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Basil EllView profile

Researcher

• Semantic Web
• Natural Language Processing
• Knowledge Base Population
• +5 more

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.

• Cybersecurity
• Network Security
• Cloud Security
• +8 more

Chhagan Lal is a Postdoctoral Fellow at the Department of Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU), Norway. He is part of the Critical Infrastructure Security and Resilience (CISaR) group and currently contributes to the EU-funded CYBERUNITY project. Certified in CISSP, CCSP, and Microsoft Azure Security (SC-100/200/300, AZ-500), he specializes in cybersecurity frameworks, compliance, risk assessment, threat intelligence, and machine learning applications. His research focuses on advancing security and resilience in critical infrastructure, particularly in network and cloud security technologies. Key areas include Internet of Things (IoT) and Industry 4.0, Cyber-Physical Systems (CPS), Software Defined Networking (SDN), Information Centric Networking (ICN), and blockchain technologies for smart contracts. He has also worked on secure communication protocols for underwater acoustic networks and mobile ad-hoc networks (MANETs). Chhagan has been actively involved in multiple EU projects, including H2020-SU-SEC-2018 (LOCARD), H2020-ICT30-2015 (TagItSmart!), and EU-India collaborations (REACH). His professional activities include serving as a journal reviewer for IEEE Transactions and Elsevier publications, Program Chair for ACM WiSec 2017, and Publicity Chair for ACNS 2020 and related conferences. He has also contributed as a Program Committee Member to several cybersecurity-focused events. His work emphasizes secure solutions for MANETs, VANETs, and industrial IoT systems, including projects like CENSOR (Cloud Enabled Secure IoT Architecture over SDN) and blockchain-based frameworks such as LEChain. Chhagan has authored book chapters on topics like secure machine-to-machine communication and vehicular cloud computing. No scientific awards are explicitly mentioned in the provided texts. His research spans theoretical and practical cybersecurity challenges, with a focus on developing robust countermeasures against DDoS attacks, enhancing QoS in multimedia networks, and privacy-preserving mechanisms in vehicular and industrial systems.

Dario GarigliottiView profile

Research Fellow

• Information Retrieval
• Natural Language Processing
• Machine Learning
• +3 more

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).

• 3D Object Retrieval
• Biometrics
• Shape Analysis
• +5 more

Theoharis Theoharis is a Professor at the Department of Computer Science (IDI), Norwegian University of Science and Technology (NTNU Trondheim), where he leads research in shape analysis and visual computing. He is a core member of the Visual Computing Lab , focusing on 3D object retrieval, biometrics, and cultural heritage applications. Research Interests : 3D Object Retrieval and Registration Biometric Identification (facial data, somatotype analysis) Shape Segmentation and Cultural Heritage Preservation Medical Imaging and Robotics Notable Projects : PRESIOUS : EU-funded initiative for cultural heritage using visual computing D4FLY : On-the-move biometric verification CHANGE ITN : Monitoring cultural heritage change with 15 PhD theses Publications highlight trends in: 3D shape descriptors (QUICCI, radial intersection count) Deep learning for 3D reconstruction and registration Applications in cultural heritage, medical imaging, and geophysics Education : BSc in Computer Science MSc in Computation DPhil in Computer Graphics and Parallel Processing Advising includes doctoral students like Evdokia Saiti (2023) and Peder Bergebakken Sundt (2024), with contributions to SHREC'16 and 3DOR2022 symposiums. Labs & Collaborations : Visual Computing Lab (NTNU) NTNU's Computing unit European Union FP7-ICT (D3.1 Deliverable)

Arild Torolv Søetorp WaalerView profile

Professor

• Semantic Web
• Ontology-Based Data Access
• Knowledge Graphs
• +3 more

Arild Torolv Søetorp Waaler is a Professor in the Reliable Systems group at the Department of Informatics , University of Oslo. His research focuses on semantic technologies, ontology-based data access (OBDA), and systems engineering for industrial applications, particularly in the oil & gas and manufacturing sectors. Research Interests: Semantic Web, Knowledge Graphs, Ontology Engineering, Machine Learning for Condition Monitoring, Data Variety Challenges, and Industrial Systems. Waaler has co-authored 15 recent publications (2017–2024) spanning semantic frameworks for digital twins, identifier management, and analytics-aware ontologies. These works emphasize data integration , query optimization , and sustainable data systems in collaboration with industry partners like Statoil and Siemens. Contact: arild@ifi.uio.no | Room 9465, Gaustadalléen 23B, Oslo.

Martin GieseView profile

Professor

• Applied Logics
• Formal Methods
• Semantic Web/Semantic Technologies
• +9 more

Martin Giese is a Professor at the Department of Informatics (IFI), University of Oslo , specializing in applied logics , formal methods , and semantic technologies . He is affiliated with the Data and Knowledge Systems (DKM) research group and has held postdoctoral positions at RICAM, RISC, and Chalmers University of Technology. PhD and Diploma in Computer Science from Universität Karlsruhe (2002/1998) Secretary of the Association of Automated Reasoning and CADE Inc. (2010–2018) Steering Committee member of Tableaux (2009–2017), Vice President (2013–2017) His research focuses on semantic web technologies , ontology-based data access (OBDA) , and industrial applications of formal methods. Key contributions include the development of OptiqueVQS (visual query system for big data) and GeoFault (ontology for geological modeling). Recent publications address SPARQL optimization with SHACL , digital twins , and geological process simulation . Scientific collaborations span institutions like Statoil , Siemens , and ACM/IEEE conferences. His work bridges theoretical logic with practical industrial data challenges , emphasizing user-centric semantic systems and scalable solutions for cross-linked datasets.

Ana OzakiView profile

Associate Professor

• Artificial Intelligence
• Knowledge Representation
• Machine Learning Theory
• +3 more

Ana Ozaki is an Associate Professor at the University of Oslo (full-time) and holds a part-time position at the University of Bergen. Her research focuses on Artificial Intelligence, particularly knowledge representation, machine learning theory, and algorithms for learning logical theories in description logic. Her primary research interests include the formalization of learning phenomena to investigate questions of learnability, complexity, and reducibility. Ozaki specializes in algorithms for learning logical theories using description logic and related formalisms. Her work bridges theoretical foundations with practical applications in knowledge representation. Ozaki's publications predominantly explore themes in computational logic, knowledge representation, and machine learning theory. Recent works focus on ethical AI applications, hybrid logic systems, and knowledge extraction techniques from language models, demonstrating consistent innovation at the intersection of formal methods and practical AI systems. She has led significant research projects including 'Learning Description Logic Ontologies' (funded by RCN) and 'Apprendimento PAC di Ontologie in Logica Descrittiva' (PACO), and has collaborated internationally on graph-based computation models. Ozaki serves the AI community through editorial roles at the Journal of Machine Learning Research and Journal of Web Semantics, and has chaired program committees for major conferences including the International Joint Conference on Rules and Reasoning and the International Description Logic Workshop.

• Industrial networks
• Inter-organisational theory
• Purchasing
• +3 more

Tim Kristian Andreas Torvatn is an Associate Professor at the Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology (NTNU). He holds the title of Excellent Teaching Practitioner, reflecting his contributions to education. His research focuses on industrial networks, inter-organizational theory, purchasing strategies, and supply networks. He has published extensively on topics ranging from public procurement reforms to project management in organizational change processes. His teaching responsibilities include courses on Strategic Programme Management, Public Procurement, and Project Management Specialization. He has authored textbooks such as Teknologiledelse - for ingeniørstudier , emphasizing practical applications for engineering students. Key research themes include analyzing supply network dynamics, evaluating project risk management tools, and exploring procurement reforms in the EU context. His work often employs longitudinal case studies to trace strategic footprints in purchasing and innovation ecosystems. He has contributed to policy discussions through articles like Reform med store utfordringer (2014), addressing challenges in public procurement reforms. Notable outreach activities include a 2014 interview on accounting simulation tools for engineers and a 1998 workshop presentation on firm boundary dynamics in interorganizational research.

• Cloud computing
• Cybersecurity
• Software engineering
• +9 more

Pål Halvorsen is a Professor at the Department of Computer Science within the Faculty of Technology, Art and Design at Oslo Metropolitan University. He works at the intersection of computer science and applied domains, with a particular focus on multimedia systems, distributed computing, and healthcare applications. Specializes in distributed multimedia systems Active in AI-driven forensic psychology applications Conducts research on medical imaging and diagnostics Develops sports analytics datasets and tools Works on communication and distributed systems His research spans several key areas of computer science, particularly focusing on multimedia systems and their applications in healthcare, sports analytics, and forensic psychology. He leads projects involving AI-driven child avatars for investigative interview training, develops datasets for medical and sports applications, and explores innovative approaches to image analysis and time-series data processing. Recent publications demonstrate strong activity in applying computer vision and deep learning to medical diagnostics, particularly in gastrointestinal tract analysis and ophthalmology. His work on sports analytics includes creating comprehensive datasets for ice hockey and soccer, while his forensic psychology research focuses on AI-enhanced interview training for child abuse investigations. Halvorsen collaborates extensively across disciplines, working with researchers in psychology, medicine, and sports science. His projects often involve developing novel tools for data analysis, including approaches to multimodal data handling, visual deep learning verification, and AI-enhanced prompt generation techniques.