Find Academic Supervisors

• Control Systems
• Robotics
• Power Systems
• +2 more

Professor Damiano Varagnolo is affiliated with the Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU), where he conducts research spanning control systems, robotics, power systems, and data-driven modeling. His work bridges theoretical advances with practical applications in underwater vehicles, medical rehabilitation, and energy infrastructure. His research focuses on: Development of formation control algorithms for autonomous underwater vehicles (AUVs) and snake robots Transient stability enhancement for grid-forming converters in renewable energy systems Data-driven modeling of physiological signals for wheelchair propulsion and industrial processes Distributed optimization for underwater communication networks Recent publications (2024-2025) demonstrate strong emphasis on: Underactuated vehicle control using hand position concepts Energy expenditure estimation through physiological signal processing Consensus protocols for JANUS-based underwater networks Educational innovation through control-themed outreach initiatives like the Advent Calendar project He actively collaborates with NTNU colleagues across engineering disciplines and contributes to curriculum development through graph-theoretic approaches for educational coherence.

Mahmoud Sayed Mahmoud EidView profile

Research Fellow

• Data-driven fault diagnosis
• Electric power trains
• Electromagnetic modeling
• +1 more

Mahmoud Sayed Mahmoud Eid is a PhD Research Fellow in Engineering Sciences at the University of Agder. His research focuses on fault diagnosis in electric powertrains using AI-driven methods. He holds a B.S. in Mechatronics Engineering from Helwan University (2013) and an M.S. in Advanced Mechanical and Robotics Engineering from Ritsumeikan University, Japan (2019). Research Focus: Develops computational models for early fault detection in electric motors and inverters under noisy operational conditions. His methodologies integrate electromagnetic theory with machine learning for industrial applications in transportation and energy systems. Publications: His 6 most recent articles (2022-2025) focus on AI-enhanced diagnostics for power electronics and motor systems, demonstrating consistent innovation in signal processing and classifier design for industrial reliability.

Narada Dilp WarakagodaView profile

Associate Professor

• Artificial Intelligence
• Autonomous Systems
• Computer Vision
• +3 more

Narada Dilp Warakagoda is an Associate Professor at the University of Oslo, affiliated with the Section for Autonomous Systems and Sensor Technologies. His research focuses on Artificial Intelligence, Autonomous Systems, Computer Vision, Robotics, Machine Learning, and Deep Learning, with notable contributions to underwater acoustics, renewable energy forecasting, and unmanned vehicle control systems. He collaborates extensively with researchers across disciplines to advance applications in environmental monitoring, energy systems, and autonomous technologies. His work integrates advanced machine learning techniques with domain-specific challenges, such as synthetic aperture sonar analysis, spatio-temporal wind forecasting, and reinforcement learning for autonomous navigation. He has published in leading journals and conferences, addressing topics like probabilistic solar irradiance prediction, graph-based wind energy modeling, and deep learning for seabed munitions detection. Warakagoda’s research bridges theoretical advancements with practical implementations, emphasizing applications in robotics, environmental science, and defense technologies. His projects often involve interdisciplinary teams, reflecting his commitment to solving real-world problems through innovative AI-driven solutions.

• Network Performance Evaluation
• Software-Defined Networking (SDN)
• Network Function Virtualization (NFV)
• +4 more

Thomas Erich Zinner is Professor at the Department of Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU), a position held since August 2019. Previously, he served as visiting professor and head of the FG INET research group at TU Berlin, and led the 'Next Generation Networks' research group at the University of Würzburg's Communication Networks chair. His educational background includes a diploma (2006) and Ph.D. (2012), both from the University of Würzburg. His research spans network architecture performance evaluation with emphasis on SDN/NFV and QoE-centric management approaches for emerging networks. Zinner's recent publications reveal strong trends toward intelligent 6G architectures integrating AI in the user plane, QoE-aware 5G resource allocation, and autonomic management of softwarized networks. His work combines theoretical modeling, simulation frameworks like OMNeT++, and practical implementations focused on real-world applicability in beyond-5G systems. He leads the Networking Research Group at NTNU working on the TeraFlow project, developing secure cloud-native SDN controllers for autonomic traffic management at massive scale. This initiative addresses critical challenges in next-generation network infrastructure through innovative controller architectures and flow management techniques.

Silvia Lizeth Tapia TarifaView profile

Associate Professor

• Formal methods
• Distributed systems
• Parallel computing
• +7 more

Silvia Lizeth Tapia Tarifa is an Associate Professor in the Department of Informatics at the University of Oslo, specializing in formal methods for parallel and distributed systems. She serves as one of the principal investigators for the NFR SJM (Smart Journey Mining) project, which runs until 2026, and actively participates in Digital Twins research with a focus on GDPR-compliant data management. Her academic affiliations include the Reliable Systems research group and the Analytical Systems and Reasoning (ASR) group at the Department of Informatics. Professor Tapia Tarifa's research spans formal methods, concurrency theory, and distributed systems with particular emphasis on self-adaptive systems, semantics of concurrent languages, compositional reasoning about distributed system behavior, and formal modeling of resource usage. Her work bridges theoretical computer science with practical applications in digital twins, GDPR compliance, and resource management in distributed environments. She has made significant contributions to the ABS language framework and active object models for parallel and distributed computing. Her publication record shows a consistent focus on formal verification techniques applied to emerging challenges in distributed computing. Recent work demonstrates increasing attention to digital twins technology, user journey modeling, and privacy-preserving systems. The research trajectory reveals evolution from foundational work on concurrent language semantics toward applied research in self-adaptive systems and GDPR-compliant architectures, while maintaining strong theoretical underpinnings in formal methods. Young Research Talent grant from Research Council of Norway (2017), the only computer science grant in that call Fellow at United Nations University, International Institute for Software Technology (2007) Active participation in formal methods community as general chair, PC chair, and committee member Professor Tapia Tarifa has supervised PhD and master's students while teaching graduate-level courses. She has led significant research initiatives including the Analysis and Complex System Research Program at SIRIUS Center (ended 2023) and the EU MSCA-ITN REMARO project on Reliable AI for Marine Robotics (ended 2024). Her current research portfolio includes multiple active grants focused on digital twins, user journey analysis, and privacy-preserving distributed systems. She collaborates extensively with researchers across Europe through various EU-funded projects including FP7 ENVISAGE, FP7 FET UpScale, and FP7 FET HATS. Her research activities are centered around the ABS language framework and its applications to distributed systems verification. She maintains active collaborations through the SIRIUS Center and participates in the international formal methods community through conference organization and program committees.

• Digitalization
• Control Systems
• Optimization Algorithms
• +4 more

Guoyuan Li is a Professor at the Department of Ocean Operations and Civil Engineering, Faculty of Engineering, Norwegian University of Science and Technology (NTNU), Ålesund Campus. His work bridges digitalization , artificial intelligence , and maritime engineering , focusing on ship maneuvering, robotics, and human-machine interaction. Ph.D. in Computer Science, University of Hamburg (2013) M.S. & B.S. in Computer Science, Chongqing University (2009 & 2006) Research Interests: Digital twin systems for ships, adaptive locomotion control in bio-inspired robotics, trajectory prediction for marine vessels, and human visual attention analysis in maritime operations. He integrates machine learning and physics-based models to enhance safety and efficiency in marine environments. Publications highlight trends in ship motion prediction , collision avoidance , and environmental disturbance modeling , with applications in digital twin technology and remote control centers . His work spans IEEE and Springer journals. Awards include multiple Best Paper Awards at IEEE conferences (2024-2014). He serves as Associate Editor for IEEE Journal of Oceanic Engineering and IEEE Transactions on Intelligent Transportation Systems . Projects include EU’s RoboSapiens (robot adaptation), Digital Twin for Green Ship Operations (Norway), and AuReCo (remote control systems). He collaborates with the Intelligent Systems Lab at NTNU.

Woldaregay, Ashenafi ZebeneView profile

Researcher

• Digital Health
• mHealth
• Artificial Intelligence in Healthcare
• +7 more

Woldaregay, Ashenafi Zebene is a researcher at UiT The Arctic University of Norway, affiliated with the Faculty of Health Sciences and the Department of Clinical Medicine. His work bridges digital health, artificial intelligence, and clinical applications, particularly in diabetes management and infectious disease surveillance. He is a key contributor to the EDMON (Electronic Disease Surveillance and Monitoring Network) project, which leverages self-recorded health data from people with Type 1 diabetes for early outbreak detection. PhD in Digital Health / Health Informatics (2021) Master’s in Electronic Disease Surveillance (2016) His research focuses on applying machine learning, data science, and AI to solve pressing healthcare challenges. Key areas include blood glucose pattern analysis in diabetes, anomaly detection, mHealth adoption, wearable technology, and syndromic surveillance. He has developed models for infection detection, surgical risk prediction, and deidentification of clinical text. His work emphasizes real-world applicability, patient engagement, and data privacy. The most recent articles show a strong trend toward transformer models, synthetic data, and instruction-guided NLP in clinical contexts, alongside continued work in mHealth usability, caregiver support, and personalized health monitoring. His publications span journals in health informatics, medical informatics, and digital health, reflecting interdisciplinary collaboration. Scientific contributions include: Development of the EDMON system for real-time infection monitoring Creation of datasets for wearables and mHealth motivation factors Systematic reviews on reinforcement learning in diabetes and AI in healthcare security Innovation in cluster detection algorithms (K-CUSUM) for outbreak detection While direct information on grants and advising is not provided, his role as a doctoral candidate and co-author on numerous student-led studies suggests involvement in research mentoring. He collaborates extensively with researchers such as Gunnar Hartvigsen, Eirik Årsand, and Karl Øyvind Mikalsen, indicating membership in a large, active digital health research group. He is involved in a multidisciplinary research lab focused on digital health innovation, mHealth systems, and AI-driven clinical decision support. The team works on real-time monitoring, data privacy, and user-centered design of health technologies, with applications in chronic disease management and public health surveillance.

• Synthetic Biology
• Biophysics
• Computational Biology
• +7 more

Rahmi Lale is an Adjunct Associate Professor and Senior Researcher at the Department of Biotechnology and Food Science, Faculty of Natural Sciences, Norwegian University of Science and Technology (NTNU). His research spans synthetic biology, biophysics, and computational biology, focusing on transcriptional and translational control in microorganisms. He employs advanced methods like microfluidics, single-cell arrays, and high-throughput screening across diverse organisms including Thermus thermophilus , Vibrio natriegens , and Pseudomonas putida . He can be reached at rahmi.lale@ntnu.no. Research Interests: Biophysics, Computational Biology, Synthetic Biology, Metagenomics, Microfluidics, Bacterial Genetics Key Projects: Horizon Europe (MetaExplore, Sustainable Exploration), RCN (SafePhaeO3), U.S. NSF (CIRCLE Center), Innovation Fund Denmark (MuscleFuel), EuroHPC (AI-based DNA Design) Scientific Awards: Kavli Prize in Nanoscience (2018), iGEM Gold Medal (2014) Teaching: Advanced Biotechnology (BT3220), Bioinformatics and Statistics (TBT4507), Bioinformatics and Biopolymeric Materials (TBT4508) Lale's recent publications focus on synthetic gene regulation, AI-driven DNA design, and extremophile bioproduction. His lab website ( Lale Lab ) details ongoing work in interdisciplinary data-driven approaches.

• Metabolomics
• Machine Learning
• Breast Cancer
• +3 more

Julia Debik is an Associate Professor at the Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), with a dual research role at the Musculoskeletal Research Group and CIMORe. She holds a PhD in Medical Technology and a Master of Science in Industrial Mathematics from NTNU, combining computational expertise with biomedical research. Academic Roles: Associate Professor (Onsager Fellow - AI and Health) Departments: Public Health and Nursing, Circulation and Medical Imaging Collaborations: National Network for Breast Cancer Research, Nordic Metabolomics Society, Metabolomics Society Research Focus: Integrating machine learning with metabolomics to uncover breast cancer risk factors, treatment response, and prognostic signatures. Her work emphasizes NMR-based metabolic profiling, biobank utilization (HUNT2, UK Biobank), and sample handling standardization (freeze-thaw cycles, delayed centrifugation). Publication Trends: Over 15 recent articles focus on metabolomic-biomarker discovery for breast cancer, circadian exercise effects, and technical validation of NMR platforms. Key subfields include lipoprotein associations, multi-omics integration, and AI-driven analysis of spatial -omics data. Scientific Contributions: Onsager Fellowship in AI and Health Key collaborator in HUNT biobank studies International presentations at Metabolomics Society conferences Developer of machine learning frameworks for multi-cancer profiling Outreach Activities: Regularly participates in public science events like Researchers' Night, delivers lectures on AI and molecular epidemiology, and contributes to popular science communication through initiatives like 'Tumorteltet'.

• Process Optimization
• Model Predictive Control
• Process Systems Engineering
• +3 more

Johannes Jäschke is Professor of Chemical Engineering at the Norwegian University of Science and Technology (NTNU), where he heads the Process Systems Engineering group. His research develops methods for operating industrial processes safely and economically using optimization and control theory. Research focuses on optimization-driven methods for process operation, real-time optimization, predictive control, and quantitative data methods for industrial applications. Jäschke leads multiple research projects including SensPATH for economic model predictive control and AutoPRO for autonomous production optimization in offshore systems. Honors: Member of Outstanding Academic Fellows Program at NTNU (2014-2018) His teaching covers process optimization and control strategies, and he serves on departmental committees while advising graduate students in process systems engineering.

• Information Security
• Risk Management
• Cybersecurity
• +5 more

Einar Arthur Snekkenes is a Professor at the Department of Information Security and Communication Technology at the Norwegian University of Science and Technology (NTNU) in Gjøvik. His research focuses on information security risk management , integrating human behavior , game theory , and adaptive security mechanisms . He has contributed extensively to understanding security trade-offs in IoT systems, behavioral economics in risk analysis, and AI-driven security practices in healthcare. His peer-reviewed publications span topics like delay discounting in security decisions, motivational profiling for risk modeling, and privacy risk analysis through conflicting incentives. He has collaborated on frameworks such as SGAM-H (Smart Grid Architecture Model Extended with the Human Layer) and Core Unified Risk Framework for assessing security method completeness. His work often bridges technical and human-centric perspectives in cybersecurity. Recent trends in his research, particularly from 2019-2021, emphasize AI applications for analyzing healthcare staff security practices , including psychosocialcultural factors and adaptive incentives . Earlier studies (2007-2015) explored gait recognition as a biometric method and side-channel attacks in hardware security. Despite no explicit scientific awards listed, his prolific output in journals like Journal of Cybersecurity , IEEE Access , and JMIR Human Factors highlights his influence in the field.

• Arctic Navigation Safety
• Maritime Risk Assessment
• Autonomous Ship Systems
• +3 more

Sheng Xu is a Research Fellow at the Department of Technology and Security, UiT The Arctic University of Norway. His postdoctoral research focuses on designing adaptive learning models for maritime education and training (MET), particularly in Arctic navigation contexts. His work integrates risk assessment methodologies, Bayesian network modeling, and data-driven analytics to enhance operational safety in ice-covered waters. Research interests include autonomous ship systems, collision risk mitigation in ice channels, and the application of probabilistic models to predict navigation hazards. He collaborates on projects analyzing vessel besetting incidents, ice load impacts on offshore structures, and human factors in Arctic navigation decision-making. Publications span 2012–2025, emphasizing statistical analysis of ice-related maritime risks and software risk modeling for autonomous vessels. His studies frequently utilize AIS data, satellite imagery (e.g., Sentinel), and hybrid causal logic frameworks. Key technical contributions include frameworks for failure scenario identification in autonomous ship control systems and reviews comparing risk analysis models in ice-covered regions. His work bridges engineering, environmental science, and policy to address challenges in Arctic shipping.

• Nonlinear Model Predictive Control
• Offshore Drilling Automation
• Power Systems Control
• +3 more

Lars Struen Imsland is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU) since 2009. He holds a PhD and MSc in Engineering Cybernetics from NTNU, with research focusing on nonlinear model predictive control, offshore drilling automation, and power systems. His career includes roles at Cybernetica AS, SINTEF ICT, and NTNU as a PostDoc and doctoral student. Research interests span control systems engineering, CO2 capture technology, and estimation in automotive applications. He chairs committees for international conferences like DYCOPS-CAB and ACOOG, and serves as Associate Editor for IEEE Transactions on Control Systems Technology. Key achievements include the Arch T. Colwell Merit Award (2008) and Automatica Outstanding Reviewer recognition (2004). He has advised over 10 PhD committees and contributed to pioneering work in MPC for wind farms, hybrid energy systems, and oil well modeling. Publications emphasize interdisciplinary applications of control theory, with recent focus on Bayesian optimization for wind farms, physics-informed neural networks for oil well monitoring, and stochastic MPC for renewable energy integration.

• Digital Twin Technology
• Artificial Intelligence
• Machine Learning
• +7 more

Adil Rasheed is a Professor in the Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU), within the Faculty of Information Technology and Electrical Engineering. His research focuses on digital twin technology, artificial intelligence (AI), machine learning (ML), physics-based modeling, and hybrid analysis methodologies. He leads projects such as the work package on Digital Twin and Asset Management, collaborating with major industry partners to advance digital twin capabilities. His interdisciplinary work bridges AI-driven approaches with traditional physics-based models for applications in wind energy, autonomous vessels, smart greenhouses, and aquaculture. Notable contributions include developing a VR-enabled smart greenhouse digital twin and the PoroTwin framework for porous media flow analysis. His recent publications span topics like safe marine navigation using reinforcement learning, federated learning for industrial IoT anomaly detection, and predictive maintenance in offshore wind turbines. Rasheed actively engages in academic outreach and serves as an advisor to research initiatives in hybrid modeling and autonomous systems. Research Interests Creation and application of digital twins for physical systems optimization Integration of AI/ML with physics-based models for hybrid systems Autonomous systems navigation and safety (vessels, drones) Wind energy systems and offshore renewable energy Condition monitoring and predictive maintenance Data-driven solutions for aquaculture and urban mobility challenges Key Projects Smart Greenhouse: AI-driven digital twin for autonomous plant growth monitoring via VR PoroTwin: Digital twin for porous media flow analysis in oil and gas Hybrid Analysis and Modeling (HAM) framework combining knowledge-based and data-driven methods NorthWind Project: Digital twin advancements for wind energy systems Publications Trends Rasheed's recent work emphasizes digital twin applications in energy systems (wind turbines), autonomous maritime navigation, and industrial IoT security. He explores federated learning techniques for decentralized anomaly detection and integrates reinforcement learning for safety-critical control systems. Collaborations with industry partners highlight practical implementations in offshore infrastructure, aquaculture, and urban mobility. Grants & Partnerships Active collaborations with industry leaders in renewable energy, maritime robotics, and smart agriculture ensure his research addresses real-world challenges. His work is supported by interdisciplinary projects combining academia-industry expertise. Labs & Teams Leads the Digital Twin and Asset Management team at NTNU, coordinating with research groups in computational engineering, autonomous systems, and renewable energy technology.

• Quaternary Geology
• Glacial Landforms
• Loose Materials Characterization
• +7 more

Professor Hans Ola Fredin holds a faculty position at the Department of Geosciences, NTNU within the Faculty of Engineering. His research focuses on Quaternary geology, glacial landforms, and geohazards related to loose materials like quick clays. He employs advanced techniques such as GIS, remote sensing, and machine learning to study spatial relationships and geological processes. Current research projects include Antarctic ice volume changes, deglaciation patterns of Norway/Scandinavia, and AI-driven geoscience applications. His work bridges field observations with computational methods, contributing to understanding both past glacial dynamics and modern geohazard mitigation. Publications from 2020–2024 highlight interdisciplinary approaches to glacial geomorphology, ice sheet behavior, and environmental risk assessment. Notable contributions include studies on strandflat formation mechanisms, radon risks from rock-avalanche deposits, and mid-Pliocene Antarctic ice thickness reconstructions.