Yuriy Rogovchenko is a Professor in the Department of Mathematical Sciences at the University of Agder. His research spans differential equations, mathematical modeling, and education innovation, with applications in biology, social sciences, and engineering. Rogovchenko has contributed extensively to mathematics education through projects like PLATINUM (Erasmus+ Strategic Partnership) and CPEA-ST-2019/10067 (Eurasia project). PhD in differential equations (Institute of Mathematics, Kyiv, 1987) Regular Associate at Abdus Salam ICTP, Trieste (2004-2011) Editor for 11 international journals Referee for over 70 journals Research Interests: Qualitative theory of differential equations, perturbation methods, mathematical modeling in interdisciplinary contexts. He focuses on enhancing conceptual understanding through inquiry-based learning and nonstandard problems. Publications: Recent works include advancements in linear system observability, parameter identification methods, and educational studies on exact differential equations. His collaborations with Svitlana Rogovchenko and Matthias Pätzold highlight applications in engineering and biology. Awards: Sørlandet kompetansefonds research award (2016).
Albert Lau is an Associate Professor of Civil and Environmental Engineering at the Norwegian University of Science and Technology (NTNU), located in Trondheim, Norway. He specializes in railway engineering, structural dynamics, and transportation systems. Lau holds leadership roles as the Study Program Leader for the MSc in Road, Railway, and Transportation Engineering, overseeing curriculum development and program coordination. His research focuses on railway track design, dynamic modeling of train-track interactions, and infrastructure maintenance, with projects such as the MeTinT initiative (Measurement with Train in Regular Traffic). He has extensive experience supervising master’s and PhD students, and his work emphasizes innovation in rail infrastructure and sustainable transportation solutions. Education and Professional Background: Lau earned his PhD from NTNU in 2018, focusing on numerical simulations of railway turnouts. Prior roles include Postdoc (2018–2020) and Assistant Professor (2017–2018) at NTNU, and teaching at Oslo Metropolitan University (2020). His industry experience includes roles as a Design Engineer (2010–2012) and Project Engineer (2013–2014) in Malaysia, where he managed construction projects and structural design. Research Interests: Lau’s work spans railway track dynamics, infrastructure health monitoring, and machine learning applications in transportation. Key projects include developing digital twins for railway test sites and analyzing ground displacement impacts on track anomalies. His contributions to the Road, Railway and Transport Group at NTNU aim to advance rail safety and efficiency through interdisciplinary approaches. Teaching and Outreach: Lau coordinates courses such as TBA4225 (Railway Engineering) and BA6012 (Fundamental Railway Technology). His outreach includes expert commentary on railway incidents, such as an interview on NRK (2024) discussing potential causes of a train accident. Current initiatives focus on revitalizing regional rail services and optimizing train positioning systems.
Inga Berre is a Professor at the Department of Mathematics, University of Bergen, and serves as Director of the Center for Modeling of Coupled Subsurface Dynamics (CSD). She leads the Porous Media Research Group and was appointed Argyris Visiting Professor at the University of Stuttgart's SimTech Cluster of Excellence in 2023. Research Interests: Mathematical modeling, partial differential equations, numerical methods for coupled thermo-hydro-mechanical-chemical processes in subsurface systems, and fault reactivation induced by injection/production. Scientific Leadership: Member of SIAM Council (2022-2027), Chair of SIAM GS activity group (2021-2022), Co-Chair of SET-Plan Deep Geothermal Implementation Working Group (2019-2021), and Chair of the Joint Program Geothermal, European Energy Research Alliance (2018-2021). Awards: 2011 Meltzer Award for Young Researchers. Advisory Roles: Member of Scientific Advisory Boards for GFZ (2024-2027) and SFB1313 (2018-), among others. Teaching: Developed courses on calculus, functional analysis, mathematical modeling, and numerical methods at the Bergen Summer Research School.
Terese Løvås serves as Vice Dean of Research and Innovation at the Faculty of Engineering, Norwegian University of Science and Technology (NTNU), where she leads strategic development of research and innovation activities. She concurrently holds the position of Professor of Combustion and Thermodynamics within the Department of Energy and Process Engineering. Her leadership responsibilities include oversight of Centers of Excellence, Horizon Europe projects, and PhD researcher training. Her research focuses on combustion engineering and alternative fuel technologies , particularly investigating ammonia and hydrogen combustion for zero-emission engines, biomass gasification processes, and reactive multiphase flow modeling. She heads the Engine Lab at NTNU and teaches Thermodynamics, Heat, and Combustion courses. Her work bridges theoretical modeling with experimental validation in sustainable energy systems. Løvås actively contributes to major research initiatives including LowEmission (SFI center), ACTIVATE (ammonia-powered agricultural vehicles), AMAZE (ammonia zero-emission), and CAHEMA (marine ammonia/hydrogen engines). Her publications reveal strong trends in ammonia combustion chemistry , emissions reduction , and advanced computational modeling for sustainable fuel systems, with increasing focus on nitrogen oxide formation mechanisms and dual-fuel strategies. Member of the Board of Directors, Combustion Institute (2022–present) Joint Editor, Proceedings of the Combustion Institute (2019–present) Alumni Fellow in Engineering, Churchill College, Cambridge University As Vice Dean, she manages NTNU's Research and Innovation Committee and represents the faculty in NTNU's Research and Innovation Committee. She supervises multiple PhD candidates and leads international collaborations through projects funded by the Norwegian Research Council, Nordic Energy Research, and EU programs. Her laboratory work focuses on optical engine diagnostics and advanced combustion testing. Løvås maintains active industry engagement through her leadership in the ComKin Research Group and membership in the Institute of Physics and Scandinavian-Nordic Section of the Combustion Institute. Her current work emphasizes practical implementation of ammonia-fueled engine technologies for marine and agricultural applications.
Marta Molinas is a Professor at the Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). Her research spans multiple interdisciplinary domains with a focus on EEG technology and brain-computer interfaces. She actively supervises numerous Master's projects and maintains extensive international collaborations with institutions including Kavli Institute for Systems Neuroscience, RIKEN Center for Brain Science, University of Tsukuba, Juntendo University, and several European universities. Professor Molinas' research interests center on developing innovative EEG technologies, particularly her FlexEEG concept for reduced-channel EEG systems with brain imaging capabilities. Her work integrates signal processing, artificial intelligence, and neuroscience to create practical applications in mental health, sleep research, neurorehabilitation, and human-computer interaction. She specializes in EEG source imaging, machine learning for brain signal analysis, and the development of brain-computer interfaces for various applications including locked-in syndrome communication, ADHD treatment, and driver monitoring systems. Her publication portfolio demonstrates strong trends in interdisciplinary research combining neuroscience with electrical engineering and artificial intelligence. The work shows particular emphasis on developing practical EEG-based systems that minimize invasiveness while maintaining analytical power, with applications spanning healthcare, rehabilitation, and human augmentation. Her research bridges theoretical signal processing with real-world implementations through numerous student projects and international collaborations. Professor Molinas actively supervises a large team of Master's and PhD students across multiple projects, with each project typically requiring two students working collaboratively. Her research is supported through numerous international collaborations with institutions in Japan, India, and Europe, indicating substantial research funding and project leadership. She has developed a pipeline of student projects that build upon previous work, creating a cumulative knowledge base within her research group. She leads the EEG ITK research team at NTNU, which focuses on developing the FlexEEG headset prototype featuring flexible, wireless, dry electrodes designed to move across the scalp. This team works at the intersection of neuroscience, electrical engineering, and computer science, developing applications for sleep research, mental health monitoring, neurorehabilitation, and brain-computer interfaces. The team collaborates extensively with international partners including the Kavli Institute for Systems Neuroscience, the International Institute of Integrative Sleep Medicine at University of Tsukuba, and several engineering departments across Europe and Asia.
Professor Ingrid Bouwer Utne is a faculty member at the Department of Marine Engineering, Norwegian University of Science and Technology (NTNU). Her primary research focuses on risk analyses of ships, marine systems, and autonomy, with emphasis on operational safety, maintenance management, and risk control in autonomous maritime technologies. Key Projects: Leader of the Risk Group at NTNU, involved in the SFI Autoship initiative and ERC AdG BREACH project addressing risk-based rationality in autonomous systems. Research Interests: Autonomous systems design, probabilistic risk assessment, safety engineering, and risk-informed decision-making for marine operations. Grants & Funding: Secured funding from the Research Council of Norway, MAROFF, and industry partners for projects like ORCAS (Online Risk Management for Autonomous Ships) and UNLOCK (Supervisory Risk Control). She supervises numerous PhD students and postdocs, focusing on topics such as autonomous vessel navigation, risk modeling for underwater robotics, and decarbonization of maritime systems. Her work bridges theoretical risk analysis with practical applications in marine autonomy and safety systems.
Filippo Maria Bianchi is an Associate Professor in the Department of Mathematics and Statistics at UiT The Arctic University of Norway, where he conducts research at the intersection of machine learning, dynamical systems, and complex networks. He is also a Senior Researcher at NORCE Norwegian Research Centre and actively contributes to the IEEE Task Force on Learning for Structured Data and the ELLIS Society. Department: Department of Mathematics and Statistics School: Faculty of Science and Technology University: UiT The Arctic University of Norway Adjunct Position: Senior Researcher, NORCE Education: Bachelor’s in Computer Engineering, Sapienza University of Rome Master’s in Artificial Intelligence & Robotics, Sapienza University of Rome (cum laude, 2012) PhD in Machine Learning, Sapienza University of Rome His research focuses on graph machine learning, time series analysis, reservoir computing, and probabilistic forecasting , with applications in energy analytics and remote sensing. He has led and contributed to numerous projects involving Arctic power grids, satellite-based environmental monitoring, and deep learning for sustainability. The recent publications reflect a strong trend in graph neural networks —particularly pooling mechanisms, spatiotemporal modeling, and explainability—alongside applications in energy forecasting, avalanche detection, and remote sensing . His work combines theoretical innovation with real-world impact, especially in Arctic and remote environments. Scientific Affiliations and Leadership: Vice-Chair, IEEE Task Force on Learning for Structured Data Member, ELLIS Society Co-founder, Northernmost Graph Machine Learning group Member, IEEE Task Force on Reservoir Computing Visiting Professor, Politecnico di Milano (2024–2025) He actively mentors students and collaborates on interdisciplinary research. He has led projects in power grid reliability, solar fault detection, and unsupervised change detection in satellite imagery . His work is supported by open-source implementations and reproducible research practices. Laboratories and Research Groups: Northernmost Graph Machine Learning group (co-founder) ARC Research Group, UiT Graph Machine Learning Group, Lugano
Professor Inge Hoff is affiliated with the Norwegian University of Science and Technology (NTNU) in the Department of Civil and Environmental Engineering, where he has served since 2009. Prior to this, he held roles as senior researcher and research leader at SINTEF. Research Interests : Materials for road construction, frost protection, laboratory testing, pavement dimensioning, road rehabilitation, state development modeling, ground-penetrating radar surveys, and concrete/natural stone coverings. Students : Mentors active PhD fellows Lisa Hannasvik, Arman Hamidi, Clara Weber, and Shoiab Ahmad. Teaching : Coordinates courses like TBA4204/BYGT1102 Transport Infrastructure , BYGT2204 Road and Railway Construction , and BA8600 Pavement Structure Dimensioning . Recent publications highlight his expertise in granular material behavior, asphalt durability under climate stressors, and advanced structural assessment techniques. Collaborations with international researchers and presentations at major conferences (TRB, International Conference on Bituminous Mixtures) demonstrate his ongoing contributions to road engineering.
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.
Roles and Affiliations: Fred Espen Benth is a Professor in the Department of Mathematics at the University of Oslo, affiliated with the Risk and Stochastics research group. He holds a Dr. scient (PhD equivalent) in mathematics from the University of Oslo (1995). His academic journey includes roles as a researcher at the Norwegian Computing Center, a postdoc at the Universities of Aarhus and Oslo, and an Associate Professor at the University of Trondheim before becoming a full professor in 2002. Research Interests: Benth’s research focuses on mathematical finance, particularly energy and weather markets, commodity derivatives, and stochastic analysis. He explores modeling, estimation, and simulation of spot and forward prices, as well as pricing options and portfolio optimization. Recent work extends to climate systems, energy transition dynamics, and machine learning applications in financial and environmental modeling. Publications and Projects: His extensive publication record includes over 150 journal articles and book chapters, with a focus on energy markets, stochastic processes, and climate-related financial instruments. Notable projects include ‘Spatial-Temporal Uncertainty in Energy Systems (SPATUS)’ and contributions to interdisciplinary energy informatics. His work bridges theoretical stochastic analysis with practical applications in energy systems and risk management. Labs and Collaborations: Benth collaborates with the Stochastics of Renewable Energy Markets (STORE) group and contributes to initiatives like the ‘Computational Modelling and Machine Learning for Applications in Hydropower’ project. His research emphasizes the integration of stochastic methods with real-world energy and climate challenges.
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.
Sebastien Nicolas Gros is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). His research focuses on safe reinforcement learning (RL) and data-driven model predictive control (MPC), with applications in energy systems, biomedical engineering, and autonomous vehicles. Institution: Norwegian University of Science and Technology Department: Engineering Cybernetics His work emphasizes AI-driven optimization for domestic energy storage, battery integration, and smart building management. Collaborations include Equinor, DNV, Kongsberg, Volvo, and CorPower Ocean. Key themes in his publications include: Control theory for renewable energy systems (wave energy converters, buildings) Biomedical applications (artificial pancreas, glucose monitoring) Transportation systems (electric vehicles, autonomous ships) Machine learning integration with physical models He supervises 6 PhD students and co-supervises projects on multi-rotor wind turbines and industrial PhD collaborations. The articles demonstrate a convergence of RL, MPC, and uncertainty quantification across energy, biomedical, and transportation domains.
Daniel Beat Müller serves as Professor at the Industrial Ecology Programme within the Department of Energy and Process Engineering at the Norwegian University of Science and Technology (NTNU), Trondheim. His office is located at Realfagbygget Gløshaugen (E4-120) with contact details daniel.mueller@ntnu.no and +4791897755. His research centers on analyzing human needs in relation to material/energy flows and environmental impacts, with two primary focus areas: (i) urban evolution and associated material flows for managing building/infrastructure stocks, and (ii) national/global metal cycles to identify supply constraint reduction strategies. His methodology integrates design, modeling, and decision-making through transdisciplinary stakeholder engagement. Müller teaches Material Flow Analysis and Systems Analysis of the Built Environment for Industrial Ecology and Civil Engineering Master's students. His research outputs demonstrate strong trends in circular cities, critical mineral management, and urban metabolism, with recent publications emphasizing building information modeling, electric vehicle battery systems, and phosphorus cycling. His work consistently addresses resource criticality within energy transition contexts. As (ad interim) chair of the International Society of Industrial Ecology’s MFA-ConAccount section, he contributes to methodological standardization. He previously served on the U.S. National Research Council’s Committee on Defense Stockpiles and remains active in Switzerland's National Research Programme 65 "New Urban Quality". Müller supervises numerous Master's and doctoral students, with thesis topics spanning lithium-ion battery recycling, building stock dynamics, and urban resource flows. His projects frequently involve industry collaboration for practical implementation of material stewardship strategies.
Hakan Basarir is a Professor in the Department of Mining Engineering at the Norwegian University of Science and Technology (NTNU), Trondheim, Norway. His research and teaching focus on mining rock mechanics, rock mass characterization, underground support systems, and the application of soft computing methods in mining engineering. PhD in Mining Engineering (2002) 20+ years of research and teaching experience 60+ publications in journals and conferences Research Interests include rock mass property prediction using measurement while drilling (MWD) techniques, numerical modeling of mining structures, optimization of mine support systems, and sustainable material development. His work integrates machine learning and computational methods to address challenges in mining geomechanics and backfill design. Recent Publications highlight advancements in AI-driven lithology prediction, eco-concrete formulation, and backfill mixture optimization. He has also contributed to tunnel stability analysis and seismic rock slope modeling. Teaching includes advanced courses in mining engineering, mineral production modeling, and specialization projects in geotechnology.
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.
