Molly Maleckar is a Research Professor at the Computational Physiology Department of Simula Research Laboratory , Oslo, Norway. Her work bridges computational modeling, cardiac electrophysiology, and biomedical applications, with a focus on arrhythmia mechanisms, fibrosis modeling, and machine learning integration in cardiac risk prediction. Research Interests include: Computational Cardiology Ion Channel Dynamics Machine Learning in Medicine Excitable Tissue Modeling Cardiac Fibrosis Analysis Biomedical Simulation Scientific Contributions span 15+ publications (2018-2024) addressing atrial fibrillation, calcium handling, and AI-driven ECG analysis. Key collaborative projects involve patient-specific ventricular modeling and educational initiatives like the Simula Summer School in Computational Physiology .
Jan Martin Nordbotten is a full-time Professor at the Department of Mathematics, University of Bergen (UiB), with adjunct positions at Princeton University and NORCE. His research focuses on applied mathematics, particularly in porous media, CO2 storage, fluid dynamics, and interdisciplinary applications in hydrology, biomedicine, and ecology. He completed his PhD at UiB in 2004 and became Norway's third youngest professor in 2007. His work emphasizes numerical methods, multiscale modeling, and experimental validation. Affiliations: UiB (full-time), Princeton (adjunct), NORCE (adjunct) Research Group: Center for Sustainable Subsurface Resources Research interests span mathematical modeling of subsurface processes, including flow in fractured media, geomechanics, and phase-field fracture. Notable contributions include analytical and numerical solutions for CO2 leakage, multiphase flow, and development of tools like DarSIA for image processing in porous media. Publications highlight advancements in mixed-dimensional models, finite element methods, and experimental validation of CO2 storage forecasts. His work bridges theoretical mathematics with practical applications in energy and environmental systems.
Behzad Alaei serves as an Associate Professor in the Section for Study of Sedimentary Basins within the Department of Geosciences at the University of Oslo's Faculty of Mathematics and Natural Sciences. His office is located in room K38 of the Geology Building at Sem Sælands vei 1, 0371 Oslo, with a professional email contact at behzad.alaei@geo.uio.no. Dr. Alaei maintains an active research profile with publications spanning from 2005 to the present, demonstrating his ongoing contributions to geological sciences. Dr. Alaei's research spans multiple critical areas within structural geology and sedimentary basin analysis, with particular expertise in fault zone architecture, seismic interpretation techniques, and CO2 storage site assessment. His work bridges theoretical geological concepts with practical applications in petroleum geology and carbon sequestration. A significant portion of his research focuses on the Norwegian Barents Sea region, where he has conducted extensive studies on normal fault systems and their geometric characteristics. His recent work increasingly integrates machine learning and deep learning approaches with traditional geological analysis, reflecting the evolving nature of geoscience research methodology. The analysis of Dr. Alaei's publication record from 2018-2024 reveals a strong thematic continuity in fault characterization research, with progressive incorporation of advanced computational methods. Early publications focused primarily on traditional structural analysis of fault systems in sedimentary basins, while more recent work demonstrates increasing integration of machine learning techniques for fault detection and characterization. A notable trend is the application of these geological insights to practical challenges in carbon capture and storage, particularly regarding fault risk assessment for CO2 storage sites in the North Sea region. His collaborative work with Anita Torabi appears consistently throughout this period, suggesting a strong research partnership. Dr. Alaei maintains an active research program with multiple ongoing projects related to sedimentary basin analysis and fault characterization. His work appears to involve significant collaboration with both academic and industry partners, particularly in the context of CO2 storage research. While specific grant details aren't provided in the available information, his consistent publication record across multiple high-impact journals suggests successful funding of his research activities over the past two decades.
Elena Celledoni is a Professor in the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU). She has been employed at NTNU since 2004 and has held the position of professor since 2009. She is a member of the Differential Equations and Numerical Analysis Group at the Department of Mathematical Sciences and serves as its leader. Her educational background includes: Master's degree in Mathematics from the University of Trieste (1993) Ph.D. in Computational Mathematics from the University of Padua, Italy (1997) Elena Celledoni's research focuses on numerical analysis, particularly structure preserving algorithms for differential equations and geometric numerical integration. Her work bridges theoretical mathematics with practical computational methods, developing algorithms that maintain the geometric properties of the systems they approximate. She has made significant contributions to Lie group integrators, energy-preserving methods, and the application of these techniques to mechanical systems and shape analysis. In recent years, her research has expanded to include the intersection of numerical methods with machine learning, exploring how structure-preserving approaches can enhance neural networks and data-driven modeling. Her publications demonstrate a clear trend toward integrating traditional numerical analysis with modern machine learning techniques while maintaining a strong foundation in geometric integration and structure preservation. This interdisciplinary approach has led to innovations in neural ODEs, structure-preserving neural networks, and physics-informed machine learning models that respect the underlying mathematical structures of the systems they model. Elena Celledoni has received recognition for her work through the following honors: Member of the Royal Norwegian Society of Sciences and Letters Member of the European Consortium of Mathematics in Industry Council Member of the board of the International Council of Mathematics in Industry and Applications Editorial board member for SIAM Review, Journal of Computational Dynamics, Journal of Geometric Mechanics, Calcolo, and Networks and Heterogeneous Media As an advisor, she has mentored several students including Torbjørn Ringholm who completed his doctoral dissertation on 'Discrete gradient methods in image processing and partial differential equations on moving meshes.' Her research has been supported by various grants enabling her to lead projects on geometric numerical integration, collaborate internationally, and organize significant academic events such as the special semester at Isaac Newton Institute of MS in 2019 on 'Geometry, compatibility and structure preservation.' She leads the Differential Equations and Numerical Analysis Group at NTNU, which focuses on developing and analyzing numerical methods that preserve the geometric structure of differential equations. The group maintains active collaborations with researchers worldwide and has made substantial contributions to advancing the field of geometric numerical integration and its applications to real-world problems.
Torgeir Welo is a Professor at the Department of Mechanical and Industrial Engineering , Norwegian University of Science and Technology (NTNU) . He specializes in metal forming , particularly aluminum alloy structures , with a focus on plastic bending behavior , dimensional stability , and 3D forming technologies . His research also encompasses Lean Product Development , emphasizing knowledge reuse and maximizing customer value in automotive and aerospace applications. Key Research Areas : Metal Forming, Aluminum Processing, Springback Control, Lean Development, Additive Manufacturing, Material Substitution Teaching : Courses on Aluminum Technology , Metal Forming Analysis , and Machine Element Design Publications (15 most recent): Focus on springback monitoring , charge weld evolution , flexible forming , machine learning applications , and circular economy frameworks in metal manufacturing.
Morten Hovd is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). His work focuses on advanced control systems, particularly in model predictive control, optimization, and power electronics. He has contributed to control design for uncertain systems, bilinear models, and modular multilevel converters. Research Interests Control Theory and Model Predictive Control (MPC) Optimization Techniques in Control Systems Power Electronics and Smart Grid Applications Stability Analysis of Hybrid and Discrete-Time Systems Teaching TTK4210 - Advanced Control of Industrial Processes TK8118 - Mini-seminar in Cybernetics
Elisabeth Wetzer is an Associate Professor in Machine Learning at the Department of Physics and Technology, UiT The Arctic University of Norway. Her research bridges artificial intelligence with healthcare applications, focusing on multimodal image registration, bias mitigation in AI, and physics-informed learning models. Current Role: Associate Professor, Machine Learning Group Research Themes: AI ethics, medical imaging, cross-modal representations, algorithmic fairness Her recent work explores technical challenges in PET imaging analysis and societal implications of AI bias. Collaborative projects span medicine, mathematics, and computer science disciplines. Key scientific contributions include: Physics-informed deep learning for PET image data Studies on multi-task learning efficacy in medical classification Research on gender bias in algorithmic systems She actively participates in diversity initiatives and public outreach, including presentations at Nobel laureate conferences and media engagements on AI ethics.
Eirin Olaussen Ryeng is a Professor at the Department of Civil and Environmental Engineering, Norwegian University of Science and Technology (NTNU). Her work focuses on transportation engineering, road safety, and human behavior in urban and rural mobility contexts. Key research themes: winter road conditions, autonomous vehicles, cyclist/pedestrian safety, route familiarity, and sustainable transport infrastructure Active in transnational studies and multidisciplinary collaborations Recent publications analyze pedestrian gait in winter, cargo bike efficiency, driver risk perception, and geometric road design impacts. She employs advanced methodologies like sensor technology, survey analysis, and crash-based modeling. Teaching includes courses on road engineering, traffic safety, and transport infrastructure. She has presented at major European Transport Conferences and Nordic Traffic Safety Academy seminars.
Christian Hirsch is an Associate Professor for Data Science and Statistics at Aarhus University, where he studies random networks motivated from biology and health sciences through techniques from topological data analysis and stochastic geometry. He is a member of the Stochastics group at the Department of Mathematics and holds additional affiliations as an Associate Fellow of the Aarhus Institute for Advanced Studies, and with the AU DIGIT Centre and the AU Quantum Campus. Current Position: Associate Professor for Data Science and Statistics, Aarhus University Previous Positions: Assistant Professor at University of Groningen and University of Mannheim Postdoctoral Experience: Aalborg University, LMU Munich, WIAS Berlin Education: PhD from Ulm University Christian Hirsch's research focuses on the statistical foundations of topological data analysis, large deviations theory in stochastic geometry, and percolation theory of spatial random networks. His work bridges theoretical mathematics with practical applications in data science, particularly in analyzing complex structures through topological methods. He investigates how topological features form and disappear in growing data structures, developing statistical tests to determine whether observed patterns are significant or merely random occurrences. His recent publications reveal a strong trend toward applying topological data analysis to increasingly complex structures, with significant focus on statistical validation of topological features. Hirsch has made substantial contributions to understanding the probabilistic behavior of persistent homology, developing functional central limit theorems and large deviation principles for topological functionals. His work spans theoretical foundations in stochastic geometry while finding applications in materials science, neural networks, and wireless communication systems. As an educator, Hirsch teaches graduate courses including Topological Data Analysis, Stochastic Geometry, Monte Carlo Simulation, Markov Decision Processes, Probability Theory, and Stochastic Processes. He has supervised numerous PhD, MSc, and BSc students, with several of his former students securing academic positions at institutions like University of Leiden, Tokyo Institute of Technology, and Budapest University of Technology. Hirsch leads a research group within the Stochastics group at Aarhus University, collaborating extensively with researchers across Europe and North America. His work demonstrates how topological methods can provide rigorous statistical insights into complex data structures, making significant contributions to both theoretical mathematics and practical data analysis techniques.
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.
Jan G. Bjaalie is Professor of Neuroinformatics and Dean of Research and Innovation at the University of Oslo Faculty of Medicine . Since 2023 he heads the faculty’s research and innovation strategy, while directing the Neural Systems and Graphics Computing Laboratory at the Institute of Basic Medical Sciences. Education: 1990 Ph.D. in Neuroanatomy, University of Oslo 1986 M.D., University of Oslo Research interests revolve around collaborative and open neuroscience, digital brain atlasing, and the cyber-infrastructures that enable data sharing. He leads efforts to build next-generation atlases that integrate multi-scale brain architecture and connectivity data, and to develop ontologies and FAIR-compliant platforms for global neuroscience. Recent work emphasizes in silico integration of rodent and human imaging datasets, leveraging machine-learning registration tools and cloud-based services such as EBRAINS. The goal is to transform how brain data are stored, visualised and reused across laboratories worldwide. Scientific output & impact: A scan of publications from 2023-2025 reveals a strong focus on digital atlas frameworks, automated image registration (DeepSlice, DeMBA), open data standards (AtOM ontology), and large-scale analyses of genetic influences on brain structure in Alzheimer’s models. These works collectively advance reproducible, high-throughput neuroanatomy and cross-species translation. Grants & leadership roles: Coordinator/Partner in EU Flagships EBRAINS 2.0, BRAIN Health, Human Brain Project (2013-2026) Infrastructure Director, Human Brain Project (2018-2023) Leader of Neuroinformatics Platform & EBRAINS Data Services (2017-2023) Head of Institute of Basic Medical Sciences (2009-2016) Executive Director, International Neuroinformatics Coordinating Facility (INCF) (2006-2008) Chair, International Brain Initiative (2021-) Editorial & governance service: Founding Chief Editor Frontiers in Neuroinformatics (2007-), Section Editor Brain Structure and Function (2002-2020), member of the INCF Governing Board and EBRAINS AISBL Management Board, and numerous international advisory panels on data governance and ethics. His laboratory hosts the Norwegian Neuroinformatics Node and collaborates closely with global consortia to deliver open-access atlases, software pipelines and FAIR data standards that underpin modern neuroscience.
Dr. Charlotte Boccara is a Group Leader at the University of Oslo, leading the Charlotte Boccara Group focused on Systems Neuroscience & Sleep. Her research addresses the neural mechanisms of sleep in cognitive development, funded by the European Union (ERC), and bridges Developmental Neurobiology, Systems Neuroscience, and Sleep Research. Education: PhD in Neurobiology (NTNU, 2014), MSc/BSc from Université Marie et Pierre Curie Research Experience: 2019–Present - Project Manager for SleepCog, 2018–2019 - Marie Curie Fellow, 2006–2012 - PhD Candidate at Kavli Institute Her work emphasizes computational methods to analyze neural representations in adult and developing brains, with key projects on SleepCog (2024–2029) mapping sleep oscillations in rat pups using in vivo recordings and optogenetics. Recent publications (2025–2008) span topics like: Autophagy and Sleep (2025) Parahippocampal Speed Cells (2022) Egocentric Boundary Representation (2019) Hippocampal Replay Mechanisms (2017) Scientific awards include: 2019 Young Research Talents (UiO) 2018 Marie Curie MSCA-IF Fellowship 2017 MRC CNDD Seed Funds 2006 NTNU Doctoral Fellowship She teaches physiology and comparative anatomy at the University of Oslo and collaborates with institutions like IST Austria, King’s College London, and SINTEF. Her lab includes researchers such as Alessandro Arena, Solomiia Korchynska, and Lina Okinina.
Fedor Fomin is a Professor in the Department of Informatics at the University of Bergen. His research focuses on Theoretical Computer Science, including Graph Algorithms, Parameterized Complexity, Combinatorics, and Combinatorial Games. He is affiliated with the Norwegian Academy of Science and Letters, the Norwegian Academy of Technological Sciences, and the Academia Europaea, and holds fellowships from ACM and EATCS. His work has been recognized with the EATCS Nerode Prize in 2015 and 2017. Dr. Fomin has authored influential books such as Kernelization: Theory of Parameterized Preprocessing and Parameterized Algorithms , which are foundational in the field of algorithm design. His recent publications explore cutting-edge topics in parameterized complexity, graph theory, and distributed computing, with contributions to approximation algorithms, kernelization, and combinatorial optimization. His awards and honors reflect his significant impact on theoretical computer science. Fomin has been awarded an ERC Advanced Grant and has mentored numerous researchers, contributing to the advancement of algorithmic techniques and their applications.
Jaakko Akola is a Professor in the Department of Physics at the Norwegian University of Science and Technology (NTNU). His research focuses on computational materials science, particularly density functional theory (DFT) and atomistic simulations of materials, nanoparticles, molecules, and interfaces. He leads significant projects such as "SIDI" (inoculation in cast iron), "Infinity-RETIS" (chemical rare events), and "AllDesign" (rational alloy design), alongside coordinating EU-funded initiatives like "CritCat" for catalyst development. The Materials Theory group under Akola employs DFT, molecular mechanics, and Monte Carlo methods to explore atomic-scale structures and functions in technological applications. Key research areas include platinum-free catalysts for hydrogen energy, amorphous semiconductors for memory devices, noble metal nanoparticles in biological environments, and alloy design for cast iron and aluminum. Recent work integrates machine learning to advance theory-driven material design, reducing reliance on experimental trial-and-error. Akola's publications highlight advancements in hydrogen evolution catalysis, phase-change memory materials, and alloy precipitation. His projects often involve interdisciplinary collaborations with experimental teams. He teaches Quantum Physics 1 (FY2045) and Computational Physics (TFY4235) at NTNU, reflecting his commitment to education alongside research.
Morten Brun is an Associate Professor at the Department of Mathematics, University of Bergen. His research spans computational topology, persistent homology, and applications in biology and data science. Email: morten.brun@uib.no Research Interests: He specializes in topological data analysis, focusing on sparse nerves, relative persistent homology, and computational geometry. His work applies topological methods to biological problems, including drug resistance modeling in tuberculosis and immune profiling in multiple sclerosis. Recent Publications: His 2025 work includes hypercubic modeling of tuberculosis drug resistance and high-dimensional immune profiling post-stem cell transplantation. Earlier articles explore computational topology techniques (2017-2024) and interdisciplinary applications in toxicology and systems biology.
