Pankaj Pandey is a Senior Research Scientist at the Norwegian University of Science and Technology (NTNU), affiliated with the Department of Information Security and Communication Technology under the Faculty of Information Technology and Electrical Engineering. He holds an LLM in International Law from the University of Sunderland, UK, and dual PhDs in Information Security (NTNU) and Applied Economics (University of Antwerp). He is certified in ISO standards including ISO 42001 (Lead Implementer), ISO 31000 (Lead Risk Manager), and ISO 27001 (Lead Auditor). He leads the European Commission-funded ENFIELD project (€11M), focusing on Trustworthy and Green AI. His research spans cybersecurity, blockchain applications, AI governance, cyber-physical systems, and financial instruments for risk management. Key projects include CYBERUNITY (cyber range federation), DELTA (smart grid security), and GHOST (IoT risk control). He has published extensively in journals like Frontiers in Energy Research and Lecture Notes in Computer Science , with a focus on AI, blockchain, and energy systems. His work integrates technical and legal frameworks, emphasizing digital sovereignty and ethical AI. Pandey collaborates internationally on standards development and advises EU projects like TRIANGLE (5G benchmarking) through HSbooster.eu. His expertise bridges technical innovation with regulatory compliance, driving societal impact in digital governance and resilient infrastructure.
Torill Ueland is an Associate Professor at the Department of Psychology, University of Oslo, affiliated with the Faculty of Social Sciences. Her research focuses on cognitive psychology, neuroscience, and neuropsychology, particularly exploring long-term recovery in first-episode psychosis, cognitive impairments in mental disorders, and inflammatory mechanisms in schizophrenia and bipolar disorders. Her work emphasizes qualitative studies on patient experiences and quantitative investigations into biomarkers such as cortisol, CRP, and telomere length. She collaborates on projects analyzing immune system dysregulation and genetic correlations in neurodevelopmental and psychiatric conditions. Current research also includes longitudinal studies on brain structure changes over time in psychosis patients. Notable trends in her publications highlight interdisciplinary approaches combining clinical psychology with neuroimaging, genetic analysis, and immunology. She explores how cognitive growth charting and normative models can improve early diagnosis and treatment strategies. Her work often addresses translational challenges in biomedical research, such as handling missing data and applying machine learning to cognitive trajectory analysis. Ueland contributes to advancing understanding of recovery pathways in severe mental illnesses, focusing on both patient-reported outcomes and biological mechanisms underlying symptomatology and functional impairment.
George Cherry is a Doctoral Research Fellow at the Rosseland Centre for Solar Physics, University of Oslo. His work focuses on magnetic flux emergence, MHD simulations, and applying Fortran and machine learning to astrophysical problems. He holds an MMath in Mathematics from the University of St Andrews (2022), with a thesis on volcanic plume dynamics and magnetic flux transport simulations. Research Interests: Magnetic flux emergence mechanisms in the solar atmosphere Development of numerical models for thermal conduction in MHD codes like Bifrost Application of machine learning to astrophysical simulations Plume dynamics in solar chromosphere and corona His recent publications address thermal conduction models in solar atmosphere simulations, contributing to both computational methods and analytical solutions for nonlinear phenomena. These studies advance understanding of energy transfer processes in magnetohydrodynamic environments. Awards: University of St Andrews Principals Medal (2022) Cherry's research is supported through participation in collaborative projects at UiO's Institute of Theoretical Astrophysics. He is actively involved in code development for solar physics simulations and has presented at international astrophysics conferences.
Erlend Ignacio Fleck Fossen is a Research Fellow at the Oslo Centre for Biostatistics and Epidemiology (OCBE), Department of Biostatistics, University of Oslo, where he develops statistical and machine learning methods to predict cancer outcomes and quality-of-life changes in head and neck cancer patients. His multidisciplinary background bridges evolutionary biology and clinical bioinformatics. Education: PhD in Biodiversity Dynamics, Norwegian University of Science and Technology (NTNU), 2014-2018 MSc in Biology (Evolution & Biosystematics), NTNU, 2012-2014 BSc in Biology (Ecology & Evolution), NTNU, 2009-2012 Research Focus: Fossen's work integrates biostatistics, machine learning, and evolutionary biology. He specializes in predictive modeling of cancer survival/recurrence, thermal adaptation mechanisms, genetic constraints in metabolic scaling, and host-parasite dynamics under climate variability. His research employs diverse models from Daphnia to clinical oncology datasets. Publication Trends: Fossen's recent articles (2019-2023) reflect a transition from fundamental evolutionary ecology (thermal adaptation, metabolic scaling) to applied medical bioinformatics (cancer outcome prediction). His work consistently demonstrates strong quantitative methodologies including survival analysis, reaction norm modeling, and machine learning applications. Affiliations & Collaborations: Member of international consortia including BD4QoL and SuPerTreat projects. Previous affiliations include Uppsala University's Evolutionary Biology Centre (2019-2021) and KU Leuven's aquatic ecology lab (2017). Contributes to open-source bioinformatics tools through GitHub repositories.
Kosio Beshkov is a Postdoctoral Fellow in Condensed Matter Physics at the University of Oslo, specializing in the intersection of topological data analysis and machine learning. His research focuses on theoretical frameworks for understanding neural network representations and biological neural systems. His primary research interests include: Theory of deep neural networks in overparametrized regimes Topological data analysis of neural manifolds De novo protein design using evolutionary algorithms and geometric modeling Connections between network representations and topological spaces Recent publications demonstrate strong trends in computational neuroscience, with 7 papers from 2021-2025 spanning journals like PLoS Computational Biology and iScience. His work consistently applies polyhedral geometry, quotient spaces, and homology to neural representation problems, while expanding into protein language models and gene therapy applications. Current technical approaches combine: Topological data analysis for high-dimensional neural data Geometric deep learning for robust representations Biophysically-detailed neuron modeling Protein structure-geometry relationships
Keshav Prasad Paudel is a Professor at the Norwegian College of Fishery Science, UiT The Arctic University of Norway, with a focus on GIS and marine resource management. His career spans academic research, teaching, and technical roles in environmental studies. Education : PhD in Geography (2011), M.Phil in Mountain Ecology (2006) from University of Bergen; MA (2001) and BA (1999) in Geography/Economics from Tribhuvan University. His research integrates GIS/Remote Sensing with socio-ecological system analysis, emphasizing coastal zone planning, marine spatial policy, and climate change impacts on Himalayan and Arctic systems. Recent publications highlight ecosystem services mapping , fisheries data modeling , and spatio-temporal marine activity analysis . He contributes to the Marine Resource Management (MARA) group and leads the PhD project Use of Geodata in Coastal Zone Management , combining academic rigor with practical applications. His teaching portfolio includes basic pedagogical competence (2018–2019) and a focus on student-centered learning .
Jinmei Lu is a Professor in the Department of Technology and Security at UiT The Arctic University of Norway, actively contributing to research and teaching in environmental and technological safety domains. Her primary affiliation is with the Sustainable Technology and Safety (STS) research group and the nICE project, based in Tromsø. Her research interests focus on Environmental Risk Analysis and Assessment , Waste Management , Environmental Pollution Monitoring , and Environmental Pollution Prevention and Mitigation . Her work employs advanced modeling and statistical techniques to understand the environmental impacts of mining, shipping, and climate change in the Arctic. The 15 most recent publications reveal a strong trend in applying hydrological and reliability models to Arctic environmental challenges. Key themes include the leaching of heavy metals from mine tailings under varying temperatures, atmospheric emissions from Arctic shipping, hydrological modeling in permafrost regions, and reliability analysis of engineering systems. The research utilizes methods from machine learning, statistical modeling, and environmental simulation. Her scientific contributions are disseminated through high-impact journals in environmental science, engineering, and sustainability. Jinmei Lu is an active advisor and collaborator, with a long-standing research partnership with Fuqing Yuan and other colleagues. Her work is supported by institutional affiliations and research projects focused on Arctic sustainability. She teaches courses such as SVF-1201 (Environmental Vulnerability in the Arctic), SIK-2004 (HSE Risk Analysis and Management), and SVF-3207 (Resilience). She is a key member of the Sustainable Technology and Safety (STS) research group and the nICE project, which are central to her collaborative research on Arctic environmental and engineering challenges.
Indika Anuradha Mendis Balapuwaduge is a Researcher at the Department of Information and Communication Technology , University of Agder , Norway. He previously served as a Senior Lecturer at the University of Ruhuna (2020-2022) and held postdoctoral and PhD positions at the University of Agder (2017-2020 and 2012-2016). Education PhD in ICT (University of Agder, 2016) MSc in ICT (University of Agder, 2012) BSc in Electrical and Information Engineering (University of Ruhuna, 2008) Research Interests : Focus on Wireless Communication with expertise in Cognitive Radio Networks , Ultra-Reliable Communication , and Applied Machine Learning . His work integrates Dependability Theory and Stochastic Process Modeling to address challenges in 5G/6G Networks , IoT , and Network Slicing . Recent Publications include studies on Electric Vehicle Charging Optimization , Secure IoT Protocols , and 5G Network Slicing . He has contributed to IEEE Transactions and Springer publications, emphasizing Dynamic Spectrum Allocation and Machine Learning Applications .
Baltasar Enrique Beferull Lozano is a tenured Professor at the University of Agder , leading the Center Intelligent Signal Processing and Wireless Networks (WISENET) since 2015. With a PhD in Electrical Engineering from USC (2002) and prior roles at EPFL, AT&T Shannon Labs, and University of Valencia, his career spans 20+ years of academic and industrial research in signal processing, wireless systems, and AI. Education: PhD (USC), MSc (USC), MSc (University of Valencia) Expertise: Data Science, Machine Learning, Graph Signal Processing, Cyber-Physical Systems His research focuses on AI-driven wireless networks and in-network collective intelligence , addressing fundamental and applied challenges in smart water systems , energy management , and next-gen 5G/6G . He has secured 20+ international projects including 10 EU-funded initiatives (HYDROBIONETS, SENDORA) and 5 RCN-funded projects. Recent publications emphasize dynamic graph learning from time series data, quantized graph filters , and multi-agent reinforcement learning for networked environments. Awards include IEEE Best Paper Awards (2012, 2021), TOPPFORSK Grant (2015), and Ramón y Cajal Program Rank #1 (2005). As a Senior IEEE Member , he serves as Area Editor for IEEE Transactions on Signal Processing and evaluates research proposals for the European Commission , NSF , and Qatar National Research Fund . His lab has produced 15 PhD graduates and collaborates with 12+ industry partners including Telenor, IBM, and SINTEF.
Odd Torleiv Furnes is an Associate Professor at the Department of Arts Education , part of the Western Norway University of Applied Sciences (HVL) . His research focuses on the intersection of music, cognition, and technology, with a particular emphasis on emotional awareness, deep learning, and musical aesthetics. Research Interests: Music cognition Musical aesthetics Deep learning in music AI in creative processes The articles authored by Furnes highlight trends in the integration of artificial intelligence with music education, emotional resilience through musical expression, and interdisciplinary approaches to understanding music’s cognitive and affective dimensions. His work bridges traditional musical theory with advanced computational methods, offering insights into how technology can enhance creative and educational practices.
Thien Phuoc Nguyen serves as a Research Fellow in the Department of Engineering Sciences at the University of Agder, Norway, based in office H4025 at Jon Lilletuns vei 9, Grimstad. Contact is available via email thien.p.nguyen@uia.no or telephone +47 37233774. His research centers on intelligent monitoring systems, leveraging artificial intelligence and machine learning to develop real-time analytical frameworks for complex environments. This work integrates sensor network deployment, data analytics pipelines, and cyber-physical system design to enable predictive decision-making in engineering contexts. As an active researcher within the Intelligent Monitoring group, his contributions advance applied AI methodologies while addressing practical challenges in system monitoring and data interpretation.
Svein Olav Glesaaen Nyberg is an Associate Professor at the Department of Engineering Sciences , University of Agder , Norway. He has been affiliated with the university since 2003, contributing to research and education in Bayesian statistics, probability theory, and stochastic processes. His work spans interdisciplinary domains including Artificial Intelligence , Biomechatronics , and Collaborative Robotics , alongside Physics and Applied Mathematics . Education: PhD (University of Oslo, 1996), MSc (University of Oslo, 1991). Prior Roles: Postdoc at University of Edinburgh (1996-98), Senior Knowledge Engineer at Computas AS (1998-2002), and teaching positions at Volda University College (2002-03). His research integrates Bayesian statistical inference with practical applications in energy systems, robotics, and materials science. Recent work includes modeling human trajectories in industrial settings using Ornstein-Uhlenbeck processes , EEG-EMG signal fusion for rehabilitation robotics, and Bayesian analysis of power grid stability . He has also published on ecological trends in Norwegian smooth snakes, linking climate change to reproductive behaviors. Scientific Awards and recognitions include authoring the textbook The Bayesian Way: Introductory Statistics for Economists and Engineers (2018) and its Norwegian counterpart Statistikk - en bayesiansk tilnærming (2016). He holds a certificate in university pedagogy (UniPed, 2009).
Assistant Professor in the Faculty of Engineering and Science at University of Agder, Norway. Active researcher in machine learning, smart grids, and wireless communication systems with specific focus on energy applications and IoT technologies. Current institution: University of Agder Email: surender.redhu@uia.no Research Interests: Specializes in applying advanced machine learning techniques to energy systems and IoT networks. Works on electricity price forecasting models, smart home energy optimization with cognitive data fusion, and wireless sensor management in constrained environments. Recent Article Trends: 15 publications from 2021-2023 show consistent focus on temporal-spatial neural networks for energy prediction, UAV-based IoT powering strategies, and biomedical signal interpretation with deep learning systems.
Surya Teja Kandukuri is a Researcher at the Department of Engineering Sciences at the University of Agder, Norway. His work focuses on fault diagnosis, prognostic health management, and control systems for renewable energy applications, particularly wind and hydroelectric power systems. Education: PhD in Mechatronics, University of Agder (2014-2018) MSc in Systems and Control, Delft University of Technology, Netherlands (2003-2006) B.Tech in Electrical and Electronics Engineering, Nagarjuna University, India (1999-2003) Research Interests: Dr. Kandukuri specializes in model-based fault diagnosis and prognostic system health management for complex engineering systems. His research integrates system identification, estimation, and control theory with advanced machine learning techniques to develop predictive maintenance solutions for renewable energy infrastructure. He has particular expertise in wind turbine systems, where he has developed innovative approaches for monitoring pitch systems, detecting electrical faults in induction motors, and assessing performance degradation over time. His work extends to hydroelectric power plants through the PHMHydro project, where he applies similar health monitoring principles to water turbine systems. The integration of physics-based models with deep learning frameworks represents a key innovation in his research approach, enabling more accurate and timely fault detection in critical infrastructure. Publications Trends: Dr. Kandukuri's publication record demonstrates a consistent focus on health monitoring systems for renewable energy infrastructure. His recent work (2022-2025) shows increasing integration of deep learning techniques with traditional signal processing methods for fault diagnosis. There's a clear progression from wind turbine systems to broader applications in hydroelectric power, reflecting expanding research scope. His collaborations span multiple countries and institutions, particularly with Norwegian and international research partners. Research Groups: Intelligent Mechatronics (iTron) Intelligent Monitoring Projects: Performance and Health Monitoring for Hydroelectric Powerplants (PHMHydro)
Henrik Kalisch is a Professor of Applied Mathematics at the Department of Mathematics, University of Bergen, where he also serves as Deputy Head of Department. His research focuses on mathematical modeling of nearshore processes, wave breaking, surfzone circulation, and wave hazards in coastal zones. Dr. Kalisch received his Ph.D. in 2001 from the University of Texas at Austin. His academic career has established him as a leading researcher in fluid mechanics, partial differential equations, and numerical analysis, with over one hundred scientific publications to his name. Professor Kalisch's research spans several key areas in applied mathematics and fluid dynamics. His work on surface water waves investigates fluid particle motion, wave breaking mechanisms, wave shoaling processes, and the influence of vorticity on wave dynamics. In the domain of wave-ice interaction , he studies moving loads on ice sheets, marginal ice zone dynamics, and interactions with internal waves. His contributions to hyperbolic conservation laws include work on singular solutions and their physical interpretation, while his research on mathematical properties of model equations examines existence, uniqueness, and stability of traveling waves and soliton interactions. His research has practical applications in wave energy devices, tidal energy, carbon storage, and ice road safety. His recent publications reveal a strong focus on developing and analyzing mathematical models for wave phenomena, particularly Boussinesq-type models, KdV equations, and their variants. The research shows increasing integration of computational methods with theoretical analysis, and growing attention to practical applications in coastal engineering and polar science. There's also a notable trend toward interdisciplinary collaboration, particularly with oceanographers and engineers working on real-world wave problems. Professor Kalisch serves as co-editor-in-chief for "Water Waves: An interdisciplinary journal," published by Birkhäuser-Springer-Nature, demonstrating his leadership in the field. Methods for real-time wave forecasting and phase control of wave energy converters (Bergen Universitetsfond, 2021-2022) MegaRoller (European Commission Horizon 2020 grant) Norwegian Research Network in Mathematical Models in Geophysical Flows (Research Council of Norway, 2016-2019) Internal Waves in the Marginal Ice Zone (Hydralab grant from European Commission) Nonlinear PDE in Spaces of Analytic Functions (Research Council of Norway, 2012-2017) Wavemaker (Research Council of Norway, 2006-2010) Professor Kalisch has supervised numerous graduate students, including current PhD candidates Enrique Martinez, Olufemi Ige, and Anders Norevik, as well as several Master's students. His former PhD students include Maria Bjørnestad (2021), Evgueni Dinvay (2019), Vincent Teyekpiti (2018), and others who have gone on to careers in academia, industry, and research institutions worldwide. He has chaired curriculum committees and developed courses in applied mathematics, fluid mechanics, and numerics at both undergraduate and graduate levels. His research group at the University of Bergen includes postdoctoral researchers like Bashar Khorbatly, adjunct professors like Francesco Lagona, PhD students, and Master's students working collaboratively on various aspects of wave dynamics and mathematical modeling.
