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Micheal Babatunde OguntolaView profile

Research Fellow

• Enhanced Oil Recovery (EOR)
• Computational Science
• Petroleum Engineering
• +3 more

Micheal Babatunde Oguntola is a Postdoctoral Fellow at the Department of Mathematics, University of Bergen (UiB), Norway. He has previously collaborated with the Department of Energy Resources at the University of Stavanger and NORCE Norwegian Research Center AS. His research focuses on computational modeling, optimization, and decision-making frameworks in energy recovery systems. Current Affiliation: Department of Mathematics, UiB Past Affiliations: University of Stavanger, NORCE Norwegian Research Center AS Research Interests : Oguntola specializes in enhanced oil recovery (EOR) optimization, multiphysics simulations of porous media, and decision analysis in petroleum engineering. His work integrates applied mathematics, computational science, and geothermal energy modeling. Publication Trends : His recent publications (2019–2024) emphasize ensemble-based optimization techniques, polymer flooding applications, and multiphysics simulations for geothermal and oil recovery systems. Key subfields include stochastic gradient methods, heterogeneous reservoir modeling, and surrogate modeling. Collaborations & Grants : Oguntola has collaborated with institutions like OPM and the IOR Center, supported by grants under reference 230303.

Hans Julius SkaugView profile

Professor

• Biostatistics
• Marine Ecology
• Computational Statistics
• +4 more

Hans Julius Skaug is a Professor in the Department of Mathematics at the University of Bergen (UiB), Norway, with a distinguished career spanning several decades focused on statistical methodology and its applications to biological and ecological problems. Dr. Skaug's research expertise encompasses several interconnected domains: Biostatistics, particularly applications of statistics and probability to marine ecology Computational statistics, with pioneering work on Automatic Differentiation and Laplace approximation for complex model fitting Artificial Intelligence, where he has recently focused on variational autoencoders and diffusion models Development of innovative methods for line transect surveys and close-kin mark recapture (CKMR) His publication record shows a clear progression from foundational statistical methodology to increasingly sophisticated applications. The 2016 paper with Bravington and Anderson on 'Close-kin mark-recapture' in Statistical Science established him as a leader in population estimation methods. Recent publications (2023-2025) demonstrate continued innovation in refining CKMR techniques, applying TMB to diverse fields like insurance claims analysis, exploring sparse Bayesian learning, and addressing measurement errors in marine mammal surveys. His work consistently bridges theoretical statistical development with practical applications to real-world conservation and management problems. Dr. Skaug served as co-Editor in chief of the Scandinavian Journal of Statistics from 2018 to 2021, demonstrating his significant standing in the international statistical community. He teaches STAT110 Basic course in statistics at UiB during both spring and fall semesters and has conducted specialized workshops on CKMR and TMB at institutions including Dalhousie University in Halifax and the Institute of Marine Research in Bergen. He is actively involved in software development for statistical modeling through the TMB project (https://github.com/kaskr/adcomp) and ADMB (http://admb-project.org/), which implement his theoretical work on combining Automatic Differentiation with statistical modeling. His current research trajectory shows increasing integration of AI techniques with traditional statistical approaches, reflecting his observation that backpropagation in deep learning is fundamentally the same computational technique as Automatic Differentiation, which he has worked with for over 20 years.

Valeria VitelliView profile

Associate Professor

• High-dimensional Data Analysis
• Functional Data Analysis
• Bayesian Statistics
• +6 more

Valeria Vitelli is an Associate Professor in Statistics at the Oslo Centre for Biostatistics and Epidemiology, University of Oslo, where she has been faculty since 2018. Her research spans high-dimensional and functional data analysis, Bayesian methods, clustering, and preference learning. As a Principal Investigator, she leads projects within the Norwegian Centre for Knowledge-driven Machine Learning (Integreat), funded by the Norwegian Research Council. Education: PhD in Statistics, Politecnico di Milano, Italy (2012) MSc in Mathematical Engineering (cum laude), Politecnico di Milano, Italy (2008) Bachelor in Mathematical Engineering (cum laude), Politecnico di Milano, Italy (2006) Dr. Vitelli's research focuses on developing statistical methods for complex high-dimensional data, with applications spanning biomedical research, cancer genomics, and precision medicine. Her work integrates Bayesian inference, machine learning, and functional data analysis to address challenges in modern biostatistics. She has made significant contributions to ranking models, clustering algorithms, and methods for analyzing curves and intrinsically smooth processes. Her publication record demonstrates strong interdisciplinary collaboration across medicine, particularly in cancer research, ophthalmology, and neurology. Recent work shows a clear trend toward developing novel statistical frameworks for integrating complex datasets in precision medicine applications, with emphasis on Bayesian approaches and high-dimensional data analysis. Scientific Recognition: Principal Investigator for the Norwegian Centre for Knowledge-driven Machine Learning (Integreat) Awarded major FRIPRO grants from the Research Council of Norway Dr. Vitelli actively mentors students and researchers in statistical methodology development. Her grant portfolio includes significant funding from the Norwegian Research Council for projects focused on Bayesian clustering methods for high-dimensional omics data. She teaches courses including "Introduction to Machine Learning in Biomedical Research" and "Statistical Principles in Genomics" for medical students and PhD candidates. She leads a research group focused on statistical models for high-dimensional and functional data within the Oslo Centre for Biostatistics and Epidemiology, collaborating closely with medical researchers across various specialties to develop and apply advanced statistical methods to biomedical challenges.

Homam Nikpey SomehsaraeiView profile

Associate Professor

• Energy Systems
• Micro Gas Turbines
• Carbon Capture and Storage
• +12 more

Homam Nikpey Somehsaraei is an Associate Professor in the Department of Energy and Petroleum Engineering at the University of Stavanger, Faculty of Science and Technology. His research focuses on sustainable energy systems, with an emphasis on micro gas turbines, carbon capture, renewable integration, and artificial intelligence applications in energy modeling and optimization. He is actively involved in advancing decarbonization technologies and improving energy efficiency in industrial and residential sectors. His research interests span a wide array of topics including micro gas turbines , CO 2 heat pumps , hydrogen energy , phase change materials , and AI-driven energy system modeling . He applies artificial neural networks and machine learning techniques to optimize performance, enable condition monitoring, and enhance data reliability in distributed energy systems. His work often integrates experimental validation with theoretical modeling to ensure practical applicability. The recent publications highlight a strong trend towards decarbonization , renewable integration , and smart energy systems . Key themes include solar-assisted heat pumps, hydrogen applications in steel production, and AI-based optimization of micro gas turbines. These works reflect a multidisciplinary approach combining mechanical engineering, energy systems, and computational intelligence to address global sustainability challenges. Scientific Awards: No scientific awards mentioned in the provided text. Advising and Grants: While no formal list of students or grants is provided, Dr. Nikpey Somehsaraei has supervised or co-supervised numerous research projects, particularly in the areas of micro gas turbines and AI applications in energy. His collaborations with industry and academic partners suggest active involvement in funded research initiatives, though specific grant details are not listed. Labs and Teams: He is associated with advanced research in energy systems at the University of Stavanger, likely contributing to or leading work within experimental and computational labs focused on micro gas turbines, carbon capture, and sustainable heating technologies. His frequent collaboration with researchers such as Mohsen Assadi and Ward De Paepe indicates participation in a dynamic, interdisciplinary research team.

• Applied Mathematics
• Mathematical Modeling
• Multiphase Flow
• +8 more

Steinar Evje is a Professor of Applied Mathematics at the University of Stavanger, affiliated with the Faculty of Science and Technology and the Department of Energy and Petroleum Technology. His research bridges mathematical modeling with applications in petroleum engineering and cancer biology. His research interests include: Mathematical modeling of multiphase flow in porous media Conservation laws and PDEs Tumor cell migration and biomechanics Fluid-structure and cell-fluid interactions Data-driven discovery of PDEs Numerical analysis and scientific computing His recent publications (2023–2024) show a strong trend in combining machine learning with physical modeling, particularly in learning flux and diffusion functions from data, solving conservation laws using graph neural networks, and modeling tumor progression under fluid pressure. These works span disciplines from petroleum engineering to computational oncology, reflecting interdisciplinary impact. Scientific awards are not mentioned in the provided text. He actively collaborates with researchers in both engineering and biomedical fields. While specific students are not listed, his extensive publication record suggests mentorship of graduate students and postdocs. He has contributed to modeling in oil recovery, well control, and tumor microenvironment dynamics, with no mention of external grants but evident sustained research output. He is involved in modeling efforts related to: Two-phase and three-phase flow in porous media Cell migration under interstitial fluid flow Spontaneous imbibition and wettability alteration Gas migration in leaking wells Low salinity flooding in oil recovery

• Petroleum Engineering
• Drilling Engineering
• Wellbore Stability
• +12 more

Bernt Sigve Aadnøy is a Professor in the Department of Energy and Petroleum Engineering at the University of Stavanger, Faculty of Science and Technology. His work bridges theoretical and applied petroleum engineering, with a strong emphasis on drilling operations, well design, and geomechanics. His research focuses on drilling optimization, wellbore stability, drilling fluids, and smart well systems. He has extensively studied the use of nanoparticles in drilling fluids, rate of penetration modeling, torque and drag in 3D wells, and closed-loop drilling optimization. His work integrates computational modeling, laboratory experiments, and field case studies. The recent publications highlight a consistent trend in applying advanced modeling techniques—including machine learning, finite element analysis, and stochastic optimization—to solve complex drilling challenges. Topics include nanoparticle-enhanced fluids, real-time mechanical specific energy minimization, and autonomous downhole control systems, reflecting a strong interdisciplinary approach combining petroleum engineering with data science and control theory. Bernt Sigve Aadnøy has collaborated with numerous researchers and students, contributing to advancements in drilling safety, efficiency, and sustainability, particularly in challenging environments such as the Arctic and deep-water reservoirs.

Hugh Alexander von ArnimView profile

Research Fellow

• multimodal analysis
• sensor data fusion
• motion capture
• +3 more

Hugh Alexander von Arnim is a Doctoral Research Fellow at the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion (IMV), affiliated with the Faculty of Humanities at the University of Oslo. His research focuses on multimodal data analysis, sensor fusion, and motion capture technologies, with an emphasis on cultural and technological intersections. Education: M.Phil in Music, Communication and Technology, University of Oslo (2021–2024) B.A. in Sound and Music Production, Darmstadt University of Applied Sciences (2017–2021) His PhD project investigates methodological approaches to analyzing multi-modal musicking datasets, particularly spatial and temporal dimensions in fused data. His work is openly accessible via his thesis page . Research Interests: Multimodal analysis, sensor data fusion, motion capture, interactive systems, and cultural critiques of mediatised bodily representations. He explores how technology shapes perceptions of human movement and sound. Publications: Recent works include studies on sonic microinteraction preservation (2025), normative body representations via motion capture (2025), and machine learning in elite sports analysis (2024). Awards: 2021: Young Research Award (2nd place) from the German Association for Music Business and Music Culture Research Labs & Projects: Active in the RITMO Centre and the project Musical human-computer interaction .

Joakim BergliView profile

Professor

• Quantum Transport
• Quantum Information Theory
• Quantum Technology
• +3 more

Joakim Bergli is a Professor in Condensed Matter Physics at the University of Oslo's Department of Physics. His research focuses on theoretical solid-state physics, including quantum transport, quantum information theory, and foundational quantum mechanics. He is particularly known for work on decoherence in superconducting qubits and electron glass phenomena. He teaches courses such as FYS4110 (Modern Quantum Mechanics) and FYS1100 (Mechanics and Modeling). His recent publications span quantum thermodynamics optimization via reinforcement learning, photon interference experiments, and material dielectric properties. He collaborates on projects like the QOMBINE initiative for quantum computing and many-body theory. Key research themes include quantum system control, noise analysis in qubits, and energy landscape interpretations. His work bridges foundational quantum theory with applied areas like quantum technology and biophysical systems.

Guttorm AlendalView profile

Professor

• Environmental Monitoring
• CO2 Storage
• Oceanography
• +5 more

Guttorm Alendal is a Professor in the Department of Mathematics at the University of Bergen. His research focuses on mathematical modeling applied to environmental challenges, particularly CO2 storage monitoring, oceanography, and climate science. He leads projects like ACTOM (Advanced Monitoring for Offshore CO2 Storage) and MATH4SDG, emphasizing cross-disciplinary approaches to sustainability. Education: Ph.D. in Applied Mathematics (1996, University of Bergen, thesis: Implementing and testing of different gravity current models). Key Research Areas: Fluid dynamics, environmental monitoring systems, carbon sequestration risk assessment, and marine pollution modeling. Collaborates with NORCE Norwegian Research Centre and international initiatives like STEMM-CCS. Publications: Over 80 peer-reviewed articles on topics like CO2 seep detection, Bayesian environmental modeling, and particle transport in oceanic waters. Recent work includes data-driven dynamical systems for disease modeling and reinforcement learning for environmental surveillance. Awards: While no specific awards listed, his work has been funded by EC H2020, Research Council of Norway, and JPI-Oceans. Grants: Active projects include ACTOM (2019-2022), MATH4SDG (2021-2026), and SFI CRIMAC (2020-2028). Former roles include leading ECO2 (FP7) and co-authoring technical reports on CO2 plume dynamics. Labs/Teams: Involved in Digital Life Norway's dCOD project (systems toxicology of Atlantic cod) and the Bergen Ocean Model development team.

• Numerical linear algebra
• Matrix polynomials and spectral theory
• Stability and perturbation analysis
• +3 more

Aleksandr Malyshev is Professor of Mathematics at the University of Bergen. His research integrates numerical linear algebra, stability theory, optimisation-based control, and image-processing algorithms, yielding a portfolio of more than 60 peer-reviewed articles and conference contributions. Education & affiliations: Professor, Department of Mathematics, University of Bergen, Norway (present) Previous research and teaching engagements in informatics and applied mathematics at the same university Research interests: Malyshev’s core interest is the theoretical and algorithmic analysis of matrix problems arising in stability, control and imaging. He develops numerically reliable tools for assessing the distance to instability of dynamical systems, constructs preconditioners that accelerate optimisation solvers in real-time model predictive control, and designs variational models for 3-D reconstruction and image denoising. His work frequently combines spectral theory of matrix polynomials with practical issues such as high-performance implementation and medical-image quantification. Across the last decade his articles reveal three dominant strands: (i) stability and perturbation of time-delay and periodic systems, (ii) preconditioned iterative solvers for interior-point and MPC formulations, and (iii) variational and learning-based approaches to depth estimation, surface reconstruction and glenoid-bone assessment. These themes are unified by a common mathematical substrate—exploitation of matrix structure to obtain computationally efficient, numerically trustworthy solutions. Scientific awards & recognition: Regular invited speaker at international workshops on numerical linear algebra and control (e.g., SK Godunov conference 2009, IFAC 2018) Funded principal investigator / co-investigator on Research Council of Norway and EU Horizon Europe grants Advising & grants: Malyshev has supervised numerous MSc and PhD candidates in numerical analysis and scientific computing and currently advises graduate researchers on projects ranging from 3-D machine-vision algorithms to Krylov-subspace preconditioning. Recent grant participation includes EU project 101373 (3-D quantification of glenoid bone loss) and the Norwegian Research Council project 262203 on perfusion-flow simulation. Labs & collaboration: He collaborates closely with the Group for Numerical Methods and Applications at UiB, the Visual Computing cluster at the Department of Informatics, and maintains international partnerships with the Universities of Brest, Lübeck, and several US institutions. These joint efforts feed cross-disciplinary projects combining rigorous matrix analysis with real-world applications in biomechanics, process control, and computer vision.

Jakub Wiktor BothView profile

Researcher

• Porous Media
• Numerical Methods
• Image Analysis
• +5 more

Jakub Wiktor Both is a permanent researcher at the Department of Mathematics, University of Bergen (UiB). He is affiliated with the Porous Media Research Group and the Center for Modeling of Coupled Subsurface Dynamics. His research focuses on numerical methods for multiphysics problems in porous media, including image-based data analysis for laboratory experiments and mathematical modeling of CO2 storage. His work involves developing tools like the DarSIA (Darcy Scale Image Analysis) toolbox for fluid displacement analysis and PorePy, a Python simulation framework for fractured porous media. He leads projects on CO2 storage validation and has been awarded an NFR FRIPRO grant for his TIME4CO2 initiative, aiming to enhance CO2 storage capacity through mathematical modeling. Key research themes include optimal transport metrics, robust numerical solvers for coupled systems, and gradient flow structures in dissipative systems. His contributions span experimental validation frameworks (e.g., FluidFlower), digital twins (PoroTwin), and open-source software for subsurface dynamics. Notable awards include election as Chair of the Board of InterPore Norway (2024) and leadership in international projects like ERC CoG MaPSI and NFR Petrosenter CSSR. His interdisciplinary approach bridges mathematics, geoscience, and engineering for sustainable subsurface resource management.

• Artificial Intelligence
• Medical Technology
• Knowledge-Based Systems
• +3 more

Asma Belhadi is a Postdoctoral Fellow at Oslo Metropolitan University's Faculty of Technology, Art and Design, Department of Computer Science. Her research focuses on Applied Artificial Intelligence, bridging domains like Neuroscience, Medical Technology, and Intelligent Transportation Systems through innovative methodologies such as Visual Transformers, Federated Learning, and Graph Algorithms. Primary affiliation: Oslo Metropolitan University Academic discipline: Computer Science Research areas: Deep Learning, Explainable AI, Biomedical Signal Processing, Sustainable Systems Her recent work explores cross-domain applications including: EEG classification using enhanced visibility graphs Medical imaging with ensemble fuzzy deep learning Secure pattern mining for Medical Internet of Things Knowledge-enhanced object detection for agriculture Metaverse security using transformer models and GANs

• Cloud computing
• Cybersecurity
• Software engineering
• +9 more

Pål Halvorsen is a Professor at the Department of Computer Science within the Faculty of Technology, Art and Design at Oslo Metropolitan University. He works at the intersection of computer science and applied domains, with a particular focus on multimedia systems, distributed computing, and healthcare applications. Specializes in distributed multimedia systems Active in AI-driven forensic psychology applications Conducts research on medical imaging and diagnostics Develops sports analytics datasets and tools Works on communication and distributed systems His research spans several key areas of computer science, particularly focusing on multimedia systems and their applications in healthcare, sports analytics, and forensic psychology. He leads projects involving AI-driven child avatars for investigative interview training, develops datasets for medical and sports applications, and explores innovative approaches to image analysis and time-series data processing. Recent publications demonstrate strong activity in applying computer vision and deep learning to medical diagnostics, particularly in gastrointestinal tract analysis and ophthalmology. His work on sports analytics includes creating comprehensive datasets for ice hockey and soccer, while his forensic psychology research focuses on AI-enhanced interview training for child abuse investigations. Halvorsen collaborates extensively across disciplines, working with researchers in psychology, medicine, and sports science. His projects often involve developing novel tools for data analysis, including approaches to multimodal data handling, visual deep learning verification, and AI-enhanced prompt generation techniques.

Silvia Francesca StortiView profile

Professor

• Artificial Intelligence
• Machine Learning
• Brain-Computer Interface
• +2 more

Prof. Silvia Francesca Storti is a faculty member at the Department of Computer Science , University of Verona , Italy. Her research spans interdisciplinary domains at the intersection of Computer Science and Neuroscience , with a focus on Brain-Computer Interfaces , Machine Learning , and Multimodal Functional Imaging . Recent publications highlight her work in: Predictive modeling of human vigilance in Industry 5.0 environments (2025) Functional connectivity analysis in Alzheimer's disease (2024) Safety-critical systems in Industry 4.0 (2024) Neurophysiological risks in professional divers (2024) Hemispheric differences in visual cortex stimulation (2023) Her research explores both theoretical and applied aspects of neural network dynamics, cortical connectivity inference, and safety-optimized human-machine interaction.

Dongping SongView profile

Professor

• Operations Management
• Supply Chain Management
• Maritime Transport
• +7 more

Prof. Dongping Song is a Professor of Supply Chain Management at the University of Liverpool's Management School. His academic journey includes studies and affiliations with Nankai University, Zhejiang University, Newcastle University, Imperial College London, and Plymouth University. Education and academic background Senior Member of IEEE Associate Editor roles Grants from EPSRC, ESRC, Royal Society, British Council, and European Commission His research focuses on integrating data analytics , mathematical modeling , and machine learning to enhance supply chain , maritime logistics , and transportation systems . Recent work emphasizes container shipping optimization , disruption management , and maritime decarbonization . Recent publications span topics such as port operations , automated terminal strategies , and emission reduction in logistics , applying simulation-based tools and stochastic dynamic programming . He has secured funding from key UK and European research councils, contributing to maritime transport advancements. Collaborations include researchers from institutions like Liverpool John Moores University, Durham University, and others.