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• Cybersecurity
• Privacy and Data Protection
• GDPR Compliance
• +7 more

Mohamed Abomhara is a Senior Researcher and Head of Department at the Department of Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU) , Faculty of Information Technology and Electrical Engineering. He serves as Discipline Leader for the MRI PET (Multidisciplinary Research group on Privacy and data protEcTion) research group. Research Interests His expertise includes: GDPR compliance and privacy-by-design Risk assessment and secure system design AI ethics and social-cyber risk mitigation Healthcare digital transformation security Border control technology ethics Recent Publications His 2024-2025 research focuses on multilingual hate speech detection , border control technology acceptance , and privacy protections in national identification systems , bridging AI ethics, cybersecurity, and regulatory compliance. Earlier work (2016-2022) addresses: Cybersecurity in digital substation infrastructure Secure collaborative healthcare information sharing Blockchain-based GDPR compliance AI-driven social media analysis

• Biometrics
• Deep Learning
• Fake Face Generation and Detection
• +5 more

Raghavendra Ramachandra is a Professor at the Department of Information Security and Communication Technology , NTNU (Norwegian University of Science and Technology) . His research focuses on Biometrics , Deep Learning , and Image Processing , particularly addressing Morphing Attack Detection and Presentation Attack Detection in biometric systems. Current Projects: SALT (2022-2026) : Developing privacy-preserving face biometric authentication with morphing attack detection. OffPAD (2022-2025) : Creating cryptographic tools and fingerprint attack detection techniques. SWAN (2015-2020) : Mitigating presentation attacks in biometric access control. Research Interests: Biometric security, deep learning for fake face detection, smartphone-based biometric authentication, and morphing attack countermeasures. Publication Trends: His recent work explores advanced neural network architectures for attack detection (e.g., attention networks, vision transformers), synthetic dataset generation, and multi-sensor biometric verification. Technical Expertise: Combines computer vision, machine learning, and cryptographic security for robust biometric systems. Contact: raghavendra.ramachandra@ntnu.no

• Autonomous Systems
• Machine Learning
• Computer Vision
• +3 more

Muhammad Adnan serves as a Research Fellow at the Department of Technology and Safety, UiT The Arctic University of Norway, with contact details including email muhammad.adnan@uit.no and phone +47 77 66 02 47. His research spans critical domains in modern engineering and computer science, with primary focus areas: Autonomous Maritime Systems : Developing operational frameworks for remotely controlled vessels and onshore operation centers Machine Learning Applications : Advancing ensemble methods for classification, spam detection, and remote sensing Computer Vision Integration : Leveraging electro-optical/NIR sensors for maritime object detection Cybersecurity Solutions : Enhancing email security through stacking ensemble techniques Recent publications (2023-2024) reveal a concentrated research trajectory merging maritime autonomy with AI-driven solutions, demonstrating significant interdisciplinary work across navigation support systems, sensor fusion, and transfer learning applications. His output shows consistent publication in high-impact journals with practical engineering implementations. No scientific awards were documented in the provided materials. Information regarding student supervision, grant funding, or laboratory affiliations was not available in the source text.

• Control Engineering
• Automation
• Marine Icing
• +5 more

Sushmit Dhar serves as an Associate Professor in the Department of Automation and Process Technology at UiT The Arctic University of Norway, focusing on control engineering and automation solutions for Arctic maritime challenges. His work bridges theoretical control methods with practical applications in extreme environments. His research spans: Classical and modern control methods (PID, LQR, MPC) State estimation and Kalman filtering Physics-Informed Neural Networks and Reinforcement Learning for decision support IoT-based automation for maritime operations Marine icing phenomena and sea spray measurement Recent publications demonstrate cutting-edge work in marine icing prediction using LiDAR and capacitive sensors, Arctic road safety systems, and optimization of maritime operations. His research integrates advanced sensing technologies with data-driven modeling to address safety challenges in cold climates, particularly through sea spray measurement innovations and route planning algorithms for fishing vessels and search-and-rescue operations. Dr. Dhar actively contributes to collaborative initiatives: Member of the Sustainable Technology and Safety (STS) research group Member of the IR, Spectroscopy, and Numerical Modelling Research Group Key participant in the SPRICE project (Multidisciplinary approach for spray icing modelling and decision support) His teaching portfolio includes foundational courses in Linear Systems (AUT-2604) and Control Engineering (AUT-2801) for Automation Engineering undergraduates, preparing students for technical challenges in Arctic engineering contexts.

• Machine Learning
• Medical Imaging
• Positron Emission Tomography (PET)
• +3 more

Samuel Kuttner is an Associate Professor II (part-time) in Machine Learning at the Department of Physics and Technology, UiT The Arctic University of Norway. He specializes in applying artificial intelligence to medical imaging challenges, with core expertise in Positron Emission Tomography (PET), deep learning algorithms, and quantitative image analysis. His work bridges machine learning with clinical applications in oncology and neurology. Research Focus: Kuttner leads innovative projects at the intersection of AI and medical physics. His primary interests include: Developing non-invasive methods for arterial input function estimation in dynamic PET imaging Implementing deep learning for tumor segmentation and recurrence prediction in glioblastoma Optimizing quantitative PET/MRI through artifact correction and reconstruction algorithms Advancing physics-informed machine learning models for biomedical applications Publication Trends: His recent articles (2020-2025) demonstrate strong focus on: AI-driven solutions for PET quantification challenges Clinical translation of radiomics and deep learning in neuro-oncology Methodological innovations for preclinical and clinical imaging workflows Interdisciplinary collaborations across machine learning, physics, and clinical medicine Research Groups: He actively contributes to the Nuclear Medicine and Radiation Biology (NMRB) group and the Machine Learning Group at UiT, fostering cross-disciplinary research in medical technology innovation.

• Medical Imaging
• Computer Vision
• Artificial Intelligence
• +2 more

Akriti Sharma serves as a Postdoctoral Fellow in the Department of Validation Intelligence for Autonomous Software Systems at Simula Research Laboratory, specializing in artificial intelligence applications for medical diagnostics and maritime engineering. Her core research interests span: Medical Image Analysis for human embryo development in IVF procedures Computer Vision using deep learning architectures (YOLO) for embryo cleavage detection Transfer Learning techniques applied to maritime shaft power prediction Explainable AI frameworks for clinical decision support systems Energy efficiency optimization in shipping through sensor data analysis From 2022-2025, her publication trajectory shows progression from embryo cell tracking automation to cross-domain transfer learning applications. Early work focused on time-lapse video analysis for cleavage timing prediction, evolving toward AI-driven embryo quality assessment and maritime power forecasting, demonstrating consistent innovation in real-world AI deployment. No scientific awards or honors were documented in the source material. Information regarding student advisement, research grants, or educational background was not provided in the available text. As part of Simula's Validation Intelligence for Autonomous Software Systems department, she contributes to developing rigorous validation methodologies ensuring safety and reliability in autonomous AI systems across healthcare and maritime sectors.