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.
Signe Søvik is a Professor at the Institute of Clinical Medicine, University of Oslo, and a senior consultant anesthesiologist at the Department of Anaesthesia and Intensive Care, Akershus University Hospital, Norway. She is affiliated with the Research Group for Human Integrative Cardiovascular Control (CIRCON) and collaborates with departments of Infectious Diseases, Cardiology, and Emergencies and Critical Care across Oslo and Akershus University Hospitals. Her research focuses on acute respiratory and cardiovascular physiology, emergency medical care evaluation, and statistical modeling of complex medical systems. Key areas include pediatric anesthesia, trauma care, neonatal cardiorespiratory control, and medical simulation for teaching. She is actively involved in clinical and translational research with strong interdisciplinary collaboration. Recent publications reveal a strong trend in human physiological responses under stress, including trauma, hypovolemia, surgical positioning, and environmental exposure. Her work integrates clinical data with advanced statistical and network analysis, particularly in trauma epidemiology, ICU patient flow, and outcomes in critical illness such as COVID-19. She frequently employs cohort studies, systematic reviews, and physiological monitoring in healthy and patient populations. Signe Søvik is deeply engaged in medical education, especially through simulation-based training. She supervises research and contributes to quality improvement in emergency and critical care settings.
Ivar Midtkandal is a Professor at the University of Oslo's Department of Geosciences, specializing in the Section for Study of Sedimentary Basins. His work focuses on sedimentology, tectonostratigraphy, and planetary geology with a particular emphasis on CO2 storage and basin evolution. He is affiliated with research groups such as Basin Studies and CO2 Storage. Research interests include rift basin dynamics, fault seal analysis, and sedimentary architecture. His projects involve Arctic petroleum exploration (ARCEx), CO2 containment (COTEC), and Triassic reservoir prediction (FORCE Triassic). Recent studies explore topics like fault complexities in CO2 storage sites and seismic geomorphology of submarine canyons. Midtkandal's work integrates field data, seismic interpretation, and experimental methods. He leads initiatives like the VISTA Centre for CO2 Storage in Volcanic-Sedimentary Systems (VICCO) and collaborates internationally on projects such as NOR-R-AM2 (volcanism and tectonics). Publications span 20+ years, addressing topics like Paleogene basin development, sedimentation records of glacial cycles, and volcanic influences on basin evolution. His research bridges geology, geophysics, and environmental science, with applications to energy and climate challenges.
Manuela Zucknick is Professor and Director of the Oslo Centre for Biostatistics and Epidemiology at the University of Oslo's Faculty of Medicine, Department of Biostatistics. Her research integrates statistical learning with translational cancer research to advance personalized medicine through multi-omics data integration. PhD Biostatistics, Imperial College London (2008) MSc Bioinformatics, Imperial College London (2004) Diplom Statistik, University of Dortmund (2003) Her research focuses on Bayesian methods for integrating heterogeneous data sources in cancer research, particularly for drug response prediction in pharmacogenomic screens and patient prognosis. She develops structured high-dimensional regression models for 'large p, small n' problems in molecular medicine, with emphasis on incorporating prior biological knowledge into risk prediction frameworks. Her work bridges statistical methodology with clinical applications in personalized cancer therapies. Her recent publications demonstrate consistent contributions to multi-omics integration and survival modeling across diverse clinical contexts including cancer, pregnancy complications, and rheumatoid arthritis. The research shows strong methodological innovation in handling high-dimensional biological data while maintaining clinical relevance. Through the Oslo Centre for Biostatistics and Epidemiology, she leads collaborative projects spanning oncology, obstetrics, and rheumatology. Her work frequently involves designing statistical frameworks for pharmacogenomic screens and developing tools for biomarker discovery in complex disease settings.
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.
Professor Kerstin Bach is affiliated with the Department of Computer Technology and Informatics at NTNU's Faculty of Information Technology and Electrical Engineering. Her research focuses on artificial intelligence, machine learning, and their applications in health informatics and robotics. Notable projects include the selfBACK app for musculoskeletal pain management and reinforcement learning for robotic systems. She has supervised numerous doctoral students in areas like activity recognition and explainable AI. Education: Formal academic degrees not explicitly listed in text. Research interests span: - Case-Based Reasoning for clinical decision support - EHealth/mHealth applications (e.g., wearable sensor analytics) - Explainable AI methodologies - Human activity recognition using accelerometer data Recent work emphasizes: - Machine learning models for health monitoring (sleep/wake detection, gait analysis) - Reinforcement learning for robotics and autonomous systems - AI-driven clinical tools for pain management Key Projects: selfBACK app: Digital self-management tool for musculoskeletal pain (RCT validated) UtiliGEM: Energy management framework for IoT devices Labs/Teams: Active in NTNU's AI research groups focusing on healthcare informatics and robotics. Collaborates with medical institutions on clinical AI applications.
Raoof Gholami is a Professor of Energy Resources at the University of Stavanger's Faculty of Science and Technology, Department of Energy Resources. His research focuses on subsurface energy storage, particularly hydrogen and CO₂ sequestration in geological formations. He has contributed extensively to understanding mechanisms like hydrogen leakage in salt caverns, CO₂-mineral interactions in chalk reservoirs, and bio-reactive transport modeling in storage sites. Key research interests include: (1) Hydrogen storage in porous media, (2) CO₂ sequestration risks and geomechanical stability, (3) Reactive transport modeling, and (4) Machine learning applications in reservoir characterization. His work bridges experimental studies (e.g., salt precipitation dynamics) with numerical modeling (e.g., caprock integrity analysis). Recent publications emphasize hydrogen storage challenges (2025), CO₂ storage in chalk (2023), and leakage risk assessment frameworks (2021). His research also explores novel materials like biodegradable surfactants for drilling fluids (2023) and advanced well path design techniques (2022). Collaborations span institutions globally, addressing topics like shale reservoirs, geothermal energy, and subsurface imaging.
Guoyuan Li is a Professor at the Department of Marine Operations and Engineering Technology at NTNU, affiliated with the Faculty of Engineering. His research focuses on digitalization, control systems, robotics, maritime operations, and human-machine interaction. He holds IEEE Senior Membership (2019) and editorial roles in IEEE Journal of Oceanic Engineering and IEEE Transactions on Intelligent Transportation Systems . Education includes a Ph.D. in Computer Science from Hamburg University (2013), M.S. and B.S. from Chongqing University (2009/2006). Key projects include EU-funded RoboSAPIENS (robotic adaptation), Digital Twin for Green Ship Operations, and TwinShip (vessel lifecycle services). Recent awards include 2024 IEEE Robotics & Automation Magazine Best Paper and multiple IEEE conference recognitions. His work spans AI-driven maritime safety, digital twin applications, and autonomous systems. Visit Intelligent Systems Lab for more.
Natasa Nord is a Professor at the Department of Energy and Process Engineering, Faculty of Engineering, Norwegian University of Science and Technology (NTNU). Her research focuses on energy efficiency, energy supply systems, district heating, and zero-energy building technologies. She leads projects like the CETPartnership, which explores thermal storage in energy systems, and the EU-funded ARV project, aiming to create climate-positive circular communities through building renovation and energy system integration. Her work emphasizes sustainable energy transitions, including the integration of renewable energy sources, thermal energy storage, and data-driven approaches for optimizing district heating systems. She collaborates on projects such as analyzing waste heat utilization from data centers, optimizing heating systems for cruise ships, and modeling energy pathways for university campuses. Her research spans topics like CO₂ heat pump performance, phase change material (PCM) storage, and smart grid integration. Education: Not explicitly stated in the provided text. Research Projects: CETPartnership: Thermal storage potentials in energy systems. ARV (H2020): Building renovation and climate-positive communities. Key Contributions: Advancing district heating optimization. Developing predictive models for energy systems. Exploring CO₂ heat pump and PCM storage applications. Her advising and grants involve guiding students on topics like thermal storage, energy efficiency in buildings, and data center waste heat recovery. She collaborates with institutions like SINTEF and leads interdisciplinary teams for energy system modeling and sustainability planning.
Esther Ulitzsch is an Associate Professor at the University of Oslo 's Centre for Educational Measurement (CEMO) . Her research focuses on advancing psychometric models, particularly Bayesian latent variable techniques for small-sample conditions, and analyzing digital interaction data from simulated learning environments. She holds a PhD from Freie Universität Berlin and previously worked as a Research Associate at the IPN – Leibniz Institute for Science and Mathematics Education in Kiel, Germany. Education: PhD in Educational Measurement (Freie Universität Berlin) Research Associate at IPN Kiel Research Interests: IRT model development for aberrant response detection Efficient estimation in small samples Clickstream analysis for student behavior Test-taking engagement dynamics Publications: Over 20 peer-reviewed articles since 2017, including work on mixture models for careless responding, neural networks for IRT estimation, and Bayesian factor modeling. Recent contributions address response time analysis, cross-country measurement invariance, and sequential process mining in interactive tasks. Affiliations: Active in the CREATE (Research on Equality in Education) and FREMO (Frontier Research in Educational Measurement) groups at CEMO.
Edmund Førland Brekke is a Professor at the Department of Technical Cybernetics, Norwegian University of Science and Technology (NTNU). He leads the Autosit and Autosight projects, and serves as a work package leader in the SFI Autoship center. His research focuses on target tracking, navigation, and SLAM, with applications in collision avoidance for unmanned vessels. He co-founded the company Zeabuz for marine surface autonomy solutions. PhD in Technical Cybernetics (NTNU, 2010) MSc in Industrial Mathematics (NTNU, 2005) Research Interests: Edmund specializes in theoretical foundations of multitarget tracking, integrating target tracking with navigation/SLAM, and applying these to autonomous maritime collision avoidance. His work addresses challenges in heavy-tailed clutter, sensor fusion, and situational awareness for autonomous ferries and river barges. Publications Trends: His recent work (2018–2022) emphasizes collision avoidance algorithms, sensor fusion, and SLAM for autonomous vessels. Key areas include maritime radar tracking, trajectory prediction using AIS data, and integration of visual/Lidar sensors for navigation. Guidance: He has supervised numerous PhD candidates on topics like digital twins, multi-sensor tracking, and risk assessment for autonomous ships, including graduates from 2014–2024. Projects: Active in Autoferry (autonomous ferries), Autobarge (river barges), and the ORCAS initiative. Collaborates with the SFI Autoship center.
Mohammad Derawi is a Professor in the Department of Electronic Systems at the Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU), Gjøvik campus. He leads the Smart Wireless Systems (SWS) research group and serves as the scientific leader of the IoT Lab at NTNU Gjøvik. Educational Background: PhD in Information Security from NISLab (Norway) and CASED (Germany) BSc and MSc in Informatics from DTU (Denmark) His research interests span smart wireless systems, Internet of Things (IoT), information security with a focus on biometric authentication, digital electronics, applied machine learning for activity recognition, and e-learning technologies. His work integrates cybersecurity, embedded systems, and data science to develop secure and intelligent IoT solutions for real-world applications. The recent publications highlight a strong trend in mmWave-based sensing for unmanned aerial systems, RF fingerprinting for secure identification, IoT security frameworks, and machine learning applications in education and human resource analytics. His research bridges theoretical innovation with practical implementation, particularly in smart cities, healthcare, and transportation. Scientific Awards and Recognition: Invitation to the Crown Prince and Princess's 50th birthday celebration, 2023 Study Quality Award, NTNU, 2017 Norway’s Youngest Professor Award, 2016 Denmark’s youngest M.Sc. engineering award, 2009 IEEE Commendation for Young Professionals Volunteer, 2011 Multiple best paper awards from IEEE, ACM, and Springer Mohammad Derawi has been involved in several funded research and development projects, including IoT Safetraffic (RFF Inland), Ambulance Drone (NTNU Vice-Rector), Wireless ECG (Innovation Norway), biometric handgun security (RFF Innlandet), and the EU Framework 7 TURBINE project. He mentors students and collaborates with international researchers, contributing significantly to both academic and applied domains. His leadership in the SWS group and IoT Lab fosters innovation in wireless and secure embedded systems. He is actively engaged in laboratory and team-based research, particularly through the Smart Wireless Systems group and the IoT Lab, focusing on developing secure, intelligent, and scalable solutions for next-generation wireless applications.
Øyvind Wiig Petersen is an Associate Professor at the Department of Structural Engineering, Norwegian University of Science and Technology (NTNU). His research focuses on bridge dynamics, wind and wave loading, inverse force identification, structural monitoring, and machine learning applications in structural mechanics. He works extensively with long-span suspension bridges and floating bridge systems. Current research areas include vortex-induced vibrations, Kalman filter applications, wind tunnel testing, and finite element model updating. He has published in leading journals like Journal of Wind Engineering, Mechanical Systems and Signal Processing, and Engineering Structures. His work integrates experimental data with computational models for structural condition assessment and load estimation.
Zhiliang Zhang is a Professor of Mechanics and Materials at the Department of Structural Engineering, Norwegian University of Science and Technology (NTNU) . He is renowned for his contributions to fracture mechanics and material science, serving as Editor-in-Chief of Engineering Fracture Mechanics and recipient of the Griffith Medal from the European Structural Integrity Society (ESIS). His research focuses on damage mechanics, hydrogen embrittlement, and anti-icing materials, utilizing experimental and computational approaches. Education: BSc and MSc in Structural Engineering, Tongji University (1985, 1988) PhD in Mechanical Engineering, Lappeenranta University of Technology (1994) Research Interests span damage and fracture mechanics , additive manufacturing (AM) , hydrogen embrittlement , and anti-icing surface development . His work integrates multiscale computational modeling with nanomechanical experiments , addressing challenges in energy, structural integrity, and material design. Publication Trends highlight his expertise in hydrogen embrittlement , gas hydrate adhesion , anti-icing surfaces , and additive manufacturing . His articles in journals like Chemical Reviews and Advanced Materials emphasize predictive modeling , nanoscale characterization , and sustainable material solutions . Scientific Awards include Griffith Medal (2024, ECF24) ESIS Fellow (2014) Norwegian Academy of Technological Sciences membership Academic Service involves external doctoral examinations at institutions like Paris Tech and National University of Singapore, and faculty review roles at Imperial College and University of Michigan. He founded the NTNU Nanomechanical Lab in 2006 and has led 14 externally funded projects totaling over €12 million.
Tiina Komulainen is a Professor at Oslo Metropolitan University's Faculty of Technology, Art and Design, specifically within the Department of Mechanical, Electronics and Chemistry. Her work focuses on environmental technology, biogas production, and wastewater treatment through advanced simulation and control engineering techniques. Key affiliations: Applied Artificial Intelligence research group Collaborative projects: MaxBiogas (with OsloMet and Veas) Research themes: Digital competence development in water industry, smart water technology Her research integrates mathematical modeling, machine learning, and technical cybernetics to optimize biological wastewater processes. Recent projects involve virtual sensors, nutrient estimation, and dynamic simulation for sustainable biogas production. Publications highlight collaborations with international institutions like Linköping University and Mälardalen University, focusing on municipal MBBR processes, energy-efficient wastewater treatment, and district heating network optimization. She contributes to educational advancements through simulation-based learning technologies and has developed teaching materials on model predictive control at Metropolia University of Applied Sciences.
