جست‌وجوی استاد و پژوهشگر

• Railway Engineering
• Transportation Systems
• Structural Engineering
• +۳ مورد دیگر

Albert Lau is an Associate Professor of Civil and Environmental Engineering at the Norwegian University of Science and Technology (NTNU), located in Trondheim, Norway. He specializes in railway engineering, structural dynamics, and transportation systems. Lau holds leadership roles as the Study Program Leader for the MSc in Road, Railway, and Transportation Engineering, overseeing curriculum development and program coordination. His research focuses on railway track design, dynamic modeling of train-track interactions, and infrastructure maintenance, with projects such as the MeTinT initiative (Measurement with Train in Regular Traffic). He has extensive experience supervising master’s and PhD students, and his work emphasizes innovation in rail infrastructure and sustainable transportation solutions. Education and Professional Background: Lau earned his PhD from NTNU in 2018, focusing on numerical simulations of railway turnouts. Prior roles include Postdoc (2018–2020) and Assistant Professor (2017–2018) at NTNU, and teaching at Oslo Metropolitan University (2020). His industry experience includes roles as a Design Engineer (2010–2012) and Project Engineer (2013–2014) in Malaysia, where he managed construction projects and structural design. Research Interests: Lau’s work spans railway track dynamics, infrastructure health monitoring, and machine learning applications in transportation. Key projects include developing digital twins for railway test sites and analyzing ground displacement impacts on track anomalies. His contributions to the Road, Railway and Transport Group at NTNU aim to advance rail safety and efficiency through interdisciplinary approaches. Teaching and Outreach: Lau coordinates courses such as TBA4225 (Railway Engineering) and BA6012 (Fundamental Railway Technology). His outreach includes expert commentary on railway incidents, such as an interview on NRK (2024) discussing potential causes of a train accident. Current initiatives focus on revitalizing regional rail services and optimizing train positioning systems.

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.

Andrei Kutuzovمشاهده پروفایل

دانشیار

• Computational Linguistics
• Natural Language Processing
• Semantic Change Detection
• +۶ مورد دیگر

Andrei Kutuzov is an Associate Professor in the Language Technology Group (LTG) within the Department of Informatics at the University of Oslo. He serves as the Norwegian on-site manager of the High-Performance Language Technology (HPLT) project and has made significant contributions to computational linguistics and natural language processing. His research primarily focuses on computational linguistics and natural language processing, with specialized expertise in semantic change detection, diachronically aware language models, distributional semantics, and large language models. Kutuzov has been instrumental in developing Norwegian language resources including NorBERT, NorELMo models, and the very large-scale NORA.LLM generative models. He created WebVectors, a web service for exploring neural distribution models for Norwegian and English texts. Analysis of his recent publications reveals a strong focus on semantic change modeling, multilingual dataset development, and Norwegian language technology. His work spans from theoretical linguistic analysis to practical applications in language modeling, with significant emphasis on low-resource and Nordic languages. Kutuzov's research demonstrates a consistent trajectory toward improving language models' understanding of semantic evolution and developing robust evaluation frameworks for Norwegian language processing. Norwegian Artificial Intelligence Research Consortium (NORA) award as Distinguished Early Career Researcher (2022) Kutuzov teaches several advanced courses including IN5550 - Neural Methods in Natural Language Processing (2019-2025) and IN3050 - Introduction to Artificial Intelligence and Machine Learning (2024-2025). He has received research funding through the HPLT project which focuses on developing high-performance language technologies. His laboratory work centers around the Language Technology Group at UiO, where he collaborates on developing Norwegian language resources and models, with particular emphasis on diachronic semantic analysis and multilingual capabilities.

Mohan Lal Kolheمشاهده پروفایل

استاد

• Smart Grid
• Renewable Energy Systems
• Electric Vehicles
• +۵ مورد دیگر

Professor Mohan Lal Kolhe is a distinguished academic at the University of Agder , serving as a Full Professor in Smart Grid and Renewable Energy within the Faculty of Engineering and Science and the Department of Engineering Sciences . With over three decades of international academic experience, he has held positions at prestigious institutions including University College London, University of Dundee, and Hydrogen Research Institute in Canada. His career spans technical innovation, policy development (e.g., as a member of South Australia’s Renewable Energy Board), and extensive research leadership in sustainable energy systems. Research Leadership : Focus on Smart Grid integration, Electric Vehicles, Hydrogen Energy, Solar/Wind Systems, and Techno-Economic Energy Analysis. Global Recognition : Listed in the top 2% of scientists worldwide (2020-2023) by Stanford University, with 10 publications averaging 200+ citations. Recent publications emphasize advanced optimization techniques for renewable integration, EV charging infrastructure, hydrogen production, and power system stability. His work has secured competitive funding from entities like the Norwegian Research Council and EU programs. Awards and Expert Roles : Top 2% Global Scientist (Stanford, 2020-2023) Highly Cited Researcher (Top 10 publications, 200+ avg. citations) Expert evaluator for European Commission, Royal Society London, EPSRC, and Cyprus Research Foundation He actively contributes to international conferences as keynote speaker and editorial board member, with leadership roles in research groups like Autonomous and Cyber-Physical Systems and Energy Systems .

• Mining Rock Mechanics
• Rock Mass Properties
• Underground Support Systems
• +۲ مورد دیگر

Hakan Basarir is a Professor in the Department of Mining Engineering at the Norwegian University of Science and Technology (NTNU), Trondheim, Norway. His research and teaching focus on mining rock mechanics, rock mass characterization, underground support systems, and the application of soft computing methods in mining engineering. PhD in Mining Engineering (2002) 20+ years of research and teaching experience 60+ publications in journals and conferences Research Interests include rock mass property prediction using measurement while drilling (MWD) techniques, numerical modeling of mining structures, optimization of mine support systems, and sustainable material development. His work integrates machine learning and computational methods to address challenges in mining geomechanics and backfill design. Recent Publications highlight advancements in AI-driven lithology prediction, eco-concrete formulation, and backfill mixture optimization. He has also contributed to tunnel stability analysis and seismic rock slope modeling. Teaching includes advanced courses in mining engineering, mineral production modeling, and specialization projects in geotechnology.

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.

• 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.

• Language Didactics
• Norwegian Language Education
• Grammar Instruction
• +۷ مورد دیگر

Eli Anne Eiesland is an Associate Professor in the Department of Primary and Secondary Teacher Education at the Faculty of Education and International Studies, Oslo Metropolitan University (OsloMet). Her work bridges linguistic research and classroom practice, with a strong focus on grammar instruction, language didactics, and the evolution of Norwegian in educational materials. Her research interests include language didactics , grammar pedagogy , linguistic change in textbooks , second language acquisition , and exploratory approaches to language teaching . She investigates how grammar is taught in schools, how language evolves in educational contexts, and how students and teachers engage with language as a system. Her recent work explores the impact of digital tools and AI on language learning and writing motivation. The trends in her publications show a consistent focus on contextualized grammar teaching , teacher reflection , and language change in educational materials . Her interdisciplinary approach combines corpus linguistics, pedagogical theory, and classroom-based research. She frequently collaborates with colleagues such as Urd Vindenes and Signe Laake, contributing to both Norwegian and international scholarly discourse. Scientific contributions and dissemination: Over 11 scientific publications, including articles in Journal of Child Language , Nordic Journal of Linguistics , and Pedagogical Linguistics 2 textbooks, including Utforskende arbeid med grammatikk i skolen (2022) 1 research report on Norwegian noun-noun compounds (2016) 73 dissemination works, including public lectures, conference presentations, and media contributions Eiesland actively contributes to teacher education and curriculum development. She has been involved in projects integrating linguistics into teacher training and analyzing language change in national curricula. Her work supports both pre-service and in-service teachers in developing deeper metalinguistic awareness and innovative teaching strategies. She has presented at national and international conferences, including the International Conference on Construction Grammar and the NordAnd conference. She is a key member of the Text and Disciplinary Didactics research group at OsloMet, where she collaborates on projects related to language learning, grammar instruction, and textbook analysis. Her work emphasizes practical applications of linguistic research in educational settings, aiming to enhance both teaching quality and student engagement with language.

Morten Brunمشاهده پروفایل

دانشیار

• Topological Data Analysis
• Computational Topology
• Persistent Homology
• +۳ مورد دیگر

Morten Brun is an Associate Professor at the Department of Mathematics, University of Bergen. His research spans computational topology, persistent homology, and applications in biology and data science. Email: morten.brun@uib.no Research Interests: He specializes in topological data analysis, focusing on sparse nerves, relative persistent homology, and computational geometry. His work applies topological methods to biological problems, including drug resistance modeling in tuberculosis and immune profiling in multiple sclerosis. Recent Publications: His 2025 work includes hypercubic modeling of tuberculosis drug resistance and high-dimensional immune profiling post-stem cell transplantation. Earlier articles explore computational topology techniques (2017-2024) and interdisciplinary applications in toxicology and systems biology.

Ingrid Hobæk Haffمشاهده پروفایل

دانشیار

• Multivariate statistics
• Copulae
• Skew distributions
• +۳ مورد دیگر

Ingrid Hobæk Haff is an Associate Professor in insurance mathematics and statistics at the Department of Mathematics, University of Oslo since 2015. She holds a master's degree in Industrial Mathematics from NTNU (2002) and a PhD from the Statistics for Innovation center (2008–2012), with a 20% position as a research scientist at the Norwegian Computing Centre. Previously, she worked there as a research scientist and senior scientist. Her research interests focus on multivariate statistics, copulae, skew and heavy-tailed distributions, and applications in insurance and finance. She has contributed to advancements in statistical modeling, particularly in copula constructions and their applications to risk assessment and extreme value analysis. Key awards include the Sverdrup award for young scientists and the mathematical award of Hanna og John Olav Stubban . She is affiliated with the Statistics and Data Science research group and the completed Stochastics of Renewable Energy Markets (STORE) project. Her work spans interdisciplinary collaborations, including applications in immunology and bioinformatics, leveraging machine learning for antibody-antigen interaction studies and synthetic data generation.

• Computational Solid Mechanics
• Experimental Solid Mechanics
• Structural Optimization
• +۴ مورد دیگر

Miguel Costas Piñó serves as Associate Professor in the Department of Structural Engineering at the Norwegian University of Science and Technology (NTNU), where he has been faculty since 2017 and was promoted to Associate Professor in 2021. His research bridges computational mechanics with practical engineering applications in structural safety and material behavior. Education PhD in Civil Engineering, University of A Coruña (2016), thesis: "Crashworthiness analysis and design optimization of hybrid impact energy absorbers" (awarded Cum Laude, International Distinctions, and Extraordinary Doctorate Award) Master of Science in Civil Engineering, University of A Coruña (2011), specialization in Structural Engineering Research Focus Dr. Costas's work centers on computational and experimental solid mechanics , with particular expertise in metal plasticity , aluminium structures , and crashworthiness under extreme loading conditions. His laboratory investigates ballistic penetration phenomena and structural optimization for energy-absorbing systems, utilizing advanced finite element methods and physical testing protocols. Current projects integrate machine learning with traditional mechanics to model complex failure modes in multi-material systems. Publication Trends His 2020-2025 publications reveal three dominant trajectories: (1) ballistic resistance of additively manufactured metals for defense applications, (2) crash safety of electric vehicle battery systems, and (3) advanced modeling of mechanical joints. These works consistently combine experimental validation with computational innovation, particularly in neural network applications for large-scale structural simulations. Awards Cum Laude Doctorate International Distinctions (PhD) Extraordinary Doctorate Award Professional Activities Dr. Costas actively collaborates with industry partners on structural safety projects and serves as principal investigator for multiple NTNU research initiatives. His teaching includes graduate courses in materials mechanics, and he maintains dual professional engagement as a classical pianist with Norway's collaborative music community. Current work focuses on AI-driven crash simulation frameworks and next-generation armor materials.

Kai Rune Larsenمشاهده پروفایل

استاد

• Natural Language Processing
• Survey Methodology
• Machine Learning
• +۳ مورد دیگر

Kai Rune Larsen holds dual roles as a Professor II (part-time) at the Department of Information Systems , University of Agder , and an Associate Professor of Information Systems at the Leeds School of Business , University of Colorado Boulder . He is also a courtesy faculty member in the Department of Information Science at the College of Media, Communication, and Information , a Research Advisor to Gallup, and a Fellow of the Institute of Behavioral Science . His research focuses on Natural Language Processing (NLP) , Survey Methodology , and Machine Learning . He is renowned for resolving Edward Thorndike’s Jingle Fallacy and pioneering the Semantic Theory of Survey Response (STSR) , which emphasizes linguistic relationships in survey data. His work on Automated Machine Learning (published by Oxford University Press) further bridges theoretical and applied research. Larsen’s projects include developing INN and TheoryOn —award-winning technologies—alongside managing the Theories Used in IS Wiki . As Director of the Federally Supported Human Behavior Project , he integrates NLP and behavioral theories to create a transdisciplinary framework for predicting human behavior, with applications in technology adoption, investment decisions, public health, and voter behavior. His scientific honors include being a Fellow of the Institute of Behavioral Science and having a paper co-authored with Lee and Kozar ranked among the top 10 most downloaded in the Association for Information Systems’ electronic library.

• Building Information Modeling (BIM)
• Digital Twins
• Construction Management
• +۳ مورد دیگر

Eilif Hjelseth is a Professor at the Department of Civil and Environmental Engineering, Norwegian University of Science and Technology (NTNU), with a focus on Building Information Modeling (BIM), Digital Twins, and Construction Technology. His work spans digital transformation in the AECO industry, automated compliance checking, and sustainable construction practices. Role: Professor, Department of Civil and Environmental Engineering University: Norwegian University of Science and Technology (NTNU) Research: BIM, Digital Twins, Construction Automation, Sustainable Development, Semantic Modeling Outreach: Authored multiple textbooks and reports on VDC implementation and BIM standards His recent publications emphasize digital twin frameworks , semantic data integration , and automation in construction , particularly through cross-validation, knowledge graphs, and circular economy applications. While specific educational details or grants aren't mentioned, his work demonstrates extensive collaboration with researchers across Norway and Europe on digitalization challenges. Key trends in his 2024–2025 articles include cross-domain BIM integration , deep learning for component classification , and standardization of digital product passports . Earlier works (2012–2020) established foundational concepts in BIM model checking, lifecycle costing, and regulatory digitization.

Ali Goharمشاهده پروفایل

پژوهشگر

• Communication Networks
• 5G and 6G
• Mathematical Modeling for Communication Networks
• +۳ مورد دیگر

Ali Gohar is a Researcher at the Department of Electrical Engineering and Computer Science, Faculty of Science and Technology, University of Stavanger. He holds a PhD in Computer Science from the same university (2019-2024) and has supervised multiple master's theses. Education: PhD - Computer Science, University of Stavanger (2019-2024) MS - Computer Science, Kookmin University (2017-2019) BS - Telecommunication, COMSATS University Islamabad (2011-2014) Certifications: AI in Education, Constructive Alignment and Digital Course Design, Intercultural Competence, NyTi Teaching Certificate, CCNA Research Interests: Ali Gohar's work centers on Deep Reinforcement Learning, 5G/6G communication networks, mathematical modeling, and resource allocation optimization. His research develops autonomous algorithms for network systems in smart cities and space-air-ground integrated networks. Publications: He has contributed to 15 recent publications spanning peer-reviewed journals and conference proceedings, focusing on network slice brokers, virtual network embedding, scalable video coding, and remote driver selection. These works intersect with AI, telecommunications, and urban infrastructure. Supervision: He has mentored students on projects including security-aware resource allocation, federated learning, and vertical industry optimization in smart cities.

Mahdi Kioumarsiمشاهده پروفایل

دانشیار

• Concrete Technology
• Structural Engineering
• Corrosion Engineering
• +۶ مورد دیگر

Mahdi Kioumarsi is an Associate Professor at Oslo Metropolitan University (OsloMet) and Østfold University College, specializing in structural engineering with a focus on advanced concrete technology and deteriorated concrete structures. He holds a Ph.D. from NTNU (2015) and an M.Sc. from Amirkabir University of Technology (2009). His research integrates experimental work with finite element modeling, addressing corrosion effects, structural performance, and sustainable construction materials. Education: Ph.D. in Structural Engineering, NTNU (2015) M.Sc. in Structural and Earthquake Engineering, Amirkabir University (2009) Research Interests: His work spans from material-level studies to full-scale structural analysis, emphasizing corrosion mitigation, nonlinear behavior, and sustainable materials. Key focus areas include: Corrosion-resistant concrete formulations Seismic behavior of historical structures Lifecycle assessment of construction materials Machine learning applications in structural analysis Recent Research Trends: Most recent publications emphasize sustainable materials innovation (e.g., ultra-high performance concrete with waste additives), corrosion impact analysis, and AI-driven predictive models for material properties. He co-edited the 2025 conference proceedings on net-zero built environments. Labs/Teams: Active in research groups like the Building and Environment Research Group and Structural Engineering Research Group (SERG). Collaborates internationally on projects like HYPERION (resilience of historic areas).