جستجوی استاد راهنمای دانشگاهی

Svitlana Rogovchenkoمشاهده پروفایل

استاد

• Differential Equations
• Impulsive Differential Equations
• Asymptotic Methods in Nonlinear Mechanics
• +۳ مورد دیگر

Svitlana Rogovchenko is a Professor at the Department of Engineering Sciences, University of Agder, Norway. Her academic work bridges pure mathematical research in differential equations with innovative pedagogical approaches in engineering and interdisciplinary education. Educational Background: Doctor of Philosophy in Differential Equations (1988), Institute of Mathematics, National Academy of Sciences, Kyiv, Ukraine Master of Science in Mathematics (1983), Kyiv State University, Ukraine Research Interests: Qualitative theory of ordinary and impulsive differential equations, asymptotic methods in nonlinear mechanics, mathematical modeling, and the intersection of mathematics education with engineering and biology curricula. She focuses on conceptual understanding, pedagogical challenges, and commognitive conflicts in learning differential equations. Recent Publications Trends: Her work spans mathematical modeling of vehicle crashworthiness, interdisciplinary education for biologists and engineers, and pedagogical innovations in ordinary differential equations. Articles emphasize assessment design, group work tensions, and bridging mathematical theory with applied contexts.

• Hydrology
• Environmental Impact Assessment
• Cold Climate Hydrology
• +۴ مورد دیگر

Knut Tore Alfredsen is a Professor in the Department of Civil and Environmental Engineering at NTNU. His research focuses on cold climate hydrology, environmental impacts of hydropower, river ice dynamics, and water resource management. He leads projects like Trygg Elv (flood detection tools) and Hydro Connect (climate change mitigation in hydropower). He supervises numerous PhD and master’s students and teaches courses in hydrology, hydropower engineering, and environmental design. His work combines field measurements, data analysis, and advanced modelling, often using LiDAR and remote sensing technologies. Key projects include Sagelva research catchment studies and evaluations of environmental impacts from hydropeaking and reservoir operations. He is an active contributor to international conferences and collaborates with organizations like NVE and the IAHR. Education: Not explicitly stated in the provided text. Affiliations: HydroCen, Center for Renewable Energy (FME), NTNU’s Civil and Environmental Engineering Department. Grants: Involved in projects like HydroFlex (turbine development) and Klima 2050 (runoff estimation). Awards: None explicitly mentioned. Labs/Teams: Leads research groups in ecohydraulics, river modelling, and cold climate hydrology. Research Highlights: Focuses on ice-jam flood hazards, hydropeaking effects on fish, and sustainable hydropower practices. Recent work includes LiDAR-based river bathymetry and historical river development analysis using AI.

• Computational Fluid Dynamics (CFD)
• Wave Hydrodynamics
• Wave Modeling
• +۹ مورد دیگر

Hans Bihs is a Professor in the Department of Civil and Environmental Engineering, Faculty of Engineering. His research focuses on computational fluid dynamics (CFD), wave hydrodynamics, and wave-structure interaction using the open-source framework REEF3D. Key Research Areas: CFD simulations, wave modeling, floating body dynamics, ocean wave energy, aquaculture hydrodynamics, sediment transport, and high-performance computing. Projects: ERC Consolidator Grant PARTRES (2023-2028), EEA Grants Portugal SurfWave (2023), NFR KPN IPIRIS (2021-2025), EEA Baltic SolidShore (2021-2024), NTNU's MAPLE (2022-2025), and DigiCoast (2021-2024). Email: hans.bihs@ntnu.no His recent publications (2025-2020) analyze fluid-structure interaction, ship-induced waves, floating offshore wind turbines, submerged vegetation, and coastal structures using advanced CFD techniques. Topics include wave hydrodynamics, turbulence, and numerical modeling for marine and aquaculture systems.

Ali Ramezani-Kebryaمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Federated Learning
• +۹ مورد دیگر

Ali Ramezani-Kebrya is an Associate Professor with tenure in the Department of Informatics at the University of Oslo (UiO), where he leads research in machine learning theory. He holds dual Principal Investigator roles at the Norwegian Center for Knowledge-driven Machine Learning (Integreat) and SFI Visual Intelligence, and is an active member of the European Laboratory for Learning and Intelligent Systems (ELLIS) Society. His service includes Area Chair positions for NeurIPS and AISTATS, and Action Editor for Transactions on Machine Learning Research. His research focuses on theoretical foundations of deep learning with emphasis on understanding input data distribution encoding in neural network layers. Key themes include minimizing statistical risk under resource constraints, addressing distribution shifts in distributed settings, and developing practical tools for robust federated learning. Current applications span emotion recognition, marine data analysis, and neuroscience, reflecting his commitment to real-world machine learning challenges as evidenced by his FRIPRO-funded Machine Learning in Real World (MLReal) project. Recent publication trends reveal three dominant threads: (1) label/covariate shift mitigation in distributed systems through entropy regularization and density ratio estimation; (2) communication-efficient optimization via layer-wise quantization and adaptive compression techniques achieving 150% speedups; and (3) robustness guarantees against tailored attacks and distribution shifts. These works consistently bridge theoretical bounds with empirical validation across domains from GAN training to federated settings. Scientific recognition includes: FRIPRO Grant for Early Career Scientists (2025) for MLReal project SFI Visual Intelligence Spotlight Publication award (2023) for federated learning work He actively mentors 11 graduate students across Oslo and Tromsø universities, with recent PhD placements at Apple and NVIDIA. Current grant portfolio features the FRIPRO Early Career award and leadership roles in two major Norwegian research centers. His lab maintains strong industry collaborations through Vector Institute and EPFL, with recent hiring for PhD and postdoc positions in physics-informed machine learning.

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.

• Control Theory
• Partial Differential Equations
• Drilling Engineering
• +۶ مورد دیگر

Ole Morten Aamo is a Professor at the Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway. His office is located at Elektro D/B2, D344, Gløshaugen, O. S. Bragstads plass 2, and he can be reached at aamo@ntnu.no or by phone at 73594386. Professor Aamo's research focuses on control theory with particular emphasis on partial differential equations (PDEs) and their applications in drilling engineering and the petroleum industry. His work spans multiple areas including boundary control of hyperbolic systems, adaptive control methodologies, vibration control in drilling operations, and leak detection systems for pipe networks. His research combines theoretical control developments with practical applications in the oil and gas sector, particularly addressing challenges related to stick-slip phenomena, torsional vibrations, and pressure oscillations in drilling operations. His publication record reveals a consistent trajectory of high-impact research in control systems, with a notable shift toward integrating machine learning approaches with traditional control theory in recent years. The majority of his work centers around hyperbolic PDE systems, with applications primarily in drilling engineering and fluid dynamics. His research demonstrates a strong connection between theoretical control developments and practical implementations in the petroleum industry. Professor Aamo has actively supervised multiple graduate students, as evidenced by the master's theses listed in his publication record. His work often appears in top-tier control journals including IEEE Transactions on Automatic Control, Automatica, and IEEE Control Systems Letters, as well as petroleum engineering venues like SPE Journal and ASME publications.

Arnoldo Frigessiمشاهده پروفایل

استاد

• Statistics
• Biostatistics
• Machine Learning
• +۵ مورد دیگر

Arnoldo Frigessi is Professor of Statistics at the University of Oslo, where he leads the Oslo Center for Biostatistics and Epidemiology and serves as director of BigInsight—a Centre of Excellence for Research-Based Innovation. This consortium unites industry, business, public actors, and academia to develop model-based machine learning methodologies for big data, with strong emphasis on health applications. His research centers on statistical methodology driven by real-world scientific challenges, specializing in stochastic models for complex dependence structures and computationally intensive inference algorithms. Core application domains include: Genomics and personalized cancer therapy (particularly breast and lung cancer) Infectious disease modeling (including pandemic response) eHealth, sensor data analysis, and recommender systems Personalized marketing and viral diffusion dynamics Analysis of his 15 most recent publications (2024-2025) reveals dominant themes in cancer systems biology , where he integrates multi-omics, single-cell transcriptomics, and computational modeling to decode tumor evolution under therapy. Parallel work advances infectious disease epidemiology through time-varying reproduction number estimation and mobility-based transmission modeling, while methodological innovations span synthetic data generation (TVineSynth), causal inference via target trial emulation, and Bayesian ranking models for recommender systems. Scientific Awards: No specific awards mentioned in source materials Frigessi actively supervises graduate students, including a Department of Informatics project on "Utilizing covariate information in recommender systems." His leadership of BigInsight—funded as a Research-Based Innovation Centre by the Research Council of Norway—secures major grants supporting interdisciplinary collaborations with industrial partners (e.g., Telenor, DNB) and public health institutions. Current projects integrate real-world clinical data with mechanistic models for treatment optimization. He directs BigInsight's multidisciplinary team of statisticians, computer scientists, and domain experts, while leading the Oslo Center for Biostatistics and Epidemiology's efforts in developing statistical frameworks for complex health data. These initiatives drive Norway's national strategy for data-driven health innovation.

• Biomedical Engineering
• Neuroscience
• Signal Processing
• +۳ مورد دیگر

Andres Soler is a Lecturer at NTNU's Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering. His research focuses on EEG signal processing for applications in brain-computer interfaces (BCI), stress/health monitoring, and low-density electrode systems. He has published extensively on topics including EEG source imaging, artifact removal, and optimized channel selection techniques. His work bridges biomedical engineering and machine learning, with notable contributions to driver alcohol detection systems and motor imagery classification for neurorehabilitation. Teaching roles include serving as Guest Lecturer for Biomedical Instrumentation and Control (TTK4270) and Adaptive Data Analysis (TTK7), while acting as main lecturer for Industrial Electrotechnics (TTK4240). His research group collaborates internationally on projects like FlexEEG and has presented at conferences such as IEEE EMBC and Brain Informatics. Key research directions include advancing EEG-based systems for clinical and automotive applications, developing algorithms for real-time brain activity decoding, and optimizing EEG hardware configurations for cost-effective implementations. Current trends show focus on enhancing signal quality through artifact mitigation strategies and improving BCI communication systems for locked-in patients.

Odd Olai Aalenمشاهده پروفایل

استاد

• Biostatistics
• Survival Analysis
• Causal Inference
• +۳ مورد دیگر

Odd Olai Aalen is a Professor of Medical Statistics at the University of Oslo, holding a PhD in Biostatistics from UC Berkeley. His research focuses on survival analysis, causal inference, and epidemiological modeling, contributing to advancements in statistical methodologies for clinical and public health applications. Aalen has been recognized for his work, including presenting the 2010 Armitage Lecture in Cambridge. Education: PhD in Biostatistics, UC Berkeley Research Interests: Development and application of survival analysis techniques Causal inference in medical research Frailty modeling in epidemiology Methodological innovations in longitudinal data analysis Recent Publications Trends: Aalen's work emphasizes translating statistical theory into practical tools for understanding disease mechanisms and treatment effects. His articles frequently address challenges in clinical trial design, mediation analysis, and addressing biases in observational studies. Key themes include improving outcomes prediction, refining causal pathway models, and integrating longitudinal data with survival analysis. Awards: 2010 Armitage Lecture, University of Cambridge Advising & Grants: While specific grant details are not listed, his extensive publication record reflects sustained funding in biostatistics and medical research. He leads the Causal inference and event history analysis research group, fostering interdisciplinary collaboration. Labs/Teams: Active in the Causal inference and event history analysis group at the University of Oslo, focusing on statistical methodologies for medical and epidemiological challenges.

Thor Inge Fossenمشاهده پروفایل

استاد

• Marine craft hydrodynamics
• Motion control systems
• Navigation systems
• +۷ مورد دیگر

Thor Inge Fossen is a Professor of Navigation and Marine Craft Control at the Department of Engineering Cybernetics, Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). He is a key scientist at the Norwegian Centre for Embodied AI (NCEI) and internationally recognized for his work in navigation systems, guidance systems, and control of marine vessels, aircraft, and drones. Professor Fossen holds a PhD in Engineering Cybernetics and an MSc in Marine Technology. His academic journey has led him to become a Fellow of AAIA, IEEE, and IFAC, reflecting his significant contributions to the field. His research spans several critical areas in marine and aerospace systems: Marine craft hydrodynamics and motion control Navigation, guidance, and control systems for marine craft, aircraft, and drones Cybersecurity of autonomous vehicles Sea-state estimation and wave analysis Attitude control and estimation Fossen's marine craft model, which is widely used in the industry Professor Fossen's publication record demonstrates a strong focus on adaptive control systems, particularly Line-of-Sight (LOS) guidance laws, with numerous papers on 3D path following for marine and aerial vehicles. His recent work (2023-2025) shows increasing integration of machine learning techniques with traditional control systems, particularly in areas like constrained control allocation using deep neural networks. There's also a growing emphasis on cybersecurity aspects of autonomous vehicle guidance systems. His scientific recognition includes: Fellow of the American Institute of Aeronautics and Astronautics (AAIA) Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the International Federation of Automatic Control (IFAC) Professor Fossen has been actively involved in advising graduate students, with numerous PhD and MSc graduates. He has led significant research projects including the Marine Systems Simulator (MSS) and the Python Vehicle Simulator, which are widely used tools in the field. His current appointments include being a Study Program Coordinator for the Master's program in Cybernetics and Robotics at NTNU and a Key Scientist at the Norwegian Centre for Embodied AI. He leads research teams focused on embodied AI applications for marine systems, with particular emphasis on safe and secure autonomous operations in complex maritime environments. His work bridges theoretical control systems with practical marine applications, making significant contributions to both academic research and industry implementation.

• Structural Dynamics
• Bridge Engineering
• Wind Engineering
• +۶ مورد دیگر

Ole Andre Øiseth is a Professor at the Department of Structural Engineering, Norwegian University of Science and Technology (NTNU), specializing in structural dynamics with focus on bridges and marine structures. He leads the structural mechanics research group. Research Interests: Wind Engineering Bridge Aerodynamics Structural Health Monitoring Operational Modal Analysis Fluid-Structure Interaction Extreme Load Analysis Key Contributions: Developed nonlinear force models for bridge aerodynamics Advanced Kalman filter techniques for wind load identification Environmental contour methods for bridge design Automated monitoring systems for long-span bridges

• Polar Science
• Arctic Marine Ecology
• Fisheries Management
• +۳ مورد دیگر

Ole Arve Misund serves as a Professor at the Norwegian Fisheries College within UiT The Arctic University of Norway, Tromsø. His research spans polar science, Arctic marine ecology, fisheries management, and international relations in polar regions, with particular focus on Norway-Russia scientific cooperation dynamics following geopolitical shifts in 2022. His research interests center on climate change impacts on Arctic marine ecosystems, acoustic monitoring of marine organisms, and the geopolitical dimensions of polar research. Recent work examines boreal species migration into the Central Arctic Ocean, machine learning applications for backscatter analysis, and management frameworks for the Central Arctic Ocean. Analysis of his recent publications reveals strong interdisciplinary trends, integrating marine ecology with political science through studies of Russian-Norwegian scientific collaboration under sanctions, illegal fishing enforcement, and policy-relevant polar research. His work consistently bridges academic research with practical fisheries management and geopolitical considerations. Misund actively engages in science communication through Norwegian media outlets like Nordlys and Fiskeribladet, addressing topics from climate policy contradictions to Antarctic hydrological changes. His expeditionary work includes North Pole 2022 aboard the "Kronprins Haakon" research vessel. Professional activities demonstrate significant leadership in polar research infrastructure development and international scientific collaboration, particularly through the Norwegian Polar Institute where he contributes to strategic planning and policy advice. His work emphasizes evidence-based approaches to Arctic governance amid evolving geopolitical landscapes.

Sigurd Skogestadمشاهده پروفایل

استاد

• Process Control
• Systems Engineering
• Optimization
• +۲ مورد دیگر

Sigurd Skogestad is Professor in the Department of Chemical Engineering at the Norwegian University of Science and Technology (NTNU). He serves as Program Leader for the 5-year Master program in Chemical and Biochemical Engineering. His research develops simple yet rigorous methods to solve engineering problems, focusing on self-optimizing control structures that enable near-optimal operation under varying conditions. His approach moves economic optimization from slow real-time optimization to faster control layers. Key research areas: Self-optimizing control structures Process systems engineering Multivariable feedback control Plantwide process control Distillation modeling and control He has received significant recognition including the AIChE Computing in Chemical Engineering Award and IFAC Fellow status. He leads research groups in Process Control and contributes to the SUBPRO center on subsea production.

• Biomechanics
• Cardiovascular System Modeling
• Uncertainty Quantification
• +۵ مورد دیگر

Leif Rune Hellevik is a Professor of Biomechanics at the Department of Structural Engineering, Faculty of Engineering at NTNU. He also serves as Vice Dean for Master Programs since 2017. His research focuses on computational biomechanics, particularly in cardiovascular systems, emphasizing uncertainty quantification and sensitivity analysis. Key projects include STARFiSh (Stochastic Arterial Flow Simulations) and MyMDT (Medical Digital Twin for hypertension management). He has extensive experience in fluid-structure interaction and numerical methods, contributing to clinical decision support systems and translational hypertension research. His teaching includes courses on numerical methods and biomechanics. Research Interests: His work spans biomechanical modeling of arterial systems, cardiovascular hemodynamics, and translational applications of computational models for personalized medicine. Recent efforts integrate machine learning and digital twins to improve hypertension treatment and prevention. Publications: Over 30 peer-reviewed articles since 2000, with recent focus on sensitivity analysis, model validation, and clinical applications in coronary artery disease. Collaborations include institutions like Linköping University, University of British Columbia, and Erasmus MC. Labs/Teams: Leads projects in computational biomechanics and vascular modeling, collaborating with multidisciplinary teams in engineering, medicine, and data science.

• Autonomous Systems
• Target Tracking
• Navigation
• +۵ مورد دیگر

Edmund Førland Brekke is a Full Professor at NTNU's Department of Engineering Cybernetics, specializing in autonomous maritime systems. He leads major projects like Autosea, Autosit, Autosight, and SFI Autoship. His research focuses on estimation theory, target tracking, SLAM, and collision avoidance for autonomous ships. He has held roles including Project Manager for key initiatives and was a Research Fellow at NUS's Acoustic Research Lab. Brekke teaches courses on sensor fusion and nonlinear state estimation. PhD: NTNU (2010), MSc: NTNU (2005) Research interests include sensor fusion, maritime navigation, and autonomous system safety. He supervises over 20 PhD students in topics like radar tracking, SLAM, and digital twins. His publications span collision avoidance algorithms, multi-sensor integration, and autonomous vessel control. He co-founded Zeabuz and collaborates on projects like Autoferry and AutoBarge. Recent work emphasizes maritime target tracking, scenario-based collision risk assessment, and sensor fusion for autonomous ferries. His contributions advance autonomous vessel autonomy and regulatory compliance with COLREGs.