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 .
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.
Kristian Seip is a Professor at the Department of Mathematical Sciences, Norwegian University of Science and Technology (NTNU), since 1993. He has held leadership roles, including Department Chair (1997–2001) and Vice Dean for Education at the Faculty of Information Technology, Mathematics and Electrical Engineering (2006–2012). He holds an MSc and PhD from NTNU. His research focuses on analysis and analytic number theory, with notable contributions to Dirichlet series, Hardy spaces, and operator theory. Key awards include the Frontiers of Science Award (2024), Doctor Honoris Causa from Universitat de Barcelona (2024), and Inaugural Fellow of the American Mathematical Society (2012). He is a member of prestigious academies like the Norwegian Academy of Science and Letters and the Royal Norwegian Society of Sciences and Letters. Seip has led major research projects, including the 2012–2013 Centre for Advanced Study program on Operator-Related Function Theory (co-leader with Yurii Lyubarskii) and multiple grants from the Research Council of Norway. His work emphasizes Fourier methods, multiplicative analysis, and interpolation theory.
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.
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.
Drew Kenneth Heard is an Associate Professor at the Department of Mathematical Sciences, Norwegian University of Science and Technology (NTNU), supported by the Trond Mohn Foundation. His research bridges stable homotopy theory, chromatic homotopy theory, and tensor triangulated geometry, with significant contributions to classification theorems and duality principles. Education: PhD (2014) from the University of Melbourne under Craig Westerland. Postdoctoral positions at Universität Regensburg (SFB Higher Invariants), Haifa University, Universität Hamburg (SPP 1786), and Max Planck Institute for Mathematics in Bonn. Heard's work focuses on the interplay between tensor triangulated categories and equivariant homotopy theory. His recent publications explore stratification theorems, vanishing lines in motivic homotopy, and the structure of Picard groups in chromatic settings. Collaborations with Tobias Barthel, Beren Sanders, and others have advanced understanding of localizing subcategories and descent techniques. Research Trends: Analysis of 15 recent articles reveals a core focus on tensor triangulated geometry (Balmer spectra, support theory), chromatic homotopy (K(n)-local spectra, Morava E-theory), and algebraic models for topological phenomena (comodule categories, local duality). Methodologically, he integrates geometric intuition with category-theoretic frameworks. Affiliations: Current: NTNU, Trondheim, Norway. Former: Universität Regensburg, Haifa University, Universität Hamburg, Max Planck Institute. Teaching: TMA4105 - Mathematics 2: Multivariable calculus and vector analysis. MA3408 - Algebraic Topology 2. Projects: Leads the "tt-geometry in Trondheim" project funded by the Trond Mohn Foundation, advancing tensor triangulated geometry classifications.
Elena Celledoni is a Professor of Mathematics at the Department of Mathematical Sciences, Norwegian University of Science and Technology (NTNU), where she has been employed since 2004. She leads the research group on differential equations and numerical analysis. Her academic background includes a Master’s degree (1993) and Ph.D. (1997) in mathematics from the Universities of Trieste and Padua, Italy, respectively. She has held postdoctoral positions at the University of Cambridge (UK), the Mathematical Sciences Research Institute (MSRI, Berkeley, CA), and NTNU. Her research focuses on numerical analysis, particularly structure-preserving algorithms for differential equations and geometric numerical integration. Recent work includes applications of neural networks in computational mechanics and data-driven modeling. She has co-authored over 100 peer-reviewed articles in journals such as Journal of Computational Physics , SIAM Journal on Scientific Computing , and Physica D . Her research interests span computational methods for dynamical systems, machine learning integration with numerical analysis, and geometric algorithms for shape analysis. She actively collaborates with international researchers, including contributions to conferences like NeurIPS and workshops on theoretical aspects of computational dynamics. Elena is a member of the editorial boards of Journal of Computational Dynamics and has organized workshops on structure-preserving integrators. Her work emphasizes preserving geometric properties in numerical methods, with applications in fluid dynamics, mechanical systems, and image processing.
Professor Ioanna Sandvig is a leading academic at the Norwegian University of Science and Technology (NTNU) , where she serves as group leader of the Integrative Neuroscience Group within the Department of Neuromedicine and Movement Science. She is also President of the Norwegian Neuroscience Society (NNS) and actively participates in international societies including the Federation of European Neuroscience Societies (FENS), Society for Neuroscience (SfN), ALBA Network, and Clinical-Academic Group for Alzheimer's Disease. Research Interests : Her group investigates neuroplasticity mechanisms in CNS damage and repair , focusing on structure-function relationships in biological neural networks under healthy and pathological conditions. They integrate in vivo , in vitro , and computational models to identify adaptive/maladaptive plasticity in neurodegenerative diseases like ALS and Alzheimer's. The research combines connectomics , transcriptional analysis , and geometric network modeling to decode network behaviors. Scientific Contributions : Recent publications explore topics including synaptic transcript dysregulation in ALS, functional complexity of 3D-engineered networks, and platinum microelectrode technologies. Her work demonstrates interdisciplinary approaches bridging neuroscience , bioengineering , and computational systems . Scientific Recognition : President, Norwegian Neuroscience Society (2024) Member, Federation of European Neuroscience Societies Member, Society for Neuroscience Member, ALBA Network Member, Clinical-Academic Group for Alzheimer's Disease Laboratory & Collaborations : The Integrative Neuroscience Group collaborates across NTNU's neuroscience departments and clinical institutions, developing tools for neuroplasticity analysis and contributing to preclinical disease modeling.
John Christian Ottem is a Professor in the Algebra, Geometry and Topology group at the Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Oslo. He maintains an active research program in algebraic geometry with significant contributions to multiple subfields. His research focuses on higher dimensional algebraic geometry, particularly on positivity of line bundles and subschemes, Calabi-Yau manifolds, the moduli space of curves, algebraic cycles, birational geometry, and Hodge theory. His work bridges theoretical foundations with concrete geometric problems, often addressing deep questions about the structure of algebraic varieties and their invariants. An analysis of his recent publications reveals a strong emphasis on birational geometry, Hodge theory, and the study of algebraic cycles. His research frequently investigates counterexamples to longstanding conjectures while developing new techniques in motivic integration, tropical geometry, and specialization methods. The consistent quality of his publications in top journals demonstrates his significant impact in the field. Young Research Talents grant from Research Council of Norway (2016-2021) Professor Ottem has supervised multiple PhD students including Bernt Ivar Utstøl Nødland, Bjørn Skauli, Martin Helsø (co-advised), Elisa Cazzador (co-advised), and Simen Westbye Moe. His research has been supported by prestigious grants including the Young Research Talents grant from the Research Council of Norway (2016-2021), which funded his project 'Equations in Motivic Homotopy.' As a member of the Algebra and Algebraic Geometry research group at the University of Oslo, Professor Ottem contributes to the vibrant mathematical community through the Algebra/Algebraic Geometry seminar at UiO. His work connects with broader mathematical research networks through collaborations with leading mathematicians worldwide.
Trygve Brauns Leergaard is a Professor at the Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo. His academic work focuses on neuroanatomy, brain connectivity mapping, morphological phenotyping, and neuroinformatics, particularly in rodent models. MD, University of Leiden (1996) PhD, University of Oslo (2000) Research Interests: Leergaard specializes in digital brain atlasing, histological validation of neuroimaging, and neuroinformatics. His work bridges computational methods with neuroanatomical studies, emphasizing standardization of brain mapping techniques. Recent Publications: His 2024–2025 research includes developing spatial integration workflows, comparative brain atlas metadata models, and developmental mouse brain atlases. Earlier work (2018–2023) explored neurodegenerative disease models, brain tumor growth patterns, and Parkinson's disease mechanisms. Laboratory & Collaborations: He co-leads the Neural Systems and Graphics Computing Laboratory and collaborates internationally with institutions including UC San Diego, Duke University, and NTNU.
Einar Malvin Rønquist is a Professor and Head of the Department of Mathematical Sciences at NTNU since August 2013. He holds a MSc from NTNU (1980) and a PhD from MIT (1988). His research focuses on numerical solutions of partial differential equations, spectral element methods, reduced basis methods, and computational fluid dynamics. He has been a leader in several research initiatives, including the Computational Science and Visualization program at NTNU (2003–2011). Rønquist is a member of prestigious academies: NTVA (since 2005) and DNKVS (since 2010). He has supervised 8 PhD students and 25 MSc students. His work spans computational science, with notable contributions to parametric modeling, parallel computing, and fluid dynamics simulation. His administrative roles include Vice President of R&D at Nektonics, Inc. (1991–1999) and Deputy Head of NTNU’s Department of Mathematical Sciences (Fall 2012). His publications highlight advancements in numerical methods for PDEs, including spectral element techniques, reduced basis approaches, and high-order approximations for complex geometries.
Elias Klakken Angelsen is a PhD candidate and Research Fellow in the Algebra, Geometry and Topology group at the University of Oslo (UiO). His academic advisors are Assoc. Prof. Achim Krause and Prof. John Rognes. He holds a Master of Science (2023) and Bachelor's degree (2021) in mathematics from NTNU, where his master's thesis on differential cohomology and geometric Hodge-filtered K-theory earned the Stubban Prize of 2023. His research focuses on algebraic K-theory, equivariant- and chromatic homotopy theory, and their intersections, particularly computations involving integral group rings of elementary abelian p-groups using geometric methods from tensor-triangulated geometry and higher algebra. Education: MSc in Mathematics (NTNU, 2021-2023) – Thesis: On differential cohomology and Geometric Hodge-filtered K-theory BSc in Mathematics (NTNU, 2018-2021) – Thesis: The K-theory and Morita equivalence classes of non-commutative tori His research interests include applying geometric ideas to algebraic K-theory computations and exploring advanced topics in algebraic topology. He is actively involved in conferences such as the European Autumn School in Topology (2024-2025), the Abel Symposium 2025, and research visits at the Isaac Newton Institute (Cambridge, 2025). Teaching responsibilities at UiO include courses like MAT1100 (Calculus), MAT3500/MAT4500 (Topology), and MAT1060 (Mathematics for Applications). Awards: Stubban Prize of 2023 (Master's thesis award) His work bridges geometric and algebraic methods in topology, with a focus on foundational questions in homotopy theory and their applications to K-theoretic computations.
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