جست‌وجوی استاد و پژوهشگر

• Metal Forming
• Aluminum Alloys
• Product Development
• +۳ مورد دیگر

Torgeir Welo is a Professor at the Department of Mechanical and Industrial Engineering , Norwegian University of Science and Technology (NTNU) . He specializes in metal forming , particularly aluminum alloy structures , with a focus on plastic bending behavior , dimensional stability , and 3D forming technologies . His research also encompasses Lean Product Development , emphasizing knowledge reuse and maximizing customer value in automotive and aerospace applications. Key Research Areas : Metal Forming, Aluminum Processing, Springback Control, Lean Development, Additive Manufacturing, Material Substitution Teaching : Courses on Aluminum Technology , Metal Forming Analysis , and Machine Element Design Publications (15 most recent): Focus on springback monitoring , charge weld evolution , flexible forming , machine learning applications , and circular economy frameworks in metal manufacturing.

• Robotics
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Dr. Danesh Tarapore is an Associate Professor at the University of Southampton specializing in robotics and AI. He focuses on human-robot interaction, swarm intelligence, and autonomous systems. His current research involves developing resilient robotic teams and optimizing learning algorithms for constrained environments. He supervises 6 PhD students in the iPhD MINDS and Computer Science programs. Dr. Tarapore's work bridges theoretical advancements with practical applications in autonomous navigation, multimodal dataset creation, and quality-diversity optimization. His publications span conferences like HRI and journals in robotics and AI. He collaborates with institutions like the University Hospital Southampton and the Boldrewood Innovation Campus. Research Interests: Human-robot collaboration, swarm systems, machine learning, and adaptive control Key Contributions: HRI-SENSE dataset, evolutionary subset selection algorithms, forest navigation frameworks Grants and Funding: Active projects in multi-agent systems and resilient robotics Dr. Tarapore maintains active roles in the robotics community through conference participation and interdisciplinary collaborations.

• Artificial Intelligence
• Machine Learning
• Medical Technology
• +۷ مورد دیگر

Anis Yazidi is a Professor at Oslo Metropolitan University, affiliated with the Faculty of Technology, Art and Design and the Department of Information Technology. His research focuses on Artificial Intelligence, Machine Learning, Medical Technology, and IoT Security, with a particular emphasis on Applications of AI in Healthcare, EEG Signal Processing, and Digital Transformation. Active research projects include AI Mind (dementia diagnostics), Glycopathology in dry eyes, and Pain and mental distress analysis Completed projects: Digital hate speech analysis, AI in reproductive technology, Nano-antibiotics development His recent publications (2023-2025) demonstrate expertise in: Tsetlin Automaton algorithms for concept learning EEG classification using visibility graphs and vision transformers AI ethics frameworks for medical practice Deepfake detection methodologies Collaborative work spans institutions in Norway, Czech Republic, and international AI research communities.

Hans Jonas Fossum Moenمشاهده پروفایل

دانشیار

• Autonomous Systems
• Sensor Technologies
• Robotics
• +۳ مورد دیگر

Hans Jonas Fossum Moen is an Associate Professor with a 20% appointment at the Department of Technology Systems, University of Oslo (UiO), and holds a 100% position as a researcher at the Norwegian Defence Research Establishment (FFI). His primary affiliation is with the Section for Autonomous Systems and Sensor Technologies. He is based at the Kjeller campus, with a visiting address at Gunnar Randers Road 19 and a postal address at Postboks 70. His research focuses on advancing autonomous systems and sensor technologies, particularly in the domains of swarm robotics, multi-agent coordination, and optimization algorithms. Key areas include UAV navigation, distributed localization in IoT networks, radar detection enhancement, and adaptive control systems for multi-functional swarms. He emphasizes the integration of biological principles into robotic systems, as evidenced by his participation in the ICRA 2018 Workshop on Swarms. His publications consistently highlight contributions to swarm intelligence, with a focus on improving data quality and efficiency in robotics applications. He has collaborated extensively with colleagues such as Kyrre Glette, Oleg Yakimenko, and Jan Dyre Bjerknes, exploring topics ranging from task allocation in multi-agent systems to evolutionary algorithms for filter optimization. His work bridges theoretical computer science with practical engineering challenges in autonomous systems. No scientific awards have been explicitly mentioned in the provided texts. Moen’s advising and grants narrative indicates no listed advisees or active grant projects, though his 20% UiO position suggests potential involvement in academic supervision. His primary research activities are embedded within FFI and the Autonomous Systems section at UiO, contributing to interdisciplinary efforts in sensor technologies and robotic systems.

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.

• 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.

Arnoldo Frigessiمشاهده پروفایل

استاد

• Statistics
• Biostatistics
• Machine Learning
• +۵ مورد دیگر

Arnoldo Frigessi is Professor of Statistics at the University of Oslo, where he leads the Oslo Center for Biostatistics and Epidemiology and serves as director of BigInsight—a Centre of Excellence for Research-Based Innovation. This consortium unites industry, business, public actors, and academia to develop model-based machine learning methodologies for big data, with strong emphasis on health applications. His research centers on statistical methodology driven by real-world scientific challenges, specializing in stochastic models for complex dependence structures and computationally intensive inference algorithms. Core application domains include: Genomics and personalized cancer therapy (particularly breast and lung cancer) Infectious disease modeling (including pandemic response) eHealth, sensor data analysis, and recommender systems Personalized marketing and viral diffusion dynamics Analysis of his 15 most recent publications (2024-2025) reveals dominant themes in cancer systems biology , where he integrates multi-omics, single-cell transcriptomics, and computational modeling to decode tumor evolution under therapy. Parallel work advances infectious disease epidemiology through time-varying reproduction number estimation and mobility-based transmission modeling, while methodological innovations span synthetic data generation (TVineSynth), causal inference via target trial emulation, and Bayesian ranking models for recommender systems. Scientific Awards: No specific awards mentioned in source materials Frigessi actively supervises graduate students, including a Department of Informatics project on "Utilizing covariate information in recommender systems." His leadership of BigInsight—funded as a Research-Based Innovation Centre by the Research Council of Norway—secures major grants supporting interdisciplinary collaborations with industrial partners (e.g., Telenor, DNB) and public health institutions. Current projects integrate real-world clinical data with mechanistic models for treatment optimization. He directs BigInsight's multidisciplinary team of statisticians, computer scientists, and domain experts, while leading the Oslo Center for Biostatistics and Epidemiology's efforts in developing statistical frameworks for complex health data. These initiatives drive Norway's national strategy for data-driven health innovation.

Sverre A. C. Kittelsenمشاهده پروفایل

استاد

• Production theory
• Efficiency measurement
• Regulation
• +۲ مورد دیگر

Sverre A. C. Kittelsen is a Senior Research Fellow at the Frisch Centre and holds a Professor II position at the Department of Health Management and Health Economics (HELED) within the Institute of Health and Society at the University of Oslo. His academic career spans over three decades with a Doctor Politicae (Dr.Polit.) in Economics from the University of Oslo (1998) and a Cand.Polit. in Economics from the University of Bergen (1985). His primary research interests include Production theory, Efficiency measurement, Regulation, Health Economics, and Energy Economics. Kittelsen has established himself as a leading researcher in Nordic healthcare efficiency analysis, particularly focusing on hospital productivity, quality assessment, and the economic implications of healthcare reforms across Scandinavian countries. He has also made significant contributions to energy economics, especially regarding European energy markets and climate policy. His recent publication record demonstrates remarkable productivity with 15+ publications since 2017, showing consistent scholarly output across health economics and energy economics. The research trends indicate a strong focus on empirical analysis of healthcare systems using advanced econometric methods, particularly data envelopment analysis (DEA) and productivity measurement techniques. His work frequently involves cross-national comparative studies within the Nordic region, examining how different healthcare organizational structures affect efficiency and quality outcomes. Kittelsen has been involved in numerous research projects related to public sector efficiency, including hospital productivity studies, tax office efficiency analysis, and energy market modeling. His work often bridges theoretical economic concepts with practical policy implications for healthcare and energy sectors. His institutional affiliations with both the Frisch Centre (a prominent Norwegian economic research institute) and the University of Oslo position him at the nexus of academic research and policy-relevant analysis. This dual affiliation enables him to contribute both to theoretical economic literature and to practical policy discussions in Norway and the broader Nordic region.

Hanna-Riikka Roineمشاهده پروفایل

دانشیار

• Digital Culture
• Narrative Theory
• Game Studies
• +۷ مورد دیگر

Hanna-Riikka Roine is an Associate Professor in Digital Culture at the University of Bergen, affiliated with the Department of Linguistic, Literary and Aesthetic Studies and the Center for Digital Narrative. Her research bridges digital media, narrative theory, and speculative fiction, with a focus on games, imagination, and democratic discourse. Her research interests include: Digital culture and computational media Narrative theory and worldbuilding Game studies, particularly narrative agency in video games Speculative fiction as a tool for imagining futures Transmediality and digital storytelling The role of fiction in democratic imagination Her recent publications explore narrative forms in social media, romance mechanics in games like Dragon Age , and the limits of narrativizing environmental systems. She investigates how digital technologies entangle with human imagination, especially in speculative contexts. Notable research projects include: CO-SPEC (Academy of Finland, 2020–2023): On human-computer entanglement in speculation Imagine, Democracy! (Kone Foundation, 2023–2026): On narrative fiction as a democratic imagination tool in Finland She has co-edited special issues and books on digital environments, games, and speculative fiction, demonstrating leadership in interdisciplinary scholarship. While no formal scientific awards are listed, her funded projects and editorial roles signify academic recognition. She has collaborated with researchers such as Laura Piippo, Esko Suoranta, and Susanna Lindberg, contributing to a vibrant research network. She has also developed innovative pedagogical methods, such as using Twine game jams for assessment in higher education. Her work is situated within a strong digital humanities framework, connecting literary theory with contemporary media and societal challenges.

• Sports Cardiology
• Cardiovascular Incidents in Athletes
• Paralympic Athlete Health
• +۴ مورد دیگر

Hilde Moseby Berge serves as an Associate Professor in the Department of Sports Medicine at the Norwegian School of Sport Sciences. She is actively engaged in research and clinical work focused on sports medicine, particularly in the areas of sports cardiology and Para athlete health. Her contact information indicates ongoing institutional affiliation with direct lines for professional communication. Dr. Berge's research interests center on cardiovascular health in athletes, with particular emphasis on sudden cardiac arrest prevention, ECG interpretation in sports settings, and health monitoring systems for elite athletes. Her work spans both Olympic and Paralympic populations, with significant contributions to understanding injury and illness patterns among these athletes. She has developed expertise in periodic health evaluations and has led initiatives to improve cardiac emergency response in sports settings, particularly through projects like ParaFRISK and YoungParaFRISK. Analysis of her publication record reveals consistent focus on cardiovascular incidents in sports, particularly examining the limitations of cardiac screening programs and the prevalence of cardiac events even among athletes with previously negative screening results. Her more recent work has expanded to include comprehensive health monitoring systems for Paralympic athletes and community-based interventions to improve survival from cardiac events in sports settings. Her research bridges clinical practice with epidemiological investigation, contributing significantly to evidence-based approaches in sports medicine. Dr. Berge maintains active collaborations with key researchers in sports medicine including Kathrin Steffen, Benjamin Clarsen, and Roald Bahr. Her work has contributed to national health monitoring programs for Norwegian Olympic and Paralympic athletes, with longitudinal data collection spanning multiple Games cycles. She appears to be involved in practical implementation of health services during major sporting events, as indicated by her publications related to preparation for the Tokyo and Paris Games.

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.

• Internet of Things
• Fog Computing
• Edge Computing
• +۹ مورد دیگر

Amirhosein Taherkordi is a Professor in the Networks and Distributed Systems group at the Department of Informatics, University of Oslo, Norway. His research focuses on resource-efficiency, scalability, adaptability, dependability, mobility and data-intensiveness of distributed systems for emerging computing technologies including Internet of Things (IoT), Fog/Edge/Cloud Computing, and Cyber-Physical Systems (CPS). Dr. Taherkordi received his Ph.D. from the Informatics Department at the University of Oslo under the supervision of Prof. Frank Eliassen, with his thesis titled "Programming Wireless Sensor Networks: From Static to Adaptive Models." He holds an M.Sc. in Information Technology Engineering (Software Engineering) from University of Science and Technology and a B.Sc. in Computer Engineering from Sharif University of Technology. His research spans multiple domains of distributed systems with emphasis on practical applications. He investigates energy efficiency in wireless sensor networks, communication optimization in IoT systems, and adaptive resource allocation in edge computing environments. His work addresses critical challenges in network traffic classification, federated learning for vehicular networks, and data processing across heterogeneous platforms. Analysis of his recent publications reveals a strong trajectory toward communication-efficient federated learning techniques for vehicular networks, energy-aware protocols for IoT data collection, and advanced machine learning approaches for network traffic analysis. His research consistently focuses on optimizing resource usage while maintaining system performance and privacy in distributed architectures. Dr. Taherkordi actively contributes to several research initiatives including the CPS Lab at UiO for Cyber Physical Systems, DILUTE: Fluid Service Abstraction for Large-Scale Cloud IoT Systems, and the Gemini Centre on IoT at UiO. His work bridges theoretical advances with practical implementations in transportation systems, environmental monitoring, and industrial automation.

Meghan Balkمشاهده پروفایل

پژوهشگر ارشد

• Ecological and Evolutionary Biology
• Paleobiology
• Functional Morphology
• +۷ مورد دیگر

Meghan Balk is a Postdoctoral Fellow with the Evolution and Paleobiology Group at the Natural History Museum, University of Oslo. Her work combines museum collections and trait databases to investigate how inter- and intra-specific traits change across time and space. She is passionate about digitizing museum data and enabling FAIR data principles for continued exploration of data-driven science across evolutionary biology and ecology. Balk received her Ph.D. from the University of New Mexico in 2017 with a concentration in Interdisciplinary Science through the Department of Biology. She earned her B.S. from the University of California, Davis in 2010 in the Department of Evolution, Ecology, & Biodiversity, with a minor in Paleobiology through the Department of Geology. Her academic journey reflects a strong foundation in both biological sciences and geological perspectives on evolutionary processes. Her research employs both micro- and macroscopic approaches to understand abiotic and biotic drivers of phenotypic evolution. She investigates within and among lineage phenotypic evolution using fossil and modern records of organisms like bryozoans. Her work on abiotic drivers examines body size changes in species like the bushy-tailed woodrat across geological time, while her research on biotic drivers explores predator-prey relationships in the fossil record, particularly focusing on species like Otodus megalodon. She utilizes machine learning and computational approaches to extract morphological trait data from specimen images. Balk's publication record demonstrates expertise across evolutionary biology, paleontology, ecology, and computational approaches. Her recent work focuses on developing FAIR and modular workflows for image-based knowledge discovery in the emerging field of imageomics. She has made significant contributions to understanding body size evolution across geological time, predator-prey relationships in the fossil record, and promoting open science principles for trait-based research. Her work bridges traditional paleontological methods with cutting-edge computational techniques. Balk is actively involved in several research projects including ROCKS PARADOX (Dissecting the paradox of stasis in evolutionary biology) and Machine-readable Nature (MaNa). She collaborates with researchers across institutions to create ontologies and workflows for trait data, such as the Functional Trait Resource for Environmental Studies (FuTRES) project and the Biology-Guided Neural Networks project. She teaches courses including Foundational Open Science Skills workshop, Git for Mere Mortals webinar, and R Basics Crash course, emphasizing the importance of reproducible research practices.

• Cyber-Physical Systems
• Distributed Systems
• Internet of Things (IoT)
• +۷ مورد دیگر

Loïc Guégan is an Associate Professor in the Department of Informatics at UiT The Arctic University of Norway, affiliated with the Faculty of Science and Technology. His work is centered on energy-efficient cyber-physical systems and distributed computing, particularly in extreme and constrained environments such as the Arctic. His research focuses on cyber-physical systems (CPS) , distributed data dissemination , IoT and edge computing , and energy-efficient protocols . He actively contributes to the development of the Distributed Arctic Observatory (DAO) project and has designed tools like the ESDS simulator for evaluating distributed systems in challenging conditions. His technical work includes power monitoring using single-board computers and the design of the LoRaLitE protocol for low-energy wireless communication. The recent publications highlight a consistent trend in data dissemination strategies , simulation frameworks , and energy optimization for IoT and edge systems deployed in remote, resource-limited settings. These studies often leverage real-world Arctic deployments and epidemic-style algorithms to ensure robustness and scalability. Loïc is a member of the Cyber Physical Systems (CPS) research group and contributes to projects including The IoT-to-Extreme-Edge Infrastructure and Sustainable Distributed Systems for Sustainable Research and Education . He teaches courses such as Parallel Programming and Operating Systems . His research is supported through institutional and project-based funding, though specific grants are not detailed in the text.

• Cryptographic Voting Systems
• Key Exchange Protocols
• Cloud Cryptography
• +۳ مورد دیگر

Kristian Gjøsteen is a Professor at the Department of Mathematical Sciences within the Norwegian University of Science and Technology (NTNU) . He actively contributes to the Algebra Group and specializes in cryptographic systems with a focus on electronic voting , security proofs , and privacy-enhancing technologies . Educational Background: MSc and PhD from NTNU Research Interests: His work spans cryptography , key exchange protocols , cloud security , and formal verification of security mechanisms. Particular emphasis is placed on coercion-resistant voting systems , lattice-based encryption , and blockchain privacy models . Article Trends: Recent publications demonstrate expertise in post-quantum cryptography , machine-checked security , and privacy-preserving voting architectures . Collaborative efforts explore hybrid cryptographic schemes , verifiable decryption , and mix-net implementations for secure elections.