Joakim Sundnes is a Chief Research Scientist and Research Professor at the Department of Scientific Computing, Simula Research Laboratory. He specializes in computational physiology, cardiac biomechanics, and mathematical modeling of cardiovascular systems. Key Research Areas: Cardiac electromechanics, computational fluid dynamics in cardiology, uncertainty quantification in cardiac models, and mechano-electric feedback mechanisms Recent Trends: Focus on patient-specific modeling, left atrial flow dynamics, right ventricular mechanics in pulmonary hypertension, and personalized treatment simulations Scientific Contributions: Active participant in international conferences and editorial work. Co-author of multiple benchmark studies and educational texts on physiological modeling.
Geir Mathisen serves as a Professor within the Department of Technical Cybernetics, Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). He is an active member of the Group for Industrial Computer and Instrumentation Systems, focusing on real-time systems integration and cyber-physical applications across industrial and energy domains. His educational background includes a Civil Engineering degree and a Doctorate (PhD), both earned from NTNU's Department of Technical Cybernetics, establishing foundational expertise in control systems and technical cybernetics. Professor Mathisen's research spans cyber-physical systems, deterministic networking, and distributed real-time systems with significant applications in smart grids and industrial automation. His work pioneers magnetic field energy harvesting for railway systems, edge-based fault detection for photovoltaic panels, and multi-robot coordination in sewing automation. Current investigations focus on power system state estimation, optimal power flow in smart grids, and deterministic communication channels for latency-sensitive applications. Analysis of his 2020-2024 publications reveals a strategic convergence of real-time computing with energy systems, particularly in railway energy harvesting and photovoltaic monitoring. His research consistently bridges theoretical advances in networking protocols with practical industrial implementations, emphasizing determinism and composability in distributed cyber-physical environments. Scientific Awards: No specific awards or fellowships were documented in the provided materials. Professor Mathisen actively supervises doctoral and master's students, including Johannes Schrimpf (2013 PhD thesis on industrial robot control), and offers project assignments as noted for fall 2021. His research is conducted through Norwegian collaborative projects on flexible distribution grids and smart grid services, though specific grant mechanisms remain unspecified in the source material. He contributes significantly to the Group for Industrial Computer and Instrumentation Systems at NTNU, which develops advanced solutions for industrial control, measurement systems, and cyber-physical integration, particularly in energy and manufacturing contexts.
Kjell Gunnar Robbersmyr is a Professor at the University of Agder's Department of Engineering Sciences and director of the Top Research Center in Mechatronics. With a Ph.D. in mechanical engineering from NTNU (1992), his career spans academic leadership, research management at Agder Research, and active contributions to IEEE. His work focuses on mechatronics, machine design, and condition monitoring, with special emphasis on fault diagnosis in electric motors and vehicle crash modeling. Senior Member of IEEE Member of Norwegian Academy of Technical Sciences Member of Agder Academy of Sciences Research interests include: Advanced fault diagnosis in electric drives using AI and signal processing Vehicle crashworthiness modeling with lumped parameter and finite element methods Optical measurement technology for machine monitoring Digital twin applications for infrastructure and wind energy systems Condition monitoring of low-speed bearings and rotating machinery Recent publications demonstrate expertise in: Deep learning for imbalanced motor fault datasets Transformer networks in power electronics diagnostics 3D reconstruction techniques for mechanical systems Multi-classifier decision fusion in power systems Dynamic operations modeling for electric vehicles Scientific contributions include: Over 50 peer-reviewed articles Leadership in the Intelligent Monitoring research group Development of novel inverter topologies Innovations in wind turbine condition monitoring Advancements in laser-based mechanical diagnostics
Amirhosein Taherkordi is an Associate Professor at the Department of Information Security and Communication Technology within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). His academic profile shows continuous research activity with publications spanning from 2011 through 2025, indicating an established career trajectory in computer science and networking research. Dr. Taherkordi's research interests focus on addressing fundamental challenges in distributed computing environments, particularly in resource-constrained scenarios. His work spans Internet of Things (IoT) systems, edge and fog computing architectures, network security protocols, and machine learning applications for network traffic analysis. He has made significant contributions to energy-efficient data collection protocols for wireless sensor networks, privacy-preserving techniques for industrial IoT systems, and communication-efficient approaches for federated learning in vehicular networks. His research consistently bridges theoretical innovation with practical implementation, addressing real-world challenges in smart transportation, environmental monitoring, and industrial automation systems. An analysis of Dr. Taherkordi's recent publication trends (2023-2025) reveals a strong emphasis on federated learning applications for vehicular networks (FedAGL, FedAPT), energy-efficient IoT data collection strategies (eU2U, ECMSH), and the integration of transfer learning with edge computing for transportation applications (TELEGAIT, FOGFLEET). His work increasingly addresses the critical tension between computational efficiency and accuracy in distributed systems, with growing applications in environmental monitoring (PmForecast) and circular economy frameworks. The interdisciplinary nature of his research spans computer science, electrical engineering, and environmental science domains. Dr. Taherkordi maintains an active collaborative research profile, working with international colleagues across multiple institutions as evidenced by his diverse publication venues including IEEE Transactions, ACM journals, and various conference proceedings. His research program appears to be well-established with consistent funding, though specific grant details aren't provided in the available text. He likely leads or contributes significantly to research groups focused on networking, IoT, and edge computing at NTNU, mentoring students in these emerging technology domains.
Richard Bischof is a Professor at the Norwegian University of Life Sciences (NMBU), affiliated with the Ecology and Natural Resource Management Section. His research focuses on wildlife population dynamics, spatial ecology, and conservation biology, particularly in large carnivores and boreal/tropical ecosystems. He specializes in advanced methodologies like spatial capture-recapture modeling and noninvasive genetic sampling to estimate population parameters and inform conservation strategies. Key research interests include understanding the effects of environmental variability, human activities, and management practices on species distribution and population trends. His work often addresses challenges in monitoring elusive species such as bears, wolves, and wolverines, emphasizing the integration of genetic data and spatial analysis to improve conservation outcomes. Recent studies highlight his contributions to quantifying population dynamics of Scandinavian large carnivores, evaluating the impacts of wind energy development on forest biodiversity, and analyzing behavioral adaptations of tropical mammals to anthropogenic pressures. Bischof’s interdisciplinary approach bridges ecological theory with applied conservation, addressing pressing issues like transboundary wildlife management and the mitigation of human-wildlife conflicts.
Fengying Dang is an Assistant Professor in the Department of Electrical and Computer Engineering at Michigan Technological University. Her research focuses on enhancing robotic intelligence through perception, sensor data utilization, and intelligent control systems for autonomy in complex environments. Education: PhD in Electrical and Computer Engineering (2021) from George Mason University, B.S. in Detection, Navigation & Control (2015) from Northwestern Polytechnical University. Research Interests : Designing robust perception/localization systems using vision and non-vision sensors, learning-based control algorithms for robotics, and applications in agricultural automation (e.g., weed detection in cotton systems) and bio-inspired underwater vehicles. Publication Trends : Key topics include autonomous driving , weed detection , flow sensing for AUVs , and deep learning in robotics . Her work combines sensor fusion , model reduction techniques , and control algorithms . Laboratory : Leads research in autonomous systems and robotics, mentoring doctoral and master's students. Alumni include MS EE graduate Benjamin Wittrup (2025), now employed at Treetown Tech LLC.
Juha-Pekka Vierinen is a Professor of Space Physics in the Department of Physics and Technology at UiT The Arctic University of Norway. His research focuses on experimental space physics with emphasis on radar and radio remote sensing techniques applied to space plasma physics, atmospheric physics, space debris, and planetary science. His research interests include: Development of new techniques for radar and radio remote sensing Space plasma physics and ionospheric studies Atmospheric physics and dynamics in the mesosphere and lower thermosphere Space debris characterization and monitoring Planetary science applications of radio techniques GNSS scintillation studies in polar regions Professor Vierinen's recent publications demonstrate expertise in utilizing advanced radar systems like EISCAT_3D, ionosonde networks, meteor radar networks, and global navigation satellite networks. His work spans from fundamental plasma physics to practical applications in space weather monitoring. A notable recent publication in Nature titled "Space weather mapped by millions of smartphones" highlights innovative approaches to space weather monitoring using distributed sensing technology. His research on interferometric imaging with EISCAT_3D represents cutting-edge developments in ionospheric plasma parameter determination. His scientific contributions include: Development of interferometric imaging techniques for EISCAT_3D radar Studies of ionospheric irregularities and their effects on GNSS signals Analysis of atmospheric responses to solar eclipses Investigation of turbulence in the mesosphere and lower thermosphere Characterization of space debris using radar techniques Professor Vierinen is involved in several research projects including UNICube, QBDebris (A CubeSat formation for space debris characterisation), and CASCADE. He is a member of the Space Physics research group at UiT and collaborates extensively with researchers worldwide.
Dr. Lin Li is an Associate Professor in Marine/Ocean Technology at the Faculty of Science and Technology , University of Stavanger . With a PhD from NTNU and degrees from Shanghai Jiao Tong University , she leads research in marine structural dynamics, aquaculture-hydrodynamics, and offshore wind integration. PhD Marine Technology (NTNU) MSc Design and Construction of Ships/Ocean Structures (Shanghai Jiao Tong University) BSc Naval Architecture and Ocean Engineering (Shanghai Jiao Tong University) Her research focuses on: Dynamic analysis of marine structures Design of aquaculture systems Hydrodynamic modeling for offshore wind Statistical wave analysis Recent publications highlight advancements in: Hybrid offshore fish cage-wind turbine systems Metocean condition modeling Subsea spool deployment methods Extreme response prediction techniques She actively contributes to international marine technology conferences and applies open-source tools for hydrodynamic validation.
Thais Mothe-Diniz is a Researcher and Adviser at the Faculty of Natural Sciences, NTNU, holding roles such as Research Adviser in the NV Faculty Administration and Admin Coordinator for the FME-Hydrogeni Project. She specializes in asteroid mineralogy and surface composition, with expertise in EU project management. Her research focuses on asteroid family dynamics, spectroscopic analysis, and planetary science. PhD in Astronomy (2002, mineralogy of small planets) MSc in Astrophysics (1998, Celestial Mechanics) Bachelor in Pure Mathematics (1994, Maxwell's equations) Research interests include asteroid mineralogy, space weathering processes, and the structural analysis of asteroid families. She has extensive experience in coordinating international projects like REVITALISE and SisAl Pilot, and manages NTNU's collaborations with institutions like Jotun and the Paris Observatory. Publications span asteroid taxonomy, volatile detection (e.g., ice on 24 Themis), and compositional studies of basaltic asteroids in the outer main belt. Her work bridges observational astronomy and planetary formation theories, with contributions to missions like NASA's Dawn targeting Ceres and Vesta. Notable competencies include certified conflict mediation (Red Cross), project leadership, and technical skills in Linux/macOS systems.
Rolands Cepuritis is an Associate Professor at the Department of Structural Engineering , Norwegian University of Science and Technology (NTNU) . With over 20 years of international experience in cement production, concrete technology, and academia, his research focuses on sustainable concrete materials and advanced rheological modeling. Research Highlights: Leading MiKS (Microproportioning with Crushed Sand) project to optimize crushed sand in concrete Coordinating COIN (High Quality Manufactured Sand) initiatives Developing the FlowCyl one-parameter rheology test for cement paste Investigating calcium sulfoaluminate binders for carbonation resistance Advancing steel fiber-reinforced concrete for thin overlays Publication Trends: Recent work emphasizes manufactured sand optimization, rheological modeling with neural networks, acid resistance of novel binders, and sustainable tunnel lining solutions via SUPERCON project. Contact: Email: rolands.cepuritis@ntnu.no Office: Materialteknisk, 3-202, Gløshaugen, Richard Birkelands vei 1a, Norway
Raquel Barreira is an Associate Professor at the School of Technology of Setúbal , Polytechnic Institute of Setúbal , and a Researcher at the Center for Mathematical Studies , University of Lisbon . She holds a PhD in Mathematics (2009) from the University of Sussex , a Master's in Functional Analysis and Differential Equations (2004), and a BSc in Mathematics (2000), both from the University of Lisbon . Her research interests span Applied Mathematics and Data Science , with a focus on: Reaction-diffusion and cross-diffusion systems for pattern formation and biological modeling Mathematical biology in predator-prey dynamics and agricultural ecosystems Time series analysis for water demand forecasting and urban water management Computational finance and risk modeling for banking systems The 15 most recent publications reflect her expertise in cross-diffusion dynamics, water infrastructure analytics, and bioinformatics applications. These works appear in journals like International Journal of Bifurcation and Chaos , Scientific Reports , and Water Resources Research . Her projects E³UDRES² and OLIVESIM highlight collaborations in European smart regions and olive grove ecosystem services. She has supervised numerous Bioinformatics and Civil Engineering students and received three innovation awards , including two regional first prizes and one national third prize in the Poliempreende business project competition.
Michael Kirkedal Thomsen is an Associate Professor in the Department of Informatics at the University of Oslo's Faculty of Mathematics and Natural Sciences. His office is located in room 10461 at Gaustadalléen 23B, 0373 Oslo, with postal address PO Box 1080 Blindern 0316 Oslo. His research focuses on programming languages and security, with particular expertise in reversible computing and programming language theory. Thomsen's research spans multiple interconnected domains within computer science. His primary focus is on reversible computing - investigating how to design programming languages, compilers, and hardware that minimize energy consumption through reversible operations. He has developed Jeopardy, an invertible functional programming language, and has made significant contributions to reversible circuit design, reversible arithmetic operations, and energy-efficient computation. His work bridges theoretical computer science with practical hardware implementation concerns, particularly examining how reversible programs behave on conventional irreversible hardware. An analysis of his recent publications reveals a clear evolution in his research trajectory. Starting with foundational work on reversible circuits and arithmetic operations (2008-2014), he has progressed toward higher-level programming language constructs for reversible computation. His 2022-2024 publications demonstrate a shift toward practical applications, educational tools, and energy analysis of reversible systems. The recurring themes across his work include invertibility, energy efficiency, formal methods, and the relationship between high-level programming abstractions and low-level hardware implementation. While specific grant information isn't detailed in the available text, Thomsen's research program clearly involves significant collaboration with colleagues at the University of Oslo and international partners. His publications show consistent work with researchers including Holger Bock Axelsen, Robert Glück, Joachim Tilsted Kristensen, and Robin Kaarsgaard across multiple years, suggesting ongoing collaborative projects and likely sustained funding support. Though not explicitly stated in the available information, Thomsen's work on reversible processor architecture, Jeopardy programming language, and energy analysis strongly suggests involvement with research groups focused on energy-efficient computing, programming language design, and potentially quantum-inspired computing architectures at the University of Oslo's Department of Informatics.
Jörn Schulz is an Associate Professor in the Department of Mathematics and Physics at the University of Stavanger, affiliated with the Faculty of Science and Technology. His research focuses on statistical shape analysis, medical imaging, and computational statistics with applications in neuroscience and biostatistics. Recent work includes studies on shape representation in medical imaging, rotational deformation analysis, and clinical studies related to Parkinson's disease and neonatal resuscitation outcomes. Key research areas include statistical methods for geometric data, non-Euclidean analysis, and interdisciplinary applications in healthcare. His publications span journals like Journal of Computational and Graphical Statistics, Medical Image Analysis, and Neurology. Notable contributions include developing shape analysis techniques for medical objects and analyzing clinical data from Parkinson's disease cohorts. Publications trends show strong focus on combining statistical theory with medical applications, particularly in imaging and patient outcomes. He has collaborated on projects involving newborn resuscitation protocols, elderly transitional care safety, and sleep pattern analysis in aging populations. His work frequently integrates advanced statistical modeling with real-world clinical challenges. Academic activities include presentations at conferences such as DAGStat and ERCIM, and involvement in projects like the Safer Births initiative. Despite not explicitly listing awards, his extensive publication record reflects significant contributions to statistical methodology and medical data analysis.
Arash Soleiman Fallah is a Professor at the Department of Mechanical, Electrical and Chemical Engineering, Faculty of Technology, Art and Design, Oslo Metropolitan University. He has held academic positions at Brunel University London (Associate Professor in Dynamics and Vibration) and Imperial College London (Assistant Professor in Aeronautics). His research focuses on mechanics of composite materials, blast and impact resistance, peridynamics, phononic metamaterials, and multiphysics modeling of wind turbines. Current affiliation: OsloMet Previous roles: Associate Professor (Brunel), Assistant Professor (Imperial) He leads research in composite wind turbine blade mechanics, frequency filtering in metamaterials, and damage modeling in metals and composites. His work includes extended finite element formulations and viscoplastic material modeling. Recent publications explore negative-stiffness composites, laser thermal forming, and multiphysics simulations for powder coating. His research spans structural engineering, acoustic metamaterials, and extreme loading conditions. Fellow of Higher Education Academy (HEA) Member of American Society of Mechanical Engineers (ASME) He serves as a Visiting Senior Research Consultant at ICP-ZHAW, contributing to interdisciplinary research in mechanics and mechatronics.
Sindre Markus Fritzner is a researcher at UiT The Arctic University of Norway, affiliated with the Faculty of Science and Technology and the Department of Physics and Technology . His work focuses on sea ice modeling, data assimilation, and machine learning applications for Arctic forecasting, with secondary contributions to fusion plasma physics. Specializes in high-resolution coupled ocean-sea ice models and satellite data integration Developed operational systems like Barents-2.5km v2.0 for Arctic forecasting Contributed to fusion reactor scrape-off layer dynamics studies His research combines traditional dynamical modeling with statistical approaches to improve Arctic operational safety and weather prediction. Recent publications highlight trends in: Arctic ocean-sea ice ensemble prediction Comparative assimilation techniques (MVN vs EnKF) Machine learning integration with climate models Plasma turbulence characterization in fusion reactors Fritzner has no explicitly listed scientific awards or advisees in the provided materials.
