جستجوی استاد راهنمای دانشگاهی

• Semantics
• Language Technology
• Recommender Systems
• +۵ مورد دیگر

Jon Atle Gulla is a Professor at NTNU and Director of the Norwegian Research Centre for AI Innovation (NorwAI). He holds academic leadership roles, including former Head of the Department of Computer Science and Informatics at NTNU. His expertise spans Semantics, Language Technology, Recommender Systems, and AI-driven innovation. He has nearly 150 international publications and advised over 100 students across MSc, PhD, and postdoctoral levels. Education: MSc in Computer Science (1988), PhD in Computer Science (1993) from Norwegian Institute of Technology (NTH) MSc in Linguistics (1995), University of Trondheim MSc in Management (Sloan fellowship, 2003), London Business School Research interests focus on Semantics and Language Technology applied to Recommender Systems, Information Retrieval, and Text Analysis. He explores AI-based innovations in digitalization and entrepreneurship, advising industry on AI adoption and commercialization. Notable projects include Big Data collaborations with DNB, RecTech for news recommendation, and Trondheim Analytica analyzing political texts/social media. Publications emphasize AI applications in news recommendation, political text analysis, and Scandinavian language models. His work addresses ethical AI, copyright implications, and cross-lingual NLP challenges. Awards: Member of the Royal Norwegian Society of Arts and Sciences. Advising/grants: Supervised 30 PhD students and 70 MSc students. Involved in startups like Fast Search & Transfer (acquired by Microsoft) and Mito.ai/Strise.ai. Active in reviewing for journals like Data & Knowledge Engineering and conferences like ACL. Labs/teams: Leads NorwAI, co-founder of INRA and NOBIDS workshops. Collaborates with industry and academia on AI-driven solutions.

Erik Velldalمشاهده پروفایل

استاد

• Natural Language Processing
• Machine Learning
• Sentiment Analysis
• +۵ مورد دیگر

Erik Velldal is a Professor in the Language Technology Group (LTG) at the Section for Machine Learning , Department of Informatics, University of Oslo . With over 25 years of experience in machine learning and natural language processing (NLP), he leads the SANT project focused on sentiment analysis and contributes to major research initiatives including MediaFutures , NorwAI , and Integreat (Norwegian Center for AI Research). His work bridges linguistic theory and computational methods, emphasizing semantic modeling and uncertainty detection. Research interests include sentiment analysis , language modeling , event extraction , and machine learning applications to NLP. Recent publications address cross-domain sentiment classification , generative event analysis , and multilingual model adaptation . He co-developed the Norwegian Review Corpus (NoReC) and Norwegian Anaphora Resolution Corpus (NARC) , foundational resources for Norwegian NLP. His projects often involve collaboration with international institutions, reflected in publications at venues like ACL, COLING, and EMNLP. Current efforts focus on entity-level sentiment analysis , diagnostic datasets for Norwegian , and evaluating compositional generalization in language models. No public record of scientific awards or part-time appointments exists.

Benjamin Ricaudمشاهده پروفایل

دانشیار

• Machine Learning
• Graph Signal Processing
• Explainable AI
• +۳ مورد دیگر

Benjamin Ricaud is an Associate Professor and Group Leader in Machine Learning at UiT The Arctic University of Norway's Department of Physics and Technology. His core affiliations include membership in the Machine Learning Group, Visual Intelligence center, and co-directorship of the Digital Technology Innovation Lab focused on Arctic-region tech startups. He also co-chairs the annual Northern Light Deep Learning conference. Ricaud's research spans: Fundamental ML : Graph signal processing, explainable AI, and generative models Applications : Microfossil classification, medical diagnostics (retinal aging), drug analysis, and climate data interpretation Emerging domains : Self-supervised learning and biological data analysis using Raman spectroscopy His recent publications (2020-2025) cluster in three domains: Graph ML methodologies (35%) Biomedical/biological applications (40%) Geoscience/climate informatics (25%) with consistent focus on interpretability and real-world data challenges. Teaching includes Image Processing (FYS-2010), Pattern Recognition (FYS-3012), and Machine Learning (FYS-2021). He leads outreach initiatives developing AI exhibits for Tromsø Science Centre.

• Wireless Communications
• Machine Learning
• Digital Twins
• +۵ مورد دیگر

Pierluigi Salvo Rossi is a Professor at the Department of Electronic Systems , Norwegian University of Science and Technology ( NTNU ), with additional roles as Deputy Head of Department (since 2021) and Deputy Manager at the Center for Green Shift in the Built Environment (since 2022). He also serves as a part-time Research Scientist at SINTEF Energy's Gas Technology department. Education: Ph.D. in Computer Engineering, University of Naples “Federico II”, Italy (2005) Dr.Eng. (cum laude) in Telecommunications Engineering, University of Naples “Federico II”, Italy (2002) Research Interests span Wireless Communications , Digital Twins , Machine Learning , and Statistical Signal Processing , focusing on applications like Industrial IoT , Fault Detection , and Energy Systems . His recent Publications highlight trends in Federated Learning , Graph Signal Processing , and Multi-Sensor Anomaly Detection across domains from Natural Gas Pipelines to Subsea Leakages . Scientific Awards include: Exemplary Senior Editor, IEEE Communications Letters (2018) Department Ambassador, NTNU (2016) IEEE Senior Member (since 2011) Professional Roles encompass editorial leadership (e.g., IEEE Sensors Journal) and conference organization (e.g., General Chair for IEEE Sensor Array and Multichannel Signal Processing Workshop, 2022). He leads major funded research projects like PREFERENCE (RCN, 2023-2027) and AUTOSHIP (RCN, 2020-2028).

Ferreira Joao Carlosمشاهده پروفایل

استاد

• Artificial Intelligence
• AI in Health and Medicine
• AI in Energy and Transportation
• +۶ مورد دیگر

Joao Carlos Amaro Ferreira is a Professor at the Faculty of Logistics, Molde University College (HiMolde), Norway. He holds PhDs in Computer Engineering and Industrial Engineering from the Technical University of Lisbon and the University of Minho, respectively. His research focuses on Artificial Intelligence (AI) applications in healthcare, energy, transportation, IoT, blockchain, and smart cities. He has led over 40 projects, including 6 as Principal Investigator, and contributed to international conferences like OAIR and INTSYS. He served as IEEE CIS President (2016-2018) and is an IEEE Senior Member since 2015. His academic contributions span AI-driven solutions for public sector informatics, healthcare data quality, and cybersecurity. He actively participates in European projects such as e-Hospital4Future and explores blockchain applications in supply chains and medical records. Ferreira leads the ABC-AI research group, emphasizing ethical and applied AI. His work bridges academia and industry through projects like gamification systems for eco-driving and AI in fisheries traceability. Recent publications highlight AI's role in cardiovascular disease detection, emergency department optimization, and blockchain-enhanced healthcare interoperability. He collaborates internationally, co-editing journals like Applied Sciences , and has authored patents in edge computing for maritime monitoring.

Lilja Øvrelidمشاهده پروفایل

استاد

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۳ مورد دیگر

Lilja Øvrelid is a Professor at the Department of Informatics, University of Oslo, leading the Language Technology Research Group. Her research focuses on syntactic and semantic text processing using machine learning techniques such as dependency parsing, negation analysis, and sentiment analysis. She teaches courses including IN1140: Introduction to Language Technology , IN5550: Neural Methods in NLP , and INF5830: Natural Language Processing . Her academic interests span natural language processing, machine learning, and computational linguistics, with a particular emphasis on Norwegian language technology. Recent publications highlight work in sentiment analysis (including patient feedback), event extraction from Norwegian news, benchmarking language models, emotion analysis for under-resourced languages (Pashto, Farsi-Dari), and bias detection in multilingual models. She actively contributes to the development of Norwegian language resources such as NorBench, NorQuAD, and NoReC. Current projects include BigMed and SIRIUS , focusing on biomedical text mining and AI infrastructure. Collaborations with colleagues like Erik Velldal, David Samuel, and Vladislav Mikhailov are frequent in her work. Despite no explicit mention of scientific awards, her contributions to NLP and computational linguistics are substantial through publications, datasets, and tool development.

Eduard Kamburjanمشاهده پروفایل

پژوهشگر

• Digital Twins
• Formal Methods
• Hybrid Systems Verification
• +۵ مورد دیگر

Eduard Kamburjan is a Researcher at the University of Oslo , affiliated with the Reliable Systems (PSY) and Data and Knowledge Systems (DKM) research groups. His work bridges formal methods , digital twin engineering , and knowledge graph applications . Research interests include: Formal verification of hybrid systems using deductive methods Digital twin architecture with compositional correctness guarantees Semantic lifting and ontology-driven modeling for complex systems Concurrency analysis and non-determinism in program verification Interactive visualization as serious games for formal methods His 2024-2023 publications demonstrate expertise in digital twin reconfiguration , semantic interoperability , and knowledge-based runtime enforcement . Key contributions include Crowbar for active object verification and ABS simulator toolchain for model-driven engineering. Collaborations span institutions like Springer , ACM , and IEEE , with work featured in Lecture Notes in Computer Science (LNCS) , Software and Systems Modeling (SoSyM) , and Science of Computer Programming . His research integrates RDF data management , behavioral contracts , and modular analysis for distributed systems.

Egor Kostylevمشاهده پروفایل

دانشیار

• Foundations of AI
• Symbolic AI
• Description Logics
• +۹ مورد دیگر

Egor Kostylev serves as an Associate Professor in the Department of Informatics within the Faculty of Mathematics and Natural Sciences at the University of Oslo. His research focuses on the theoretical foundations connecting symbolic and sub-symbolic artificial intelligence, particularly examining relationships between formal logic systems and machine learning approaches. His educational background includes an MSc (Specialist, 2005) and PhD (Candidate, 2009) from Lomonosov Moscow State University under Prof. Vladimir A. Zakharov. He subsequently held research positions at the University of Edinburgh (2010-2013) and the University of Oxford (2013-2020) before joining the University of Oslo in 2020. Kostylev's research interests center on bridging symbolic AI formalisms with sub-symbolic approaches. He investigates connections between various logics (Description Logics, Temporal Logics, Datalog), query languages (SPARQL, Regular Path Queries, OTTR), and machine learning formalisms (Graph Neural Networks, Markov Logic Networks). His work addresses critical challenges in Explainable, Trustworthy, and Green AI through theoretical foundations that connect different AI paradigms. His publication record demonstrates consistent high-impact contributions in theoretical computer science and AI, with numerous publications in top venues including AAAI, LICS, Journal of the ACM, and ICLR. His recent work shows a clear trajectory toward unifying logical reasoning with neural network approaches, particularly through graph neural networks and their connections to logical formalisms. The research spans theoretical foundations of knowledge representation, temporal reasoning in knowledge bases, and the logical expressiveness of modern neural architectures. As a research leader, Kostylev supervises multiple PhD students including Shuwen (Aurora) Liu, Maximilian Pflüger, Roxana Pop, Dongzhuoran Zhou, and Erik Snilsberg. He serves as a Research Theme Leader for the Integreat SFF: Norwegian Centre for Knowledge-driven Machine Learning. His teaching responsibilities include IN3020/4020 Database Systems courses. He leads the Data and Knowledge Management (DKM) research group at the University of Oslo, which focuses on foundational aspects of knowledge representation, database theory, and the intersection with modern machine learning techniques. The group actively collaborates with international researchers and contributes to advancing theoretical understanding of how symbolic and neural approaches to AI can complement each other.

• Scientific Computing
• Numerical Analysis
• Applied Mathematics
• +۵ مورد دیگر

Eirik Valseth is an Associate Professor of Scientific Computing at the Norwegian University of Life Sciences (NMBU), Department of Data Science. He holds concurrent roles as a research associate at the Oden Institute, University of Texas at Austin, and an affiliated researcher at Simula Research Laboratory (Department of Numerical Analysis and Scientific Computing). His expertise lies in advanced finite element methods for PDEs with applications in flood modeling and hydropower systems. Current Affiliation: NMBU (Norwegian University of Life Sciences) Secondary Affiliations: Oden Institute (UT Austin), Simula Research Laboratory Research interests span numerical methods for challenging PDE systems, including: Stabilized finite element formulations Hurricane storm surge and riverine flood modeling Hydropower infrastructure analysis Computational mechanics and applied mathematics His recent publications (2024–2025) emphasize flood risk assessment (compound flooding, dam breaks, dredging impacts), advanced numerical methods (isogeometric analysis, stochastic finite elements, graph-grammar algorithms), and environmental applications (pollution transport, pathogen distribution, mosquito population dynamics after hurricanes). Key trends include cross-disciplinary integration of physics-aware machine learning and robust hydrodynamic simulation tools. Valseth's work extends to software development (e.g., WAVEx for spectral wave models, SWEMniCS for coastal circulation) and large-scale modeling frameworks like the ADCIRC unstructured mesh model for US coasts. Collaborative projects involve institutions such as University of Texas at Austin, Simula, and NOAA.

• Artificial Intelligence
• Machine Learning
• Recommender Systems
• +۴ مورد دیگر

Özlem Özgöbek is an Associate Professor at the Department of Computer Technology and Informatics, Norwegian University of Science and Technology (NTNU). Her research spans artificial intelligence, machine learning, and recommender systems with a focus on privacy, fake news detection, and educational technology. NTNU - Department of Computer Technology and Informatics Her work explores multimodal fake news detection, privacy implications in recommender systems, and technology-enhanced classroom interaction. Recent publications analyze digital education trends and classroom tools. Özgöbek collaborates with international researchers and contributes to news recommendation workshops. Her projects address ethical AI, environmental sustainability, and real-time information processing.

Adín Ramírez Riveraمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Representation Learning
• +۶ مورد دیگر

Adín Ramírez Rivera is a Professor in the Digital Signal Processing and Image Analysis (DSB) group at the Department of Informatics, University of Oslo. His research focuses on representation learning and computer vision, particularly exploring machine learning methods to describe and understand visual data. He is a Senior Member of the IEEE and a member of the ELLIS Society. Education : PhD from Kyung Hee University's Image Processing Lab, South Korea; Bachelor's degree in Engineering from Universidad de San Carlos de Guatemala, majoring in Computer Science and Systems Engineering. Ramírez Rivera's research spans diverse computer vision tasks including facial analysis, object detection, image enhancement, and vision transformers. His work emphasizes self-supervised learning, fair representation learning, and novel neural network architectures for image segmentation and classification. Recent publications highlight trends in vision transformers, crowd counting, facial expression recognition, and fair representation learning. His articles frequently address statistical modeling, feature extraction, and deep learning techniques for visual tasks. Scientific Awards : Senior Member of the IEEE, Member of the ELLIS Society. He collaborates with researchers across institutions, contributing to projects involving anomaly detection, multilingual translation, and astrophysical modeling. His lab affiliations include the Digital Signal Processing and Image Analysis group and the Section for Machine Learning at the University of Oslo.

• Biometrics
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Raghavendra Ramachandra is a Professor at the Department of Information Security and Communication Technology (IIK) , within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU) in Gjøvik. His research focuses on biometric systems, particularly in face, fingerprint, and finger vein recognition, with emphasis on presentation attack detection, morphing attack detection, and deep learning applications. Current research projects include: SALT (2022-2026) : Developing privacy-preserving facial biometric authentication systems. OffPAD (2022-2025) : Creating cryptographic tools and presentation attack detection for fingerprint biometrics. SWAN (2015-2020) : Developing biometric countermeasures against presentation attacks. His recent publications demonstrate technical expertise in: Face morphing attack detection using vision transformers and point cloud networks Image fusion techniques for multispectral biometrics GAN-based synthetic data generation for security evaluation Explainable AI approaches for biometric verification Professor Ramachandra also supervises PhD and Master’s students, and has extensive experience in leading national and EU research initiatives.

Pinar Heggernesمشاهده پروفایل

استاد

• Algorithms
• Graph Theory
• Combinatorics
• +۳ مورد دیگر

Pinar Heggernes is a Professor at the Department of Informatics at the University of Bergen , specializing in algorithms and graph theory . Her research focuses on solving NP-hard graph problems through polynomial-time and fixed parameter tractable algorithms , particularly on chordal , split , and outerplanar graphs . She served as the elected deputy rector of the University of Bergen (2021–2025) with responsibility for education and digital knowledge, and previously as head of the Department of Informatics until 2021. Her career spans leadership in externally funded projects, editorial roles in international journals, and active participation in national research policy, including co-founding the Norwegian Artificial Intelligence Research Consortium (NORA). Education: PhD in Informatics from the University of Bergen. Research Interests: Algorithms for graph classes, computational complexity, parameterized algorithms, and combinatorial optimization. Collaborations: Extensive partnerships with academia, businesses, and public administration, including advisory roles in the Bergen Chamber of Commerce and the Norwegian Cognitive Center . Grants: Project leader for four Research Council of Norway FRIPRO grants (CLASSIS, SCOPE, MIST, and earlier projects) and participant in EU, Trond Mohn Foundation, and National Security Authority grants. Students: Supervised Paloma T. Lima (PhD, 2019) and co-supervised numerous advisees through collaborative projects. Labs/Teams: Coordinated interdisciplinary ICT research and education initiatives at UiB, with international collaborations in the USA, France, and Turkey.

Martin Wulf Gerdesمشاهده پروفایل

دانشیار

• eHealth
• Artificial Intelligence
• Machine Learning
• +۹ مورد دیگر

Martin Wulf Gerdes is an Associate Professor at the Department of Information and Communication Technology, University of Agder, Norway. With a Ph.D. in eHealth and over two decades of experience in telecommunications and healthcare technology research, he focuses on developing user-friendly, interoperable eHealth solutions using ICT and IoT. His career spans roles at Ericsson Eurolab and academic leadership in Norway. Education: M.Sc. equivalent in General Electrical Engineering (1998) from RWTH Aachen, emphasizing ICT. Ph.D.: Information and Communication Technologies with eHealth specialization (2019) from University of Agder. Research Interests: Gerdes explores AI-driven decision support systems, distributed eHealth infrastructure, telemedicine, and assistive technologies for chronic disease management. His work bridges IoT, data privacy, and user-centered design in healthcare robotics. Teaching: Since 2014, he has taught at bachelor's and master's levels, currently leading courses in Communication & Cooperation, Technology Understanding, and seminar projects in eHealth. Scientific Publications: His 15 most recent articles (2020-2024) span AI in dental radiology, blood glucose prediction, digital twins, and ethical considerations in healthcare algorithms. Topics include IoT integration, data privacy, and pandemic analytics.

• Next Generation Internet
• Human-centric Networking
• Ontology and Knowledge-based Management
• +۱۳ مورد دیگر

David Palma is an Associate Professor at the Department of Information Security and Communication Technology , Faculty of Information Technology and Electrical Engineering , Norwegian University of Science and Technology (NTNU). His research focuses on next-generation networking paradigms, including intent-based networking , knowledge-driven management , and IoT device automation . Key research interests include: Human-centric Internet of Things (IoT) Ontology-based network management Cloud/Edge/Fog computing for IoT Arctic and satellite communication 5G integration in smart grids His recent publications (2024-2019) highlight trends in knowledge graphs for network compliance, XR applications in critical sectors, UAV-based emergency networks , and 5G-enabled smart grid protection . Articles also explore Arctic connectivity via satellite swarms and energy-efficient IoT management.