جست‌وجوی استاد و پژوهشگر

• 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.

• Safe Reinforcement Learning
• Data-Driven Model Predictive Control
• Artificial Intelligence
• +۷ مورد دیگر

Sebastien Nicolas Gros is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). His research focuses on safe reinforcement learning (RL) and data-driven model predictive control (MPC), with applications in energy systems, biomedical engineering, and autonomous vehicles. Institution: Norwegian University of Science and Technology Department: Engineering Cybernetics His work emphasizes AI-driven optimization for domestic energy storage, battery integration, and smart building management. Collaborations include Equinor, DNV, Kongsberg, Volvo, and CorPower Ocean. Key themes in his publications include: Control theory for renewable energy systems (wave energy converters, buildings) Biomedical applications (artificial pancreas, glucose monitoring) Transportation systems (electric vehicles, autonomous ships) Machine learning integration with physical models He supervises 6 PhD students and co-supervises projects on multi-rotor wind turbines and industrial PhD collaborations. The articles demonstrate a convergence of RL, MPC, and uncertainty quantification across energy, biomedical, and transportation domains.

• 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.

• Control Theory
• Partial Differential Equations
• Drilling Engineering
• +۶ مورد دیگر

Ole Morten Aamo is a Professor at the Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway. His office is located at Elektro D/B2, D344, Gløshaugen, O. S. Bragstads plass 2, and he can be reached at aamo@ntnu.no or by phone at 73594386. Professor Aamo's research focuses on control theory with particular emphasis on partial differential equations (PDEs) and their applications in drilling engineering and the petroleum industry. His work spans multiple areas including boundary control of hyperbolic systems, adaptive control methodologies, vibration control in drilling operations, and leak detection systems for pipe networks. His research combines theoretical control developments with practical applications in the oil and gas sector, particularly addressing challenges related to stick-slip phenomena, torsional vibrations, and pressure oscillations in drilling operations. His publication record reveals a consistent trajectory of high-impact research in control systems, with a notable shift toward integrating machine learning approaches with traditional control theory in recent years. The majority of his work centers around hyperbolic PDE systems, with applications primarily in drilling engineering and fluid dynamics. His research demonstrates a strong connection between theoretical control developments and practical implementations in the petroleum industry. Professor Aamo has actively supervised multiple graduate students, as evidenced by the master's theses listed in his publication record. His work often appears in top-tier control journals including IEEE Transactions on Automatic Control, Automatica, and IEEE Control Systems Letters, as well as petroleum engineering venues like SPE Journal and ASME publications.

• 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.

• Control Systems
• Robotics
• Marine Robotics
• +۳ مورد دیگر

Kristin Y. Pettersen is a Professor at the Department of Technical Cybernetics, Norwegian University of Science and Technology (NTNU), and a Professor II at the Norwegian Defence Research Institute (FFI). She is a co-founder of Eelume AS, a company specializing in underwater robotics solutions. Education: Civil Engineering and PhD in Technical Cybernetics from NTNU Her research focuses on advanced control systems for marine and underwater vehicles, particularly snake robots and autonomous underwater vehicles (AUVs). Key areas include formation control, path following, adaptive guidance algorithms, and safety-critical control in dynamic environments. Recent work explores machine learning integration and energy-shaping techniques for robust locomotion. Publications highlight trends in Model Predictive Control (MPC) , Collision Avoidance , and Task-Priority Operational Space Control for redundant and underactuated systems. Her work bridges theoretical control theory with practical applications in marine robotics, including autonomous inspections and cooperative transport. Labs/Teams: Collaborates with NTNU's Faculty of Information Technology and Electrical Engineering and co-founded Eelume AS, advancing subsea robotic manipulation technologies.

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.

Erlend Lavikمشاهده پروفایل

استاد

• Television Studies
• Film History
• Film and TV Analysis
• +۳ مورد دیگر

Erlend Lavik is Professor at the Department of Information Science and Media Studies, University of Bergen, Norway. Specialising in television studies, film history, and digital criticism, he leads research on cinematic television, narrative complexity, and the scholarly video essay. He is a key figure in the study of HBO’s The Wire , which he frames as emblematic of the medium’s “third golden age.” Education 2008 – PhD, Changing Narratives. Five Essays on Hollywood History , University of Bergen 2001 – MA (hovedoppgave), Kjærlighet i seriemonogamiets tidsalder. Romantiske komedier i Hollywood , University of Bergen Research Interests Lavik’s work interrogates the intersection of aesthetics, narrative theory, and media policy. Core themes include: Serial television and long-form storytelling Film and television authorship (showrunner model, copyright aesthetics) Digital criticism and the video essay as scholarly genre Hollywood historiography from classical to post-classical cinema Documentary ethics and truth claims in factual media Publication Trends Across more than 60 publications, Lavik moves fluidly between peer-reviewed journals, academic monographs, trade-press essays, and experimental video essays. Recurrent case studies include The Wire , The Sopranos , Succession , and blockbuster cinema, examined through lenses of narrative complexity, intertextuality, and cultural memory. Selected Public Engagement & Service Chairman of the jury, Best Norwegian Documentary, Bergen International Film Festival 2012 Keynote speaker, Nordic Media Conference, Akureiri 2011; NTNU seminar on video essays 2012 Public lectures at NRK Drama, University of Tromsø, Bergen Public Library, and numerous film clubs Labs, Teams, Collaborations Lavik is affiliated with the research group Film, Television, and Visual Culture at the University of Bergen, where he collaborates on projects concerning production studies, visual aesthetics, and digital distribution. While specific grant numbers are not listed, his sustained output and conference presence indicate ongoing funded research within Norwegian and European media-studies networks.

Jo Herstadمشاهده پروفایل

دانشیار

• Human-Computer Interaction (HCI)
• Universal Design
• Inclusive Technology
• +۴ مورد دیگر

Jo Herstad is an Associate Professor in the Department of Information Systems Design at the University of Oslo , where she has worked since 2002. With a background in industry at Ericsson (1990-2003), Herstad bridges technical expertise with academic rigor. She teaches courses including: IN1030 - Introduction to Design, Use, Interaction (since 2017) IN2000 - Software Engineering with Project Work IN5480 - Interaction with AI and Autonomous Systems Research Focus: Her work explores Human-Computer Interaction through: Universal Design principles in digital environments Human-Robot Interaction for domestic applications Inclusive Technology across diverse populations Participatory Design methodologies Privacy Dynamics in smart homes Multimodal Systems for elderly care Key Publications (2017-2024) examine motion profiling in robotics, equitable digital feedback, and socio-technical systems. Her supervision includes 15+ master's theses on topics ranging from chatbot ethics to universal design in education. Current projects focus on: Artificial Intelligence and Sensor Technology Multimodal Elderly Care Systems (MECS) Podcast Accessibility (PUD) RHYME: Music Technology for All

Daniel Romeroمشاهده پروفایل

استاد

• UAV Communications
• Wireless Communications
• Time-Series Analysis
• +۵ مورد دیگر

**Daniel Romero** is a **Professor** in the **Department of Information and Communication Technology** at the **University of Agder**, Norway. His research focuses on UAV communications, time-series analysis using machine learning and network science, and decentralized processing for sensor networks. He holds a Ph.D. in Signal Theory and Communications from the University of Vigo (2015), an M.Sc. in Signal Theory (2011), and a Telecommunication Engineering degree (2009). **Education**: Ph.D. in Signal Theory and Communications, University of Vigo (2015) M.Sc. in Signal Theory and Communications, University of Vigo (2011) Telecommunication Engineering, University of Vigo (2009) **Research Interests**: His work spans UAV communication systems (focusing on low-latency, high-reliability networks), time-series analysis for complex systems (using ML and network science), and decentralized computation in sensor networks to improve robustness and hardware efficiency. Recent projects include radio map estimation for mmWave beam alignment, spoofing detection via graph neural networks, and aerial base station placement optimization. **Publications**: Over 30+ peer-reviewed articles in top venues like IEEE Transactions on Wireless Communications and ICC. Recent trends emphasize radio map estimation (2023–2024), UAV-enabled spectrum surveying (2022), and robust D2D communications (2022). **Advising & Grants**: Teaches PhD courses (Statistical Signal Processing, Advanced Optimization) and leads the **Advanced Signal Processing Lab (ASL)**. Collaborates with the **CIEM (Center for Integrated Emergency Management)** on crisis-related communication systems. **Labs/Teams**: Directs the Advanced Signal Processing Lab (ASL.uia.no) and contributes to CIEM, applying ML and signal processing to emergency management challenges.

Tor Skeieمشاهده پروفایل

استاد

• High-Performance Networking
• Distributed Systems
• InfiniBand Technology
• +۲ مورد دیگر

Tor Skeie is a Professor at the University of Oslo's Department of Informatics, specializing in networks and distributed systems. His research focuses on high-performance networking, InfiniBand technologies, and adaptive routing systems. Current investigations include automated parameter tuning for reservoir simulations, adaptive routing in InfiniBand hardware, and modeling WiFi quality attenuation. His work develops efficient solutions for virtualized HPC environments and cloud computing infrastructures. Recent publications demonstrate innovations in network modeling, adaptive routing algorithms, and performance analysis of distributed systems. Research collaborations span European projects on high-performance networking infrastructures. Leads the Networks and Distributed Systems (ND) research group investigating fault-tolerant routing, network virtualization, and congestion control mechanisms.