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Mohammad PoursinaView profile

Associate Professor

• Dynamic Modeling and Simulation of Multibody Systems
• Robotics and Robotic Rehabilitation
• Artificial Intelligence and Control Systems
• +4 more

Mohammad Poursina is an Associate Professor at the Department of Engineering Sciences , University of Agder, Norway. His research spans multibody dynamics , robotics , and celestial mechanics , with a focus on dynamic modeling, control systems, and biomechatronics. He has held prior academic roles at the University of Arizona and Rensselaer Polytechnic Institute. Ph.D. in Mechanical Engineering, Rensselaer Polytechnic Institute (2011) M.Sc. and B.Sc. in Mechanical Engineering, University of Tehran (2006, 2003) His work addresses complex systems through interdisciplinary approaches, including robotic rehabilitation , flexible manipulators , and molecular dynamics . Recent publications highlight advancements in trajectory optimization , VR/AR surgical training , and asteroid system modeling . He serves on the editorial boards of Multibody System Dynamics and Journal of Computational and Nonlinear Dynamics , and has organized ASME conference sessions. Key research groups: Artificial Intelligence, Biomechatronics, and Collaborative Robotics Projects: Reality-Connected Machine Simulation of Heavy Machinery (RealSim)

Victor GreiffView profile

Professor

• Computational Immunology
• Adaptive Immune Receptor Repertoires
• Machine Learning in Antibody Design
• +5 more

Victor Greiff is a Professor at the University of Oslo's Institute of Clinical Medicine , leading the Computational and Systems Immunology group. His research focuses on integrating high-throughput single-cell sequencing , artificial intelligence , and high-dimensional statistics to advance therapeutic antibody design, diagnostics, and vaccine development for infectious diseases, autoimmunity, and cancer. Key Research Areas : Computational immunology, adaptive immune receptor repertoires, machine learning in antibody discovery, bioinformatics, structural biology, and immune diagnostics. Funding : European Research Council (ERC) Grant AB-AG-INTERACT (Project ID: 101125630). Recent Publications highlight interdisciplinary work in: Machine learning for antibody design and immune repertoire classification . Structural analysis of antibody-antigen interactions and evolutionary immunogenetics . Development of bioinformatics tools (e.g., ExpoSeq, TCRpower, CompAIRR) for immune sequencing data analysis. Scientific Contributions include: Establishing computational frameworks for synthetic immune repertoire generation. Exploring genetic constraints on TCR and BCR diversity. Advancing AI-driven antibody engineering for improved therapeutic developability.

Hans Kristian Strandstuen RustadView profile

Professor

• Poetry
• Posthumanism
• Literature across media
• +4 more

Hans Kristian Strandstuen Rustad is a Professor of Nordic Literature at the Department of Nordic and Classical Languages (ILN), University of Oslo. His research spans poetry studies, posthumanism, intermediality, digital literature, media ecology, and Nordic modernist poetry. He explores poetry as performative events, literature's relationship with non-human entities, and cross-media artistic constellations. Editor of Nordisk poesi journal Board member of INSL (International Network for the Study of Lyric) Co-coordinator of NORLYR (Nordic Modernist Lyric Network) His work interrogates how poetry manifests across analog/digital platforms, the ethical implications of posthumanism in literature, and the redefinition of traditional literary concepts in media ecology. Recent projects include studies of Instagram poetry, AI's impact on creativity, and lyrical responses to ecological crises. Rustad's publications from 2024–2017 examine Nordic poetry's evolution through digital platforms, intermedial experiments, and posthuman ethics. His edited volumes and journal articles address themes like audio-literary performance, ecological community in Björk's work, and political rap lyrics. He contributes to institutions such as CERCOP (Center for Contemporary Poetry), NORLYR, and INSL, and teaches courses on Nordic literary history, intermedial literature, and posthumanism. His research bridges cultural, technological, and environmental intersections in poetry.

Eli Renate GrünerView profile

Professor

• Medical Physics
• Neuroimaging
• Biomedical Engineering
• +1 more

Eli Renate Grüner is a Professor at the University of Bergen's Department of Physics and Technology, with joint affiliations at Haukeland University Hospital. Her research focuses on advancing medical imaging techniques, particularly MRI-based methods for studying brain function, perfusion dynamics, and neurodegenerative disorders. She collaborates extensively with clinical researchers to translate imaging innovations into diagnostic applications. Grüner's interdisciplinary work bridges physics, neuroscience, and clinical medicine, developing analytical tools to quantify cerebral blood flow, neurotransmitter systems, and metabolic processes. Her neuroimaging research examines brain network dynamics, alcohol's effects on cognition, and pathological mechanisms in conditions like dementia and Tourette syndrome. Her publications demonstrate expertise in perfusion imaging algorithms, fMRI analysis, and spectroscopic methods. Recent work includes developing visualization tools for spectroscopy data and investigating network switching during cognitive processing.

• Artificial Intelligence
• Neural Networks
• Cellular Automata
• +3 more

Stefano Nichele is a Professor at Oslo Metropolitan University's Faculty of Technology, Art and Design, where he leads research in the Department of Computer Science with a focus on Artificial Intelligence. His laboratory investigates the intersection of biological and artificial intelligence through neural networks and cellular automata. Research Focus: Nichele's work bridges computational neuroscience, complex systems, and machine learning. Key themes include: Developing AI tools for neuroscience applications like dementia prediction Modeling neural network dynamics using biological and computational approaches Evolutionary algorithms and quantum computing hybrids Cellular automata frameworks for emergent intelligence Research Projects: AI-Mind: Developing AI-based dementia diagnostic tools FeLT: Human-machine-environment interactions in ecological contexts DeepCA: Biological-artificial intelligence integration SOCRATES: Efficient distributed data analysis Publications: His recent works (2024-2025) primarily explore neural network dynamics, cellular automata applications, and computational neuroscience models. The research demonstrates consistent focus on emergent behavior in complex systems and biological computation.

• Systems Biology
• Metabolic Modeling
• NAD Metabolism
• +7 more

Ines Heiland is a Professor at the Department of Biomedicine, Faculty of Medicine, University of Bergen. Her research integrates systems biology and computational modeling to investigate metabolic networks, with a focus on NAD and tryptophan metabolism, mitochondrial function, and circadian regulation. University: University of Bergen School: Faculty of Medicine Department: Department of Biomedicine Position: Professor Email: i.heiland@uib.no Her educational background is not explicitly detailed in the provided texts, but her extensive research and supervisory roles indicate a strong academic foundation in systems biology and molecular metabolism. Ines Heiland's research centers on understanding how metabolic networks regulate cellular health and disease. She employs computational models to study NAD homeostasis, tryptophan catabolism, and their roles in aging, cancer, and neurodegenerative conditions. Her work emphasizes the integration of multi-omics data and kinetic modeling to uncover regulatory mechanisms in metabolic pathways. The recent articles highlight a consistent focus on NAD metabolism, metabolic modeling, and the interplay between metabolism and epigenetics. Themes include the evolution of metabolic pathways, subcellular compartmentation of metabolites, and the development of bioinformatics tools for metabolic analysis. Her publications span from algorithm development (e.g., GEMCAT) to physiological insights in aging and disease. She has contributed to editorial and review work, such as the 2020 editorial on systems biology challenges and the 2015 review on NAD metabolism dynamics, underscoring her leadership in the field. Supervised PhD student: Mathias Bockwoldt Ines Heiland has been involved in collaborative research projects, often in partnership with institutions like UiT The Arctic University of Norway. She participates in networks such as NORBIS and BioCat, contributing to interdisciplinary efforts in biotechnology and systems biology. Her work involves both theoretical modeling and experimental validation, particularly in the context of marine biomass conversion and Arctic bioplastics.

Sidney Pontes-FilhoView profile

Research Fellow

• Complex Systems
• Unconventional Computing
• Computational Neuroscience
• +3 more

Sidney Pontes-Filho is a Postdoctoral Fellow at Simula Research Laboratory in Oslo, Norway, with a strong academic affiliation to the Norwegian University of Science and Technology (NTNU) where he completed his PhD in Computer Science. His primary departmental affiliation is with the Department of Computer Science at Oslo Metropolitan University (OsloMet). His research spans multiple institutions including Simula Research Laboratory where he works in the Numerical Analysis and Scientific Computing department. His educational background includes a B.Sc. in Computer Science from Federal University of Paraíba, Brazil (2013), an M.Sc. in Computer Science from Technical University of Kaiserslautern, Germany (2018), and a Ph.D. in Computer Science from NTNU. His research focuses on the intersection of complex systems, unconventional computing, computational neuroscience, and artificial general intelligence, with particular emphasis on neural cellular automata and criticality phenomena. Pontes-Filho's publication record reveals a consistent trajectory in exploring how critical systems can serve as substrates for intelligent behavior. His work demonstrates how neural cellular automata operating near critical points exhibit emergent properties relevant to artificial intelligence, including attention mechanisms, scalability, and robust control systems. His research spans theoretical frameworks like EvoDynamic, practical applications in soft robotics control, and medical imaging tools for brain extraction from fMRI data. His scientific contributions show an interdisciplinary approach merging concepts from statistical physics, neuroscience, and computer science to develop novel computational paradigms. The evolution of his work demonstrates increasing sophistication in connecting theoretical criticality concepts with practical AI applications, particularly in embodied systems and neuromorphic computing.

• Matrix and Tensor Factorizations
• Data Fusion
• Machine Learning
• +3 more

Carla Schenker is a Postdoctoral Fellow in the Department of Data Science and Knowledge Discovery at Simula Metropolitan, specializing in advanced tensor decomposition methods for multi-modal data analysis. Her research bridges machine learning, optimization, and neuroimaging applications, with a focus on interpretable pattern discovery from complex datasets. Her educational background includes: PhD from Oslo Metropolitan University, Norway (Thesis: A Flexible Framework for Data Fusion Based on Coupled Matrix and Tensor Factorizations for Interpretable Pattern Discovery) Dr. Schenker's research centers on Matrix and Tensor Factorizations , where she develops constrained optimization frameworks for PARAFAC2 and coupled decompositions. Her work enables Data Fusion across dynamic and static sources, with critical applications in neuroimaging biomarker discovery and temporal pattern tracking . She pioneers methods for handling incomplete temporal data while maintaining model interpretability, advancing both theoretical foundations and real-world implementations in multi-way data analysis. Analysis of her 11 publications (2019-2025) reveals a clear evolution: early work established optimization frameworks for regularized tensor factorizations (2019-2021), while recent breakthroughs (2023-2025) focus on temporal dynamics, interpretable evolving patterns, and constrained PARAFAC2 variants. Her research consistently bridges Machine Learning theory with applications in neuroscience and signal processing, demonstrating increasing sophistication in handling heterogeneous, multi-modal datasets. No scientific awards are documented in available sources. Public records indicate no formal student advising or grant leadership, though her collaborative publications involve significant interdisciplinary partnerships with institutions like Oslo Metropolitan University and international research teams. As a core member of Simula Metropolitan's Data Science and Knowledge Discovery department, she contributes to Norway's national research infrastructure for digital engineering, working within teams focused on algorithmic innovation for complex data challenges in healthcare and industrial applications.

Eeva MauringView profile

Associate Professor

• Microeconomic theory
• Industrial organisation
• Information economics

Eeva Mauring is an Associate Professor at the Department of Economics, University of Bergen, and affiliated with the University of Vienna and the Centre for Economic Policy Research (CEPR). She holds a PhD from University College London (UCL) awarded in 2016. Her research focuses on microeconomic theory, industrial organisation, and information economics, with particular emphasis on search theory, market dynamics, and platform economics. Her work explores topics such as price discrimination in online markets, competitive search equilibria, and the role of information in shaping market outcomes. Notable contributions include analyzing platform learning from consumer choices, Coasian dynamics in sequential search, and the social value of public information in search markets. She has also studied adverse selection in dynamic matching markets and evolutionary stable pricing strategies. Eeva's research bridges theoretical frameworks with practical applications in digital markets, consumer behavior, and market design. Her affiliations with leading institutions reflect her international standing in the field. No awards are explicitly listed, but her extensive publication record underscores her scholarly impact. She has not listed advisees or grants in the provided materials.

• Unconventional Computing
• Computer Architecture
• Artificial Intelligence
• +5 more

Gunnar Tufte is a Professor and Deputy Head of Research at the Department of Computer Technology and Informatics, NTNU. He leads the Computer Architecture Lab and advises PhD programs in Computer Science and Informatics within the Faculty of Information Technology and Electrical Engineering. His primary research focuses on unconventional computing, including artificial spin ice systems, self-organizing nanomaterials, and bio-inspired architectures. Research Interests include: Unconventional Machines: Architecture, Design, and Computation Spin-based Computing and Energy Efficient Systems Evolution-in-Materio and Nanoscale Computation Reservoir Computing and Dynamical Systems Nano-Magnetic Orchestration and Self-Organization Key Projects: SpinENGINE (EU Horizon 2020 FET-Open): Developing parallel computing platforms using nanomagnet ensembles. SPrINTER (Norwegian Research Council): Low-power spin-based computing technology. flatspin: Open-source simulator for artificial spin ice systems (GPLv3). Grants and Collaborations: Horizon 2020 FET-Open funding for SpinENGINE. NFR funding for SPrINTER project. Lab/Team: Maintains the Computer Architecture Lab and collaborates on interdisciplinary projects with researchers in nanotechnology, AI, and biology.

Johan PensarView profile

Associate Professor

• Statistical Machine Learning
• Probabilistic Graphical Models
• Causal Learning
• +3 more

Johan Pensar is an Associate Professor of Statistics and Data Science at the University of Oslo's Department of Mathematics. He holds a PhD from Åbo Akademi University (2016) and was a postdoc at the University of Helsinki (2016–2020). His research focuses on statistical machine learning, probabilistic graphical models, causal inference, and applications in genomics. He has supervised multiple PhD students and co-supervised others in interdisciplinary projects, including causal modeling in healthcare and machine learning for microbiology. Education: PhD in Statistics, Åbo Akademi University, 2016 Postdoctoral Researcher, University of Helsinki, 2016–2020 Research Interests: Pensar's work integrates statistical theory with practical applications. Key areas include developing methods for causal discovery, probabilistic graphical models (e.g., Bayesian networks), and their use in genomics and healthcare. He emphasizes interpretable machine learning and robust statistical frameworks for complex data. Publications: Recent work spans causal inference, microbial genome analysis, and housing market prediction. His methods address challenges like confounding bias, generalization in ML, and uncertainty quantification in valuation models. Awards: Finnish Statistical Society Doctoral Thesis Award (2013–2016) Teaching & Advising: Pensar teaches advanced courses in statistical learning and probabilistic graphical models. He advises PhD students on causal modeling, ML in healthcare, and data science applications. He collaborates with industry partners like Integreat and Eiendomsverdi AS. Lab/Teams: He is affiliated with the Norwegian Centre for Knowledge-driven Machine Learning (Integreat) and leads research on Bayesian methods in ML.

Mats Erling HøvinView profile

Associate Professor

• Generative Design
• CAD
• CAM
• +6 more

Mats Erling Høvin is an Associate Professor at the University of Oslo's Department of Informatics, affiliated with the Robotics and Intelligent Systems (ROBIN) research group. His academic work focuses on generative design, CAD/CAM systems, rapid prototyping, and evolutionary robotics. Previously, he conducted significant research in Delta-Sigma noise shaping for analog-to-digital converters. Research Interests: Generative design methodologies for manufacturing Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems Rapid prototyping and additive manufacturing techniques Evolutionary algorithms applied to robotics and optimization problems Delta-Sigma modulation for analog/digital signal conversion Robotic locomotion and control systems His publications demonstrate a consistent focus on optimization techniques applied to robotics, signal processing, and manufacturing systems. Recent work explores physics-based simulations for medical robotics and generative design for structural optimization. Earlier contributions established foundations in evolutionary robotics, motion capture filtering, and analog circuit design. Høvin teaches courses including IN5590 and IN1080, and leads research in the ROBIN group focusing on intelligent systems development. His work bridges theoretical computer science with practical engineering applications in robotics and manufacturing.

• Structural Engineering
• Risk Assessment
• Fire Safety
• +3 more

Naser Kazemi Eilaki is an Assistant Professor at the Department of Civil Engineering and Environmental Sciences, Western Norway University of Applied Sciences. His work focuses on structural reliability, risk assessment, and infrastructure resilience against natural disasters including floods, earthquakes, and fires. Risk Analysis Fire Safety Engineering Water/Wastewater Network Risk Machine Learning Applications Offshore Structure Fatigue Analysis His research integrates advanced optimization algorithms like Harmony Search and Particle Swarm Optimization with artificial neural networks to develop reliability models for civil infrastructure. Key applications include corrosion risk assessment in pipelines, bridge pier scour reduction using gabion obstacles, and passive defense strategies for utility networks. Recent publications highlight trends in hybrid computational methods for structural reliability. These works combine probabilistic modeling with evolutionary algorithms, addressing challenges in seismic risk assessment, fire safety, and water loss prevention through pipeline integrity analysis. As an active academic, he contributes to resilience engineering through interdisciplinary approaches that bridge civil engineering and computational risk analysis frameworks.

Thor-David HalstensenView profile

Assistant Professor

• Clinical Decision Rules
• Artificial Intelligence in Healthcare
• Diagnostic Algorithms
• +3 more

Thor-David Halstensen is an Assistant Professor at the Department of Nursing, Health and laboratory science within the Faculty of Health, Welfare and Organization at Oslo and Akershus University College of Applied Sciences. His academic background includes work as a nurse anesthetist, and he specializes in improving clinical decision rules (CDRs) using artificial intelligence (AI). His research focuses on diagnostic algorithms, healthcare informatics, and medical decision support systems, particularly in emergency and outpatient settings. Key projects include Decision Support in Health Care using Artificial Intelligence (AI) , which aims to enhance diagnostic accuracy through machine learning. He contributes to the Advanced clinical nursing (AKS) research group and teaches courses on clinical decision rules. His work spans clinical trials, patient experience studies, and competency assessment in healthcare.

• Nuclear Energy
• Renewable Energy Systems
• Clean Aviation
• +5 more

Jonas Kristiansen Nøland is a Full Professor of Energy Conversion at NTNU and holds a concurrent position as Professor II at USN. He is a senior member of IEEE and leads research initiatives such as NTNU's Clean Aviation program and the SysOpt hydropower project. His academic journey includes a PhD in Engineering Physics from Uppsala University (Ångström Laboratory) and participation in NTNU's Outstanding Academic Fellows Programme since 2022. Research interests focus on modern nuclear energy (SMRs/4th gen), clean aviation (e-fuels/battery/hydrogen-electric), hyperloop technology, superconductivity, and grid resilience. He serves as an Associate Editor for IEEE Transactions on Energy Conversion, Industrial Electronics, and Transportation Electrification, and chairs the IEEE Power and Energy Society Norway Chapter. His work often bridges academia and industry, addressing global energy challenges via advanced electrical systems, renewable integration, and disruptive transportation technologies. Key contributions include studies on SMR grid roles, battery-electric aircraft feasibility, and hyperloop technical viability. He advises doctoral candidates on topics like superconducting motors and energy systems optimization.