جستجوی استاد راهنمای دانشگاهی

Mohan Lal Kolheمشاهده پروفایل

استاد

• Smart Grid
• Renewable Energy Systems
• Electric Vehicles
• +۵ مورد دیگر

Professor Mohan Lal Kolhe is a distinguished academic at the University of Agder , serving as a Full Professor in Smart Grid and Renewable Energy within the Faculty of Engineering and Science and the Department of Engineering Sciences . With over three decades of international academic experience, he has held positions at prestigious institutions including University College London, University of Dundee, and Hydrogen Research Institute in Canada. His career spans technical innovation, policy development (e.g., as a member of South Australia’s Renewable Energy Board), and extensive research leadership in sustainable energy systems. Research Leadership : Focus on Smart Grid integration, Electric Vehicles, Hydrogen Energy, Solar/Wind Systems, and Techno-Economic Energy Analysis. Global Recognition : Listed in the top 2% of scientists worldwide (2020-2023) by Stanford University, with 10 publications averaging 200+ citations. Recent publications emphasize advanced optimization techniques for renewable integration, EV charging infrastructure, hydrogen production, and power system stability. His work has secured competitive funding from entities like the Norwegian Research Council and EU programs. Awards and Expert Roles : Top 2% Global Scientist (Stanford, 2020-2023) Highly Cited Researcher (Top 10 publications, 200+ avg. citations) Expert evaluator for European Commission, Royal Society London, EPSRC, and Cyprus Research Foundation He actively contributes to international conferences as keynote speaker and editorial board member, with leadership roles in research groups like Autonomous and Cyber-Physical Systems and Energy Systems .

• Wireless Communications
• Machine Learning
• Digital Twins
• +۵ مورد دیگر

Pierluigi Salvo Rossi is a Professor at the Department of Electronic Systems , Norwegian University of Science and Technology ( NTNU ), with additional roles as Deputy Head of Department (since 2021) and Deputy Manager at the Center for Green Shift in the Built Environment (since 2022). He also serves as a part-time Research Scientist at SINTEF Energy's Gas Technology department. Education: Ph.D. in Computer Engineering, University of Naples “Federico II”, Italy (2005) Dr.Eng. (cum laude) in Telecommunications Engineering, University of Naples “Federico II”, Italy (2002) Research Interests span Wireless Communications , Digital Twins , Machine Learning , and Statistical Signal Processing , focusing on applications like Industrial IoT , Fault Detection , and Energy Systems . His recent Publications highlight trends in Federated Learning , Graph Signal Processing , and Multi-Sensor Anomaly Detection across domains from Natural Gas Pipelines to Subsea Leakages . Scientific Awards include: Exemplary Senior Editor, IEEE Communications Letters (2018) Department Ambassador, NTNU (2016) IEEE Senior Member (since 2011) Professional Roles encompass editorial leadership (e.g., IEEE Sensors Journal) and conference organization (e.g., General Chair for IEEE Sensor Array and Multichannel Signal Processing Workshop, 2022). He leads major funded research projects like PREFERENCE (RCN, 2023-2027) and AUTOSHIP (RCN, 2020-2028).

Lokukaluge Prasad Pereraمشاهده پروفایل

استاد

• Maritime Technology
• Offshore Systems
• Advanced Data Analytics
• +۶ مورد دیگر

Lokukaluge Prasad Perera is a Professor in Maritime Technology at UiT The Arctic University of Norway and a Senior Research Scientist in Smart Data at SINTEF Digital . He holds a BSc in Mechanical Engineering from Oklahoma State University (1999), MSc in Systems & Controls from the same institution (2001), and a PhD in Naval Architecture and Marine Engineering from Technical University of Lisbon (2012). His research focuses on Maritime and Offshore Systems , Advanced Data Analytics , Autonomous Navigation , Energy Efficiency , and Digital Twin Applications . He has published over 100 peer-reviewed papers and was recognized in the World's Top 2% Scientists (2021-2022) by Stanford University. Key professional experiences include roles at SINTEF Ocean (2014–2017), Center for Marine Technology and Engineering in Portugal (2008–2012), and Wärtsilä Finland (2012–2014). He has also held academic positions at Naval & Maritime Academy and Ocean University of Sri Lanka . His work addresses challenges in emission reduction , renewable energy integration , and safety-critical systems for maritime operations. Current projects emphasize trustworthiness of autonomous ships and data-driven decision frameworks for energy efficiency.

• Maritime safety management and leadership
• Maritime accident and casualty analysis
• Maritime Autonomous Surface Ships (MASS)
• +۳ مورد دیگر

Tae Eun Kim is an Associate Professor in Maritime Safety Management at UiT The Arctic University of Norway, working within the Department of Technology and Security. Her research, teaching, and industrial collaboration focus on maritime safety and human factors, with particular expertise in maritime safety management, accident analysis, Maritime Autonomous Surface Ships (MASS), and human factors in maritime operations. Dr. Kim's research spans four interconnected domains: maritime safety management and leadership, maritime accident and casualty analysis, Maritime Autonomous Surface Ships (MASS), and human factors in maritime operations. She has developed assessment instruments like the Safety Leadership Self-Efficacy Scale (SLSES) and conducted STAMP-based causal analyses of maritime accidents. Her work on MASS addresses safety challenges in mixed navigational environments and examines leadership competencies for autonomous shipping operations. Her human factors research explores how technological advancements impact navigators' performance, crew dynamics, and safety outcomes, including gender parity issues in the maritime industry. Dr. Kim's publication record reveals a strong focus on the intersection of maritime safety, technology, and human performance. Her recent work increasingly addresses autonomous shipping technologies, with numerous publications on AI decision transparency, learning analytics in maritime simulator training, and multi-modal data analysis for nautical skill development. She has conducted systematic reviews on simulator training approaches and scenario design, contributing significantly to methodology development in maritime education and training. Her research demonstrates a clear trajectory toward integrating emerging technologies with traditional maritime safety practices as the industry transitions toward greater automation. Dr. Kim is actively involved in several significant research projects, including the i-MASTER EU Horizon Europe Research and Innovation Project, the REFRAME project, and the SPRICE project (Multidisciplinary approach for spray icing modelling). She is a member of both the Advanced Maritime Ship Operations research group and the Maritime Safety Science (MARSCI) Research Group, demonstrating her commitment to collaborative research in maritime safety science. Dr. Kim teaches several specialized courses at UiT, including SVF-3206 Safety Management and Accident Investigation, TEK-3014 Navigation Technology, MFA-2100 Maritime Digitalization, MFA-8010 Maritime HTO (Human-Technology-Organisation) and Innovation, and MFA-2018 Maritime Administration and Leadership. Her teaching portfolio reflects the interdisciplinary nature of her expertise, bridging engineering, safety science, and organizational behavior in maritime contexts.

• Electrical Engineering
• Lighting Technology
• Energy Efficiency
• +۶ مورد دیگر

Associate Professor Eilif Hugo Hansen works at the Department of Electrical Energy, Norwegian University of Science and Technology (NTNU). His primary research areas are electrical installations and lighting technology, with a focus on energy efficiency, daylight utilization, and applications in aquaculture. He has contributed to standardization committees like NEK/NK64 and IEC TC64, and co-developed the LYSSTYR lighting control software. Department: Electrical Energy School: Faculty of Information Technology, Mathematics, and Electrical Engineering University: Norwegian University of Science and Technology Education includes a Civil Engineering degree (1985) and Doctorate in Engineering (1990) from NTH (now NTNU). He is a certified electrical contractor with expertise in low-voltage systems. Research Highlights : Over 30 years of publications in lighting technology, electrical safety, and energy systems. Key themes include ground fault detection , non-radial electrical networks , and daylight integration in buildings. His work with Lysforsk center and SINTEF collaborations underscores his industry impact. Teaching involves courses like TET4165 Light and Lighting and TET4170 Electrical Installations . He has supervised over 40 master's theses, including topics on smart lighting , power distribution , and energy-saving technologies . Professional Engagement : Member of NEK/NK64 Low-Voltage Installations committee Contributor to IEC TC64 MT12 standards Former head of the Energy Transformation and Electrical Installations program (1997-2000) Co-developer of the LYSSTYR software for simulating daylight rhythms

Knut Håkon Holeمشاهده پروفایل

دانشیار

• Radiology
• Oncology
• Medical Imaging
• +۴ مورد دیگر

Knut Håkon Hole is an Associate Professor at the University of Oslo's Department of Radiology and Nuclear Medicine. His research focuses on diagnostic imaging applications in oncology, particularly in prostate and rectal cancers. He specializes in MRI, PET, and radiogenomics techniques to assess tumor biology, treatment response, and recurrence. Expertise: Prostate cancer imaging, neoadjuvant therapy response, tumor hypoxia, and imaging biomarkers Key affiliations: Oslo University Hospital (Rikshospitalet), Radium Hospital Research interests include: Developing MRI and PET protocols for cancer staging and recurrence detection Integrating imaging with genomic data (radiogenomics) Optimizing therapeutic approaches using imaging biomarkers Recent work highlights: Prostate cancer radiogenomics and hypoxia biomarkers (2024) MRI/PET comparisons for tumor localization (2021-2023) Neoadjuvant therapy response assessment in rectal and breast cancers (2020-2023) Publications span over 50 peer-reviewed articles with a focus on translational imaging research. Collaborates extensively with oncology and urology teams.

Egor Kostylevمشاهده پروفایل

دانشیار

• Foundations of AI
• Symbolic AI
• Description Logics
• +۹ مورد دیگر

Egor Kostylev serves as an Associate Professor in the Department of Informatics within the Faculty of Mathematics and Natural Sciences at the University of Oslo. His research focuses on the theoretical foundations connecting symbolic and sub-symbolic artificial intelligence, particularly examining relationships between formal logic systems and machine learning approaches. His educational background includes an MSc (Specialist, 2005) and PhD (Candidate, 2009) from Lomonosov Moscow State University under Prof. Vladimir A. Zakharov. He subsequently held research positions at the University of Edinburgh (2010-2013) and the University of Oxford (2013-2020) before joining the University of Oslo in 2020. Kostylev's research interests center on bridging symbolic AI formalisms with sub-symbolic approaches. He investigates connections between various logics (Description Logics, Temporal Logics, Datalog), query languages (SPARQL, Regular Path Queries, OTTR), and machine learning formalisms (Graph Neural Networks, Markov Logic Networks). His work addresses critical challenges in Explainable, Trustworthy, and Green AI through theoretical foundations that connect different AI paradigms. His publication record demonstrates consistent high-impact contributions in theoretical computer science and AI, with numerous publications in top venues including AAAI, LICS, Journal of the ACM, and ICLR. His recent work shows a clear trajectory toward unifying logical reasoning with neural network approaches, particularly through graph neural networks and their connections to logical formalisms. The research spans theoretical foundations of knowledge representation, temporal reasoning in knowledge bases, and the logical expressiveness of modern neural architectures. As a research leader, Kostylev supervises multiple PhD students including Shuwen (Aurora) Liu, Maximilian Pflüger, Roxana Pop, Dongzhuoran Zhou, and Erik Snilsberg. He serves as a Research Theme Leader for the Integreat SFF: Norwegian Centre for Knowledge-driven Machine Learning. His teaching responsibilities include IN3020/4020 Database Systems courses. He leads the Data and Knowledge Management (DKM) research group at the University of Oslo, which focuses on foundational aspects of knowledge representation, database theory, and the intersection with modern machine learning techniques. The group actively collaborates with international researchers and contributes to advancing theoretical understanding of how symbolic and neural approaches to AI can complement each other.

Lars Kristiansenمشاهده پروفایل

استاد

• Mathematical Logic
• Computability Theory
• Complexity Theory
• +۴ مورد دیگر

Lars Kristiansen is Professor II (part-time Professor) at the Department of Mathematics, University of Oslo . His research centres on the intersection of mathematical logic, computability theory, and computational complexity, with recent emphasis on computable analysis, weak first-order theories, subrecursive degree structures, and implicit computational complexity. Research Interests Computable Analysis: Representations and computational complexity of irrational and real numbers. Weak First-Order Theories: Decidability, interpretability and fragments of concatenation theories. Subrecursive Degree Theory: Fine structure of honest subrecursive degrees and the Grzegorczyk hierarchy. Implicit Computational Complexity: Reversible computing, type systems that capture complexity classes, and resource-bounded program analysis. Across more than 50 refereed publications since 1996, a clear trend emerges: an early focus on subrecursive hierarchies and honest degrees evolved into an intensive study of the computational content of real number representations and the logical strength of weak arithmetics. Recent work (2023-2025) deepens this agenda, analysing the complexity of converting between alternative representations of reals and the degree structures that these induce. Scientific Output While no named awards are listed, Kristiansen’s contributions are disseminated in top venues such as Bulletin of Symbolic Logic , Annals of Pure and Applied Logic , Archive for Mathematical Logic , Science of Computer Programming , and leading LNCS conferences. His work is frequently co-authored with Amir Ben-Amram, Jakob Grue Simonsen, Juvenal Murwanashyaka, Ivan Georgiev, and Neil D. Jones, indicating active collaborative networks in both logic and theoretical computer science. Research Groups & Collaborations Logic research group at the University of Oslo Data and Knowledge Management (DKM) group

Chong-Yu Xuمشاهده پروفایل

استاد

• Hydrological Modelling
• Climate Change
• Regional Evapotranspiration
• +۴ مورد دیگر

Prof. Chong-Yu Xu is a Professor of Hydrology at the University of Oslo's Department of Geosciences, affiliated with the Section for Geography and Hydrology (GeoHyd). He has held this position since 2005, having previously served as an Associate Professor at Uppsala University (1998–2005) and Assistant Professor (1994–1998). His research focuses on hydrological modeling, climate change impacts, regional evapotranspiration, and uncertainty analysis. He teaches courses such as GEO4310 (Stochastic Methods in Hydrology) and GEO4320 (Hydrological Modelling). Education: BSc in Hydrology (Nanjing University, 1978–1982), MSc in Regional Hydrological Modeling (Free University Brussels, 1986–1988), and PhD in Hydrological Modelling (Free University Brussels, 1988–1992). He has been honored with prestigious awards, including the NHF Lifetime Achievement Award (2022) and IWA Publishing Award (2022). He serves as an honorary professor at institutions like Hohai University and is a doctoral supervisor at multiple universities. His research spans global, regional, and local hydrological modeling, with a focus on climate change adaptation and water resource management. He leads projects such as the NORHED-II initiative on climate change and ecosystem management in Malawi and Tanzania. His work bridges theoretical hydrology with practical applications, including flood risk reduction and hydropower optimization. Publications highlight advancements in hydrological extremes, non-stationary drought assessment, and AI-driven flood prediction. Collaborative efforts with international networks like the Nordic Hydrological Association underscore his global impact in hydrological sciences.

Per-Arne Andersenمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Reinforcement Learning
• Tsetlin Machines
• +۷ مورد دیگر

Per-Arne Andersen is an Associate Professor at the Department of Information and Communication Technology within the University of Agder . His research focuses on artificial intelligence , reinforcement learning , Tsetlin machines , and deep learning , with applications in real-time strategy games , industrial environments , and IoT systems . Projects: RESTORE Research Groups: CAIR - Center for Artificial Intelligence Research, CIEM - Center for Integrated Crisis Management, Intelligent Mechatronics (iTron) His work explores safe and sustainable reinforcement learning , interpretable AI , and generative environment modeling . He has developed frameworks like CaiRL and CostNet for high-performance RL environments and goal-directed learning. Recent publications include advancements in Tsetlin automaton analysis , GNSS jamming classification , and road quality detection . Articles from 2025-2016 span machine learning , computer vision , and environmental modeling . He contributes to IEEE , Springer , and LNCS publications, with a focus on interdisciplinary AI applications in crisis management , cybersecurity , and industrial optimization .

• Scientific Computing
• Numerical Analysis
• Applied Mathematics
• +۵ مورد دیگر

Eirik Valseth is an Associate Professor of Scientific Computing at the Norwegian University of Life Sciences (NMBU), Department of Data Science. He holds concurrent roles as a research associate at the Oden Institute, University of Texas at Austin, and an affiliated researcher at Simula Research Laboratory (Department of Numerical Analysis and Scientific Computing). His expertise lies in advanced finite element methods for PDEs with applications in flood modeling and hydropower systems. Current Affiliation: NMBU (Norwegian University of Life Sciences) Secondary Affiliations: Oden Institute (UT Austin), Simula Research Laboratory Research interests span numerical methods for challenging PDE systems, including: Stabilized finite element formulations Hurricane storm surge and riverine flood modeling Hydropower infrastructure analysis Computational mechanics and applied mathematics His recent publications (2024–2025) emphasize flood risk assessment (compound flooding, dam breaks, dredging impacts), advanced numerical methods (isogeometric analysis, stochastic finite elements, graph-grammar algorithms), and environmental applications (pollution transport, pathogen distribution, mosquito population dynamics after hurricanes). Key trends include cross-disciplinary integration of physics-aware machine learning and robust hydrodynamic simulation tools. Valseth's work extends to software development (e.g., WAVEx for spectral wave models, SWEMniCS for coastal circulation) and large-scale modeling frameworks like the ADCIRC unstructured mesh model for US coasts. Collaborative projects involve institutions such as University of Texas at Austin, Simula, and NOAA.

• Maritime Engineering
• Digital Twin Technology
• Marine Robotics
• +۳ مورد دیگر

Hans Petter Hildre is Head of Department at the Department of Ocean Operations and Civil Engineering , part of the Faculty of Engineering at the Norwegian University of Science and Technology (NTNU). His work focuses on maritime engineering, digital twin technology, and marine operations. Research interests include: Digital Twin Applications in Maritime Industry Offshore Operations and Wind Turbine Installation Marine Robotics and Autonomous Systems Wave Field Estimation and Environmental Load Analysis Human-Machine Interaction in Maritime Contexts Co-simulation and Real-time Monitoring Recent publications highlight trends in: Wave shielding effects for offshore vessels Knowledge transfer from automotive/aviation to maritime Crane path planning using digital twins Visual attention zone recognition systems Hydrodynamic modeling and sensitivity analysis Smart city-maritime integration Scientific collaborations span institutions including: European Commission (Future Skills Reports) Royal Institution of Naval Architects The American Society of Mechanical Engineers (ASME) IEEE Transactions on multiple domains Springer Publishing

• Power Systems
• Smart Grids
• Reactive Power Control
• +۳ مورد دیگر

Charu Sharma is an Associate Professor in the Department of Electrical Engineering at UiT The Arctic University of Norway, specializing in power systems and smart grid technologies. Her work focuses on reactive power control, voltage stability, and optimization of renewable energy-integrated networks. Research on cyber-physical co-simulation frameworks for real-time grid management Development of hybrid renewable energy microgrids for rural and industrial applications Expertise in optimization algorithms (e.g., BFOA-PSO, ANFIS) for energy systems Recent publications highlight her contributions to DER-enriched distribution networks, low-inertia system stability, and intelligent load frequency control. She actively collaborates with researchers on projects like Cooperative Isolated Renewable Energy Systems and arcICE , addressing reliability and sustainability challenges.

• Internet of Things
• Fog Computing
• Edge Computing
• +۹ مورد دیگر

Amirhosein Taherkordi is a Professor in the Networks and Distributed Systems group at the Department of Informatics, University of Oslo, Norway. His research focuses on resource-efficiency, scalability, adaptability, dependability, mobility and data-intensiveness of distributed systems for emerging computing technologies including Internet of Things (IoT), Fog/Edge/Cloud Computing, and Cyber-Physical Systems (CPS). Dr. Taherkordi received his Ph.D. from the Informatics Department at the University of Oslo under the supervision of Prof. Frank Eliassen, with his thesis titled "Programming Wireless Sensor Networks: From Static to Adaptive Models." He holds an M.Sc. in Information Technology Engineering (Software Engineering) from University of Science and Technology and a B.Sc. in Computer Engineering from Sharif University of Technology. His research spans multiple domains of distributed systems with emphasis on practical applications. He investigates energy efficiency in wireless sensor networks, communication optimization in IoT systems, and adaptive resource allocation in edge computing environments. His work addresses critical challenges in network traffic classification, federated learning for vehicular networks, and data processing across heterogeneous platforms. Analysis of his recent publications reveals a strong trajectory toward communication-efficient federated learning techniques for vehicular networks, energy-aware protocols for IoT data collection, and advanced machine learning approaches for network traffic analysis. His research consistently focuses on optimizing resource usage while maintaining system performance and privacy in distributed architectures. Dr. Taherkordi actively contributes to several research initiatives including the CPS Lab at UiO for Cyber Physical Systems, DILUTE: Fluid Service Abstraction for Large-Scale Cloud IoT Systems, and the Gemini Centre on IoT at UiO. His work bridges theoretical advances with practical implementations in transportation systems, environmental monitoring, and industrial automation.

• Control Systems
• Model Predictive Control
• Power Systems Engineering
• +۳ مورد دیگر

Morten Hovd is a Professor in the Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU). His research focuses on advanced control systems, model predictive control (MPC), power electronics, and optimization algorithms. He has contributed significantly to the development of robust control strategies for uncertain systems and has published extensively in leading journals and conferences in the field of control engineering. His research interests span several key areas in control systems engineering, including model predictive control, nonlinear control systems, optimization under uncertainty, and applications in power systems and energy efficiency. He is particularly known for his work on discrete-time bilinear systems, modular multilevel converters (MMCs), and the integration of machine learning techniques with control theory. His contributions address both theoretical advancements and practical implementations in industries such as energy and petroleum engineering. Hovd's recent publications highlight advancements in energy-efficient control systems, stochastic surrogate modeling for subsurface flows, and optimization algorithms tailored for complex engineering problems. His work often combines rigorous mathematical frameworks with real-world applications, such as improving the reliability of power systems and enhancing reservoir management through data-driven methods. He is actively involved in teaching courses such as TTK4210 (Advanced Control of Industrial Processes) and TK8118 (Mini-seminar in Cybernetics). His research has led to innovations in fault detection for power systems, energy-efficient building climate control, and robust MPC strategies for uncertain systems.