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Sidney Pontes-FilhoView profile

Research Fellow

• Complex Systems
• Unconventional Computing
• Computational Neuroscience
• +3 more

Sidney Pontes-Filho is a Postdoctoral Fellow at Simula Research Laboratory in Oslo, Norway, with a strong academic affiliation to the Norwegian University of Science and Technology (NTNU) where he completed his PhD in Computer Science. His primary departmental affiliation is with the Department of Computer Science at Oslo Metropolitan University (OsloMet). His research spans multiple institutions including Simula Research Laboratory where he works in the Numerical Analysis and Scientific Computing department. His educational background includes a B.Sc. in Computer Science from Federal University of Paraíba, Brazil (2013), an M.Sc. in Computer Science from Technical University of Kaiserslautern, Germany (2018), and a Ph.D. in Computer Science from NTNU. His research focuses on the intersection of complex systems, unconventional computing, computational neuroscience, and artificial general intelligence, with particular emphasis on neural cellular automata and criticality phenomena. Pontes-Filho's publication record reveals a consistent trajectory in exploring how critical systems can serve as substrates for intelligent behavior. His work demonstrates how neural cellular automata operating near critical points exhibit emergent properties relevant to artificial intelligence, including attention mechanisms, scalability, and robust control systems. His research spans theoretical frameworks like EvoDynamic, practical applications in soft robotics control, and medical imaging tools for brain extraction from fMRI data. His scientific contributions show an interdisciplinary approach merging concepts from statistical physics, neuroscience, and computer science to develop novel computational paradigms. The evolution of his work demonstrates increasing sophistication in connecting theoretical criticality concepts with practical AI applications, particularly in embodied systems and neuromorphic computing.

• Porous Media
• Subsurface Flow Modeling
• Reservoir Engineering
• +7 more

Carl Fredrik Berg is a Professor in the Department of Geosciences at the Norwegian University of Science and Technology (NTNU). His research focuses on porous media flow, subsurface reservoir modeling, and energy-efficient production strategies in petroleum engineering. Key areas include multiphase flow dynamics, CO₂ sequestration, phase-field modeling, and digital rock physics. Recent work emphasizes the interplay between material microstructure and macroscopic properties, particularly in porous media. Collaborations involve developing advanced computational methods for reservoir simulation, optimization algorithms for well placement, and energy policy analysis under CO₂ taxation frameworks. Publications highlight contributions to phase-field equations, permeability estimation from CT scans, and stochastic modeling for subsurface flows. His work bridges theoretical physics, applied mathematics, and engineering applications in oil recovery and environmental geoscience.

• Mathematics
• Partial Differential Equations
• Computational Mathematics
• +3 more

Håkon Andreas Hoel is an Associate Professor at the Department of Mathematics, University of Oslo. His research focuses on numerical methods for stochastic and partial differential equations, Monte Carlo methods, and data assimilation with an emphasis on developing efficient computational techniques for stochastic problems. Education: PhD in Numerical Analysis from KTH Royal Institute of Technology (2012), Master and Bachelor in Computational Science from University of Oslo (2006/2004). He has held postdoctoral positions at KAUST (2012–2014), University of Oslo (2014–2016), EPFL (2016–2017), and was a Junior Professor at RWTH Aachen (2019–2022). His research spans multilevel Monte Carlo methods, ensemble Kalman filtering, and stochastic numerical analysis with applications in spatiotemporal processes and uncertainty quantification. His work bridges theoretical analysis and practical computational implementation. Publications emphasize methodological advancements in stochastic numerical methods, with contributions to complexity reduction and error analysis in high-dimensional problems. Research groups include Computational Mathematics and Beregningsorientert Matematikk.

Geir Olve StorvikView profile

Professor

• Data Science
• Computational Statistics
• Bayesian Hierarchical Modelling
• +5 more

Geir Olve Storvik is a Professor in Statistics and Data Science at the University of Oslo. His research focuses on computational statistics, Bayesian hierarchical modeling, Monte Carlo methods, spatio-temporal modeling, and dynamical processes. He has made significant contributions to Bayesian machine learning and its applications in biological and environmental domains. University: University of Oslo Department: Statistics and Data Science Academic Rank: Professor Storvik's research spans interdisciplinary applications of Bayesian methods in fields such as neural networks, DNA methylation, and infectious disease modeling. His recent work includes sparse Bayesian neural networks and sequential Monte Carlo approaches for epidemiology. Key projects include Bayesian methods in machine learning and BigInsight Statistical and machine learning methods for sensor data . He serves as an advisor to PhD students like Aliaksandr Hubin and Ivar Grytten, with completed theses in graph-based genomics and Bayesian variable selection. His work appears in journals like Transactions on Machine Learning Research , The Journal of Artificial Intelligence Research , and Journal of the Royal Statistical Society , covering topics from model sparsity to ecological forecasting.

Odd Erik GarciaView profile

Professor

• Fusion energy
• Plasma physics
• Complex systems modeling
• +7 more

Odd Erik Garcia is a Professor at the Department of Physics and Technology , Faculty of Natural Sciences and Technology, UiT The Arctic University of Norway. He serves as Center Director for DYNAMO (UiT Aurora Center for Non-linear Dynamics and Complex Systems Modeling) and leads fusion energy research initiatives. His academic career spans 12+ years at UiT, including leadership roles as Institute Head (2015-2019) and departmental leadership positions.

Maben RabiView profile

Professor

• Cyber-Physical Systems
• Networked Control
• Hybrid Systems
• +5 more

Maben Rabi is a Professor at the Department of Information Technology and Communication, University of Southeast Norway. His work focuses on cyber-physical systems, networked control, and hybrid systems with applications in intelligent transportation and robotics. Current projects: CriSp (Research Council) and SafeSmart (KK Foundation). Research keywords: Cyber-Physical Systems, Networked Control, Hybrid Systems, Digital Society, DigiTech. Research Trends : His recent publications emphasize road friction estimation for autonomous vehicles, nonlinear control in relay feedback systems, probabilistic sampling for networked estimation, and packet loss modeling in vehicular networks. These works bridge control theory, cyber-physical systems, and transportation safety. Teaching : Courses include Technology Project (ITD25018) and Digital Control and Cyber-Physical Systems (ITD30019) .

Thordis Linda ThorarinsdottirView profile

Professor

• Statistics
• Data Science
• Environmental Science
• +5 more

Thordis Linda Thorarinsdottir is a Professor of Statistics and Data Science at the University of Oslo's Department of Mathematics, affiliated with the Faculty of Mathematics and Natural Sciences. She previously worked as a Chief Research Scientist and Research Leader for Climate and Environment at the Norwegian Computing Center (2006–2023). Her research focuses on developing stochastic models for environmental sciences, emphasizing uncertainty quantification and probabilistic prediction in climate, weather, and hydrology. Key areas include spatial and spatio-temporal modeling, Bayesian frameworks, and forecast evaluation. **Education**: PhD in Mathematical Statistics from Aarhus University (2006). **Research Interests**: Environmental applications in climate science, spatial modeling, probabilistic forecasting, and extreme event analysis. She collaborates with experts in meteorology, hydrology, and climate science to address data deficiencies and real-world challenges. **Projects**: Leads initiatives like CONFER (Climate Futures) and is part of the Integreat research group. Her work integrates statistical theory with environmental process understanding to enhance predictive accuracy and decision-making under uncertainty. **Awards**: Not explicitly listed, but her contributions to climate and environmental statistics are recognized internationally. **Grants & Labs**: Active in interdisciplinary collaborations, including the Norwegian Research Council-funded projects. Her team focuses on probabilistic modeling and ensemble forecasting techniques.

• Quantum Computing
• Statistical Physics
• Stochastic Processes
• +3 more

Sergiy Denysov serves as Professor in the Department of Information Technology at Oslo Metropolitan University's Faculty of Technology, Art and Design. He is an active member of the Mathematical Modeling research group, focusing on the intersection of computational physics and quantum information systems. His research spans: Quantum Computing and Noisy Intermediate-Scale Quantum (NISQ) devices Statistical mechanics of complex systems Stochastic processes including Lévy flights and Markovian dynamics Spectral theory of random operators Machine learning applications in physical modeling Mathematical foundations of quantum information Analysis of his 2021-2025 publications reveals strong emphasis on quantum-classical algorithm synergies, spectral properties of quantum operations, and anomalous diffusion phenomena. His work consistently bridges theoretical physics with computational implementations, particularly through Python-based tools for stochastic process modeling. Denysov actively contributes to the Mathematical Modeling research group, which develops analytical and computational frameworks for physical systems across quantum information, statistical mechanics, and complex dynamics domains.

Abdalrahman AlgendiView profile

Research Fellow

• Planning
• Optimization
• Decision Support
• +3 more

Abdalrahman Algendi is a Research Fellow at the Faculty of Logistics, Molde University College. His primary role involves advancing research in logistics optimization, decision support systems, and operational planning. Contactable at abdalrahman1.algendi@himolde.no and located in Office C314. Research Interests: Focused on solving complex logistics challenges through optimization techniques. Specializes in integrating mathematical models for healthcare routing, maritime inventory systems, and decision support frameworks. His work bridges theoretical algorithms with real-world applications in emergency healthcare logistics and maritime supply chains. Advising & Grants: Currently involved in research projects related to home healthcare optimization and maritime logistics. No listed advisees/students at present. Labs/Teams: Active member of the Planning, Optimization and Decision Support research group at Molde University College.

Aliaksandr HubinView profile

Associate Professor

• Statistics
• Artificial Intelligence
• Econometrics
• +4 more

Aliaksandr Hubin is an Associate Professor at the University of Oslo's Department of Mathematics and Natural Sciences, affiliated with the Center for Computational Inference in Evolutionary Life Science (CELS) and Statistics and Data Science research groups. His career spans Bayesian methodology development, machine learning applications, and interdisciplinary research. PhD in Statistics (University of Oslo, 2018) MSc in Operations Research (Molde University College, 2014) Specialist degree in Economic Cybernetics (Belarusian State University, 2013) Research spans: Bayesian model selection and averaging Probabilistic machine learning Computational statistics Applications in genomics, clinical diagnostics, and aquaculture Weak supervision methods in NLP Medical decision support systems Recent publications focus on: Bayesian neural network compression Clinical score validation Outlier detection mechanisms Evolutionary optimization in inference Domain-informed deep learning Scientific recognition: NIMA 2014 award Multiple student research awards (2012-2013) Graybill poster competition second place (2017) Methodological contributions include: Genetically modified MCMC algorithms Adaptive simulated annealing EM Probabilistic framework development

Olena TymoshenkoView profile

Research Fellow

• Stochastic Differential Equations
• Stochastic Analysis
• Mathematical Finance
• +6 more

Olena Tymoshenko is a Research Fellow at the University of Oslo, Department of Mathematics, specializing in stochastic analysis and its applications. She is affiliated with the Risk and Stochastics research group and contributes to projects on robustness, asymptotic behavior, and stability of stochastic differential equation (SDE) models. Current Position: Research Fellow, Department of Mathematics, University of Oslo Previous Role: Associate Professor at National Technical University of Ukraine (2019–2023) Research Interests : Stochastic differential equations, asymptotic behavior of solutions, mathematical finance, limit theorems, pseudoregularly varying functions, and stochastic dynamics in biological systems. Recent Article Trends : Focus on partial differential equations driven by Lévy noise, McKean-Vlasov systems, and Brownian sheets. Applications span mathematical finance, epidemiology (monkeypox transmission), and control theory (Kalman filter algorithms). Teaching : Courses include Introduction to Mathematical Finance and Investment Theory (STK-MAT3700, Fall 2022) and Statistical Methods and Applications (STK4900, Spring 2023). Other Affiliation : Participant in the International Summer School as a student.

• Wind Energy
• Offshore Wind Turbine Technology
• Structural Optimization
• +6 more

Michael Muskulus is a Professor at the Department of Civil and Environmental Engineering at NTNU. His research focuses on offshore wind energy systems, structural optimization, and fatigue monitoring for wind turbines. He leads the Offshore Wind Turbine Technology research group, supervising 3 PhD students. Key projects include X-ROTOR (EU Horizon 2020), FLOAWER (MSCA ITN), and DSF ABYSS. He has held leadership roles at the European Academy of Wind Energy and serves on editorial boards for Wind Energy Science and Journal of Ocean and Wave Energy . His work spans advanced simulation techniques, support structure design, and operational optimization of wind farms. Education and Professional Activities: Extensive international collaboration across 16 PhD defenses as external examiner. Competencies include finite element methods, supercomputing, and stochastic dynamics analysis. Active in 8+ international funding programs and 30+ journal reviews. Research Interests: Disruptive offshore wind turbine concepts (e.g., X-shaped rotors, floating systems) Fatigue lifetime extension strategies Massively parallel simulation frameworks Optimization of operations & maintenance (O&M) workflows Recent Publications Trends: Focus on structural reliability, machine learning applications in hydrodynamics, and novel load extrapolation algorithms. Over 50 peer-reviewed articles since 2015, emphasizing interdisciplinary approaches to wind energy challenges. Grants & Labs: Active in EU-funded initiatives like TWEED (2024-), managing research teams in advanced analysis methods and offshore wind system integration.

• Process Control
• Petroleum Engineering
• Optimization Algorithms
• +3 more

Bjarne Foss is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), and Managing Director of the Kavli Institute for Systems Neuroscience. He has been a professor since 1989 and holds a PhD in Engineering Cybernetics from NTNU. His roles include Vice President for Research at NTNU (2017–2020) and Adjunct Professor at the University of South-East Norway (2021–2023). He has extensive board experience with companies like Cybernetica AS, Solution Seeker AS, and KTH Royal Institute of Technology. Foss specializes in process control, reservoir management, and optimization algorithms, with over 300 publications and an H-index of 49. He has supervised 31 PhD students and led major international projects for ESA and EU. Awards include the IBM Open Collaborative Faculty Award (2010, 2011), the Karl Diesel Best Paper Award (1995), and recognition for pedagogical innovation in 2007. His research focuses on enhancing oil production efficiency, automation in energy systems, and control strategies for complex industrial processes. His education includes a PhD from NTNU (Engineering Cybernetics) and an MSc degree awarded with the prestigious «Innstilling» honor. Professional activities include editorial roles, IEEE Senior Membership, and leadership in international committees like IFAC. Foss co-founded Solution Seeker AS (2013), Cybernetica AS (2000), and FIRST Scandinavia (2000). Recent work emphasizes distributed optimization for large-scale oil systems, dynamic control under uncertainty, and shale-gas production strategies. His publications span journals like IEEE Transactions on Automatic Control and Journal of Petroleum Science and Engineering , addressing challenges in reservoir control, model predictive control, and well-placement optimization.

Asieh Abolpour MofradView profile

Researcher

• Machine Learning
• Neural Networks
• Associative Memory
• +7 more

Asieh Abolpour Mofrad is a researcher at the Department of Informatics, University of Bergen (UiB), with a dual PhD background in behavioral sciences and informatics. She is currently engaged in the Retail Fresh project, leveraging machine learning to reduce food waste in the retail sector in collaboration with Link Retail. Previously, she contributed to the DRONE (Drug Repurposing for Neurological Diseases) project at the Department of Global Health and Public Health, UiB, where she applied machine learning to Norwegian health registry data to investigate Parkinson's disease treatments. Her academic credentials include two doctoral theses: one from OsloMet (2021) on the integration of behavior analysis and machine learning for modeling stimulus equivalence, and another from UiB (2021) on clique-based neural associative memories. Her research bridges artificial intelligence, cognitive modeling, neural networks, and public health applications. Her research interests span machine learning, neural associative memory, reinforcement learning, cognitive modeling, health informatics, and drug repurposing. She employs computational frameworks such as projective simulation and tournament-based neural networks to model complex cognitive and biological phenomena. The analysis of her recent publications reveals a consistent focus on machine learning applications in both cognitive science and healthcare. Her work includes modeling stimulus equivalence, designing neural memory architectures, solving stochastic optimization problems, and analyzing large-scale health data for neurological disease risk. These efforts reflect interdisciplinary innovation, combining theoretical computer science with real-world applications in psychology and medicine. Scientific Contributions: Developed the Enhanced Equivalence Projective Simulation (E-EPS) framework for modeling derived relations in behavior analysis. Designed tournament-based neural networks for efficient sequence storage and bidirectional retrieval. Applied machine learning to large-scale health registries to identify drug classes associated with Parkinson’s disease risk. Contributed to adaptive learning systems based on flow theory for educational optimization. She has advised no publicly listed students but collaborates extensively with researchers across institutions. Her work is supported by interdisciplinary research projects and she actively disseminates code via GitHub, particularly in Jupyter notebooks for reproducibility. She is affiliated with research teams in informatics and global health at UiB, contributing to both theoretical and applied machine learning initiatives. Laboratories and Research Teams: Department of Informatics, University of Bergen – Machine Learning and Neural Systems group. DRONE Project – Interdisciplinary team on drug repurposing using AI and health data. Retail Fresh Project – AI for sustainable retail, in collaboration with industry partners.

• Numerical Simulations
• Oceanography
• Environmental Risk Assessment
• +4 more

Tor Nordam is an Associate Professor at the Norwegian University of Science and Technology (NTNU) , affiliated with the Faculty of Natural Sciences and the Department of Physics . While his primary employment is at SINTEF Ocean , he maintains a secondary academic role at NTNU for supervision, research, and occasional teaching. His expertise spans numerical methods for oceanic pollutant transport , Monte Carlo risk assessment , and light scattering from rough surfaces. Education : PhD in Physics from NTNU, focusing on numerical light scattering simulations Teaching : Courses like Advanced Theoretical Physics - Transport modelling with Stochastic Differential Equations and Computational Physics His research emphasizes Lagrangian particle methods , advection-diffusion problems , and environmental applications such as oil spill modeling , microplastics , and climate intervention materials . Recent work includes Bayesian backtracking for ocean drift and AI-driven ocean robotics . The 2025 articles highlight advancements in oceanographic modeling and climate intervention technologies , while 2023-2020 publications address Arctic oil spill dynamics , produced water transport , and biodegradation kinetics . Collaborations with institutions like University of Edinburgh and University of Oslo demonstrate interdisciplinary engagement.