NTNU research group SHAPE and active participation in multiple research centers. Her educational background includes: PhD in Information Science from the University of Bergen (UiB) Master's degree in Information Technology from Aalborg University (AAU) in Denmark Master of Science in Economics from the Norwegian School of Economics (NHH) Computer Engineering degree from Møre og Romsdal School of Engineering (MRIH) One-year practical-pedagogical education from NHH and UiB NTNU's UniPed program for employees Professor Karlsen's research spans a wide spectrum of digital transformation topics. Her work focuses on the integration of concepts from multiple domains to design and develop software-intensive systems. She explores modern software development methodologies, socio-technical systems thinking, and systems engineering approaches. A significant portion of her research addresses emerging technologies such as digital twins, virtual reality, and service robots as building blocks for innovative solutions. She also investigates the digital economy and sustainable development through digital transformation, with particular emphasis on business modeling and change management. Her recent publications demonstrate a strong focus on applying digital technologies to solve real-world problems across multiple sectors. There's a clear trend toward using digital twin technology in various contexts including urban planning, offshore wind farms, and healthcare. Her work in search and rescue operations shows consistent application of AI and knowledge management techniques. Karlsen's research bridges theoretical concepts with practical applications, particularly in sustainability-focused domains like renewable energy and healthcare. Professor Karlsen actively supervises students at all academic levels and has been involved in numerous research projects. She has held various academic leadership positions including vice-dean, head of department, and research group leader. Her collaborative work spans multiple sectors including healthcare, search and rescue, offshore, banking, food, and construction industries. She is affiliated with several research groups and centers: SHAPE - SamHandlingsArena for Prosjektledelse og Endring (2023-present), Deputy Chair The Norwegian Open AI Lab (NAIL) (2023-present) Green2050 - The Centre for Green Shift in the Built Environment (2022-present) Forskningsarena for bærekraftsanalyse (2020-present), Deputy Leader Forskningsgruppen for bærekraftig digital transformasjon, SDT (2017-present), Founder and former Leader
Fernando Raymundo Velazquez Quesada is an Associate Professor in the Department of Information Science and Media Studies at the University of Bergen. His research focuses on formal logic, artificial intelligence, and multi-agent systems, with particular emphasis on dynamic epistemic logic, communication protocols, and belief dynamics. He is affiliated with the Logic and AI research group, contributing to theoretical frameworks for reasoning about knowledge, intervention, and social interactions among agents. His work bridges logic and computer science, addressing topics such as false belief tasks in cognitive modeling, partial communication models for agent collaboration, and formal semantics of causal reasoning. He has published extensively in top venues like AAMAS, Studia Logica, and Dynamic Logic workshops. Key contributions include foundational studies on knowing-how logics, cautious distributed belief, and epistemic dynamics in multi-agent systems. Velazquez Quesada’s research integrates philosophical inquiry with computational methods, aiming to formalize complex reasoning processes in both theoretical and applied contexts. His recent projects explore uncertainty-aware reasoning mechanisms and the interplay between social influence and network structures in agent-based systems.
Haidar Hosamo is an Associate Professor at Oslo Metropolitan University's Faculty of Technology, Art and Design, affiliated with the Department of Built Environment. His work focuses on building sustainability, energy optimization, and digital twin technologies. Research areas: BIM, occupant modeling, predictive maintenance Key tools: Machine learning, AI-driven sensitivity analysis Recent publications address machine learning for floating solar arrays, dynamic LCA uncertainty, 5D BIM adoption, and knowledge graph integration in heritage conservation. His work combines data science with civil engineering to enhance building performance and occupant comfort.
Trond Linjordet is a Postdoctoral Fellow at the Hylleraas Center for Quantum Molecular Sciences, University of Oslo, affiliated with the Department of Chemistry. His academic background includes a Bachelor's in Nanotechnology, Master's in Quantum Information and Optics, and a PhD focused on machine learning applications in linguistic domains using knowledge graphs. He possesses commercial experience as a data scientist and scientific advisor, along with research experience in smart cities projects. Linjordet's research centers on machine learning theory and applications across multiple domains. Primary interests include: Machine learning for catalyst discovery in chemistry Knowledge graph construction and applications Natural language processing and question answering systems Synthetic data generation and optimization Quantum molecular sciences applications His publications demonstrate consistent focus on knowledge graph technologies, question answering systems, and urban informatics. He maintains active research collaborations as evidenced by co-authorship networks. No awards, student advisements, or grant information are documented in the provided materials.
Arnaud Gotlieb serves as a Research Professor and Chief Research Scientist at Simula Research Laboratory in Norway, where he heads the Department Validation Intelligence for Autonomous Software Systems. His academic career spans over two decades with significant contributions to software testing and artificial intelligence integration. Gotlieb's research focuses on Validation Intelligence of Autonomous Software Systems , Software Testing , Software Validation and Verification , and the application of Constraint Programming and Machine Learning to safety-critical systems. His innovative work bridges theoretical computer science with practical industrial applications, particularly in industrial robotics and autonomous systems. His publication record shows a consistent trajectory of high-impact research with approximately 15 recent publications (2024-2025) focused on metamorphic testing, constraint acquisition, explainable AI for autonomous driving, and verification of AI systems. The research demonstrates a clear evolution from foundational constraint programming work toward contemporary challenges in trustworthy AI and autonomous systems validation. Gotlieb has developed several influential tools including ITE (Stratified Disjunctive Reasoning), TITAN / FLOWER (Test suite optimization), EUCLIDE (constraint-based platform for critical C programs), and FPSE (Floating Point Symbolic Execution), which have advanced the field of automated software testing.
Basil Ell is a Researcher at the University of Oslo's Department of Data and Knowledge Management, affiliated with SIRIUS labs. He holds a PhD from Karlsruhe Institute of Technology (KIT) and has held postdoctoral positions at Bielefeld University's CITEC. His work bridges Semantic Web technologies and Natural Language Processing, focusing on knowledge base population, semantic parsing, and information extraction from text/tables. He splits his time between Oslo and Bielefeld, contributing to projects like semantically enhanced Virtual Research Environments and table understanding systems. Education: B.Sc./M.Sc. in Computer Science from Mannheim University of Applied Sciences, Pohang University of Science and Technology, and IIT Madras. PhD (2015) from KIT under Prof. Rudi Studer. Research Trends: Recent publications emphasize semantic data integration (e.g., materials informatics), link prediction in knowledge graphs, and clinical trial evidence synthesis. His work often combines RDF data structures with NLP techniques. Labs/Teams: Active in SIRIUS labs (Oslo) and previously at CITEC/Bielefeld's Semantic Computing group.
Samrat Gupta is an Associate Professor in the Information Systems area at the Indian Institute of Management Ahmedabad (IIMA), with additional roles as a Senior Researcher at the University of Agder (Norway) and visiting researcher at Bratislava University of Economics and Business (Slovakia). His academic foundation includes a doctoral fellowship from the Indian Institute of Management Lucknow and a bachelor's degree in Information Technology from Punjab Engineering College Chandigarh. His research centers on four interconnected themes: 1) Network theoretic modelling and analytics, 2) Information disorder driven by social media, 3) User engagement dynamics on digital platforms, and 4) User-centered digitalization frameworks. These interests bridge computer science, behavioral economics, and social media analytics, with applications in misinformation detection, governance systems, and consumer behavior. Gupta's publications demonstrate consistent focus on network analytics and digital society challenges, with recent work emphasizing AI transparency, social media polarization, and data quality. His research consistently integrates computational methods with social science frameworks, particularly through graph theory applications and user-centered design paradigms. Awards & Grants: SPARC Research Grant from Ministry of Education, Government of India (2019) Best Paper Award at ALLDATA International Conference (2019) EU Horizon-funded FAME Project: Federated decentralized data marketplace Ministry of Education-funded project on polarization dynamics and echo chambers He maintains active editorial roles including Associate Editor positions for ICIS, ECIS and PACIS conferences, and contributes to doctoral education through courses on network modeling and database systems. His funded projects include European Commission initiatives on data marketplaces and Indian government collaborations on socio-cultural polarization.
Dario Garigliotti is a Research Fellow at the Department of Informatics, University of Bergen. His work focuses on information retrieval, natural language processing, and AI applications for structured/unstructured data integration. He has contributed to systems like EquinorQA for enterprise question answering and Nordlys for entity-oriented search. His research emphasizes explainability in AI, particularly with large language models (LLMs), and addresses challenges in entity retrieval, task-based search, and climate-related document analysis. His recent work explores RAG systems for SDG evidence identification and administrative text generation. He has collaborated with institutions like the University of Stavanger and Polytechnic University of Valencia, and his funding includes a Research Council of Norway grant (2022-2026).
Kimji Nuneza Pellano is a Research Fellow at the Norwegian University of Science and Technology (NTNU), affiliated with the Center for Movement Sciences (Bevegelsessenteret) in Trondheim. Her work bridges artificial intelligence and clinical medicine, focusing on developing interpretable computational models for pediatric movement disorder diagnostics. Her core research areas include: Medical Artificial Intelligence Human Movement Analysis Cerebral Palsy Explainable AI Graph Convolutional Networks Early Diagnosis Pellano's publications reveal a concentrated research trajectory in spatio-temporal modeling of infant motor development. Her 2024-2025 work demonstrates systematic refinement of skeleton-based activity recognition frameworks, evolving from general human movement metrics to specialized cerebral palsy detection systems. This progression shows increasing clinical integration, with recent models incorporating medical domain knowledge to enhance diagnostic transparency in neurological assessments. She actively disseminates findings through high-impact journals like IEEE Access and Sensors, and presented at the 2025 European Academy of Childhood Disability conference, indicating strong engagement with both AI and clinical research communities. Her collaborative network includes key NTNU researchers in movement science and pediatrics, reflecting interdisciplinary work at the AI-medicine interface where technical innovation directly addresses clinical diagnostic challenges in neurodevelopmental disorders.
Hongchao Fan is a Professor in the Department of Civil and Environmental Engineering at the Faculty of Engineering, Norwegian University of Science and Technology (NTNU). His research focuses on 3D city modeling (digital twins) , laser scanning , photogrammetry , and spatial data mining using crowdsourcing data , with applications in urban infrastructure, cultural heritage preservation, and solar energy assessment. Key research areas: 3D building reconstruction from point clouds Knowledge graph integration for urban data OpenStreetMap (OSM) data inequality analysis Solar energy potential modeling in Nordic cities Machine learning for geospatial applications Current projects: Digitalization for Culture Heritage (2025-2028, Norwegian Directorate for Higher Education and Skills) MobilityLab Stor-Trondheim (MoST) (2024-2026, digital twins for transportation models) Hellios (2022-2024, solar energy in Nordic cities) Deep learning 3D building models (NTNU Digital, 2021-2025) Technical expertise: Airborne Laser Scanning (ALS) data processing, semantic facade reconstruction, photogrammetric algorithms, and GIS-based decision support systems. He serves as Associate Editor for Big Earth Data and Geo-Spatial Information Science .
Roxana Pop is a Research Fellow at the University of Oslo , affiliated with the Faculty of Mathematics and Natural Sciences and the Data and Knowledge Management (DKM) research group. Her research focuses on integrating neural methods with symbolic AI approaches to enhance temporal predictions on structured data, particularly through graph neural networks (GNNs). Her work aims to extract interpretable logical rules from trained neural networks, combining the strengths of data-driven and symbolic AI paradigms. Before her PhD, she conducted research in Natural Language Processing (NLP), specializing in Abstract Meaning Representation (AMR) parsing, a semantic parsing technique. In 2024, Pop co-authored a publication in the Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER) titled FactGenius: Combining Zero-Shot Prompting and Fuzzy Relation Mining to Improve Fact Verification with Knowledge Graphs , which explores fact verification techniques using knowledge graphs and zero-shot prompting. Her projects include dynamic link prediction and evaluating the trainability of GNNs for logic-expressible functions. She is associated with the Data and Knowledge Management group, with research interests spanning Artificial Intelligence, Machine Learning, and Knowledge Graphs. Her profile does not list any scientific awards or advisees.
Heinz Adolf Preisig is a Professor in the Department of Chemical Engineering at the Norwegian University of Science and Technology (NTNU). With an extensive international academic career, Professor Preisig has held positions at Texas A&M University (USA) as Associate Professor, University of New South Wales (Australia) as Senior Lecturer, and Eindhoven University of Technology (Netherlands) as Professor of Chemical Engineering, Physics, and Electrical Engineering before joining NTNU. His research focuses on Process Systems Engineering with particular emphasis on ontology-based computational engineering, multi-scale modeling, and materials modeling frameworks. Professor Preisig has developed systematic approaches for constructing, manipulating, and simplifying process models through hierarchical ontology-based software suites. His work bridges theoretical modeling frameworks with practical industrial applications, with significant contributions to process modeling methodologies that integrate mechanistic reductionism with holistic empirical components. His research spans from fundamental theoretical developments to applied industrial projects across multiple sectors including energy, materials, and chemical processing. Analysis of Professor Preisig's publication record (2017-2022) reveals a clear trajectory toward more sophisticated ontology-based modeling approaches with increasing integration of semantic web technologies with traditional process engineering. His work demonstrates consistent development of frameworks that enable better model interoperability, reusability, and systematic construction across different domains. A significant portion of his recent work focuses on multi-scale modeling applied to complex materials like polyurethane foams and biomass conversion processes, showing his ability to connect fundamental physical principles with practical engineering applications through advanced computational frameworks. Professor Preisig has been instrumental in major European research initiatives: VIPCOAT Horizon2020 Project: Creating an open innovation platform for corrosion protection coatings in the aeronautic industry MarketPlace EC Horizon 2020 project: Developing a Materials Modelling and Collaboration platform as a one-stop-shop for materials modeling MoDeNa Framework 7 Project: Building multi-scale software frameworks for polyurethane foam modeling EcoLodge: Bio fuel production project using coupled bio reactors He teaches core courses at NTNU including TKP4106 Process Modelling, TKP4135 Process Systems Engineering, and supervises chemical engineering specialization projects. His teaching philosophy centers around models as the foundational concept in engineering education, emphasizing multi-scale understanding and systematic model construction. Professor Preisig leads the Process Systems Engineering group at NTNU, which develops computer-aided modeling tools for systematic process model construction, documentation generation, and code implementation across various simulation environments.
