جستجوی استاد راهنمای دانشگاهی

• Cybersecurity
• Artificial Intelligence in Security
• Cyber Ranges
• +۵ مورد دیگر

Muhammad Mudassar Yamin is an Associate Professor at the Department of Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU), Gjøvik. His research bridges cybersecurity, artificial intelligence, and ethical considerations in digital systems. He collaborates internationally on cyber range technologies, adversarial AI, and multilingual threat detection frameworks. His research interests include: Cybersecurity exercise automation and modeling AI/ML applications for hate speech and fake news detection Security assurance frameworks for cloud services Attack-defense scenario generation using serious games Hybrid IoT cyber range architectures Recent publications analyze CTF scoring algorithms, develop self-supervised hate speech detection models, and explore large language models for cybersecurity scenario generation. His work integrates explainable AI principles with practical security implementations. He teaches: IIK3100 - Ethical Hacking and Penetration Testing TTM4175 - Introduction to Cyber Security and Data Communication

• Structural Health Monitoring
• Concrete Bridges
• Bridge Dynamics
• +۵ مورد دیگر

Daniel Cantero is an Associate Professor in the Department of Structural Engineering at the Norwegian University of Science and Technology (NTNU). He leads research initiatives in structural health monitoring, bridge dynamics, and vehicle-infrastructure interaction, with significant contributions to concrete bridge analysis and railway infrastructure diagnostics. His work bridges computational modeling with practical engineering solutions. PhD in Bridge Dynamics (University College Dublin, 2010) Postdoctoral Fellowships at Plaxis BV, Trinity College Dublin, and Royal Institute of Technology (KTH) His research focuses on structural health monitoring, bridge dynamics under moving loads, and signal processing for infrastructure diagnostics. Recent work explores federated learning for population-based monitoring, 3D vehicle modeling, and corrosion effects in post-tensioned bridges. Current publications highlight advancements in: Railway bridge condition monitoring using train responses Machine learning for damage assessment Wavelet analysis in soil-structure interaction Finite element tools for vehicle-bridge interaction Probabilistic models for structural diagnostics As course coordinator for TKT4220 - Concrete Structures 2 and instructor in multiple structural engineering courses, he mentors PhD students like Muhammad Zohaib Sarwar. He contributes to NTNU's Concrete Group and participates in international Marie-Curie projects.

Marius Aasanمشاهده پروفایل

پژوهشگر

• Bayesian paradigms
• Probabilistic machine learning
• Generative modelling
• +۱۵ مورد دیگر

Marius Aasan is a Machine Learning Researcher and PhD Candidate at the Department of Informatics, University of Oslo. He is affiliated with the Digital Signal Processing and Image Analysis (DSB) group and the Visual Intelligence (VI) research center. BSc in Mathematics and Informatics (MAMI) from University of Oslo MSc in Data Science from University of Oslo His research spans probabilistic machine learning, generative models, and invertible neural networks (INNs), with applications to imaging and inverse problems. He explores attention mechanisms in vision transformers and representation learning, integrating contextual priors into deep learning. Mathematically, he focuses on representation theory, manifolds, and generalized Fourier analysis. His recent work includes advancements in vision transformers for image tokenization, graph neural networks for visual question answering, and methods bridging densely connected networks with INN/Normalizing Flow architectures. He is part of the DSB research group and contributes to the Visual Intelligence (VI) center, focusing on research-based innovation in informatics.

• Digital signal processing
• Machine learning
• Sensor network
• +۳ مورد دیگر

Stefan Werner is a Professor at the Department of Electronic Systems, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU). Holding degrees in civil engineering from the Royal Institute of Technology (Sweden) and a Dr.Scient from Helsinki University of Technology (Finland), he leads research in digital signal processing, machine learning, and wireless communication. His work focuses on: Distributed learning algorithms Federated learning security Privacy-preserving signal processing Kernels and graph-based learning Robust wireless communication Recent publications address federated learning with non-convex penalties, anomaly detection in hyperspectral data, and resilience against model-poisoning attacks. Collaborators include researchers from IEEE and international institutions. Selected affiliations: Signal Processing research group, NTNU IoT@NTNU initiative Norwegian Open AI Lab

Sarina Thomasمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Sarina Thomas is an Associate Professor in the Department of Informatics at the University of Oslo, Faculty of Mathematics and Natural Sciences. She is a key member of the Digital Signal Processing and Image Analysis (DSB) research group, where she leads research at the intersection of artificial intelligence and medical imaging. Her work focuses on developing advanced AI techniques for the automatic analysis of cardiac ultrasound images, with strong ties to clinical applications. Her research interests include: Artificial Intelligence and Deep Learning Medical Image Analysis and Signal Processing Cardiovascular Imaging and Ultrasound Technology Automated Diagnosis and Clinical Decision Support Systems The recent publications highlight a strong trend in applying graph convolutional networks and deep learning models to improve robustness and accuracy in cardiac segmentation and intervention planning. These works reflect a focused effort on translating AI innovations into practical tools for cardiovascular diagnostics. Her collaboration with GE Vingmed underscores the industrial and clinical relevance of her research. Sarina Thomas has received recognition through active publication in high-impact journals such as Herz and IEEE Access . She advises students and leads projects in AI-driven biomedical signal processing, though specific advisees are not listed. Her research is supported by collaborations with industry and clinical partners, contributing to real-world applications in cardiology. She is actively involved in the Digital Signal Processing and Image Analysis research group, which focuses on cutting-edge methods in signal and image interpretation for healthcare applications.

Björn Thor Jónssonمشاهده پروفایل

پژوهشگر ارشد

• Audio Synthesis
• Evolutionary Algorithms
• Quality Diversity Search
• +۳ مورد دیگر

Björn Thor Jónsson is a Doctoral Research Fellow at the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, affiliated with the Department of Informatics (IFI) under the Faculty of Mathematics and Natural Sciences at the University of Oslo. His work focuses on applying evolutionary algorithms and quality diversity search methods to create systems for unbounded sonic discovery. Current Role: Doctoral Research Fellow at RITMO Centre Institution: University of Oslo Projects: Evolutionary Sound Exploration, Modeling and Robots, Musical Human-Computer Interaction Björn investigates how generative models, when pushed beyond prompted boundaries, can yield novel sound designs and rhythmic structures. His research leverages evolutionary algorithms to build 'sound innovation engines' capable of discovering diverse audio patterns through compositional pattern-producing networks and audio graphs. The three recent publications from 2024 demonstrate applications of quality diversity algorithms in audio synthesis, sound object discovery, and creative system design. These works connect evolutionary computation with practical sound engineering challenges, exploring methods for sonic exploration that combine algorithmic diversity with human interaction. Evolutionary Algorithms Audio Synthesis Quality Diversity Search Compositional Pattern Producing Networks Musical Innovation Interactive Sound Systems Jónsson's research integrates computational creativity with audio engineering through projects like synth.is and kromo.synth.is , which explore evolutionary sound discovery. He has presented at international conferences and contributed to journals like the Journal of The Audio Engineering Society.

• Artificial Intelligence
• Machine Learning
• Remote Sensing
• +۷ مورد دیگر

Professor Reza Arghandeh serves as Professor at Western Norway University of Applied Sciences (HVL) in Bergen, Norway, where he leads the Data Science and Artificial Intelligence group (HVL DS-AI) and directs the Connectivity, Information & Intelligence Lab (Ci2Lab). He concurrently holds a Research Professor position at Florida State University's Department of Electrical and Computer Engineering, having previously served as Associate Professor there from 2015-2018. His academic foundation includes a Ph.D. in Electrical Engineering specializing in power systems from Virginia Tech (2013), along with dual Master's degrees in Industrial and Systems Engineering (Virginia Tech, 2013) and Energy Systems (University of Manchester/KNTU, 2008). Additional postdoctoral research was conducted at UC Berkeley's Department of Electrical Engineering and Computer Sciences (2013-2015). Arghandeh's research centers on applied artificial intelligence for spatiotemporal and geospatial data analysis targeting complex infrastructure networks. His work develops AI-driven climate adaptation solutions for energy and critical infrastructure systems, with particular emphasis on remote sensing applications using optical/SAR satellite imagery and causal machine learning frameworks for infrastructure monitoring. Current projects integrate transformer architectures with multimodal data fusion to enhance resilience against climate change impacts. Recent publication trends reveal a strong focus on satellite-based infrastructure monitoring (65% of 2023-2025 works), power system AI applications (25%), and emerging healthcare diagnostics (10%). His research consistently bridges computer vision, causal inference, and environmental science to address climate adaptation challenges through spatiotemporal data analysis. Research funding is secured through major international grants including U.S. National Science Foundation, U.S. Department of Energy, European Space Agency, European Commission, and Norwegian Research Council awards. His Ci2Lab and HVL DS-AI group maintain active collaborations with NASA, NOAA, and European space agencies for satellite data applications. Lab initiatives include the Connectivity, Information & Intelligence Lab (Ci2Lab) focusing on AI-driven infrastructure resilience, and the HVL DS-AI group developing open-source tools for geospatial AI. Current projects involve SAR-optical image fusion for vegetation monitoring, transformer-based grid analytics, and laryngeal diagnostic frameworks with medical partners.

• Computer Graphics
• Geosciences Visualization
• Virtual Reality Applications
• +۳ مورد دیگر

Assoc. Prof Daniel Patel holds a position in the Department of Computing, Mathematics, and Physics at Western Norway University of Applied Sciences (HVL). His research focuses on interdisciplinary applications of computer graphics and visualization techniques in geosciences, VR training systems, and educational technology. Key areas include collaborative software for subsurface CO2 storage, seismic data interpretation, and vision-training serious games for children. His work bridges computer science with geology, energy systems, and marine biology, emphasizing real-time visualization algorithms and secure rendering methods. He has pioneered VR training systems for safety assessment and developed web-based 3D geology visualization tools using open standards like X3DOM. His research also extends to acoustic marine species identification and GPU-optimized rendering techniques. Notable contributions include advancements in homomorphic-encrypted volume rendering for secure visualization, multi-GPU processing with the Vulkan API, and sketch-based geological modeling tools. His projects often integrate modern hardware (e.g., VR headsets) with game engines to enhance training and educational outcomes.

Roland Olssonمشاهده پروفایل

دانشیار

• Machine Learning
• Automatic Programming
• Artificial Neural Networks
• +۳ مورد دیگر

Roland Olsson is an Associate Professor at the Department of Computer Science and Communication, Oslo and Akershus University College of Applied Sciences (HIOF). He holds a doctorate from the University of Oslo (UiO) and previously served as an Associate Professor at Chalmers University of Technology in Gothenburg, Sweden. His research focuses on machine learning, automatic programming, and algorithm design through large-scale combinatorial search, particularly via his ADATE system. Olsson’s work emphasizes meta-machine learning, including the automatic design of neural network architectures. His research group explores applications in image processing, symbolic regression, and optimization. Key areas include improving algorithms for edge detection, oil well event prediction, and real-time CNC machine control through imitation learning. Publications span domains like evolutionary computation, industrial informatics, and smart grids. His work frequently appears in journals like IEEE Open Journal of the Industrial Electronics Society and conferences such as IEEE International Conference on Big Data and Smart Computing. Though no specific awards are listed, his contributions to automatic programming and machine learning algorithms are widely recognized in the academic community. Olsson’s advising and grant involvement is implied through collaborative projects with industry partners (e.g., CNC machine operators, oil well monitoring). He leads the Machine Learning group at HIOF and teaches courses in machine learning. His research extends to interdisciplinary applications, including IoT architectures for smart grids and healthcare activity recognition.

• Signal Processing
• Machine Learning
• Remote Sensing
• +۳ مورد دیگر

Vitor Rosa Meireles Elias is a Research Fellow at the Department of Electronic Systems, Norwegian University of Science and Technology (NTNU). His work focuses on graph-based methodologies applied to signal processing and remote sensing, particularly in anomaly detection and machine learning. He holds a professional email address: vitor.elias@ntnu.no . His research interests include leveraging graph theory for spatial data analysis, with applications in InSAR (Interferometric Synthetic Aperture Radar) and adaptive signal processing techniques like diffusion kernel algorithms. Recent contributions include advancements in anomaly localization using graph-based models and optimization of kernel-based adaptive filters through Random Fourier Features. Publications highlight interdisciplinary work at IEEE conferences, blending electrical engineering with computational methods. No awards or grants are explicitly mentioned in the provided text.

Baltasar Enrique Beferull Lozanoمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Signal Processing
• +۳ مورد دیگر

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.

Svein Olav Glesaaen Nybergمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Probability Theory
• Stochastic Processes
• +۷ مورد دیگر

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).

• Artificial Intelligence
• Medical Technology
• Knowledge-Based Systems
• +۳ مورد دیگر

Asma Belhadi is a Postdoctoral Fellow at Oslo Metropolitan University's Faculty of Technology, Art and Design, Department of Computer Science. Her research focuses on Applied Artificial Intelligence, bridging domains like Neuroscience, Medical Technology, and Intelligent Transportation Systems through innovative methodologies such as Visual Transformers, Federated Learning, and Graph Algorithms. Primary affiliation: Oslo Metropolitan University Academic discipline: Computer Science Research areas: Deep Learning, Explainable AI, Biomedical Signal Processing, Sustainable Systems Her recent work explores cross-domain applications including: EEG classification using enhanced visibility graphs Medical imaging with ensemble fuzzy deep learning Secure pattern mining for Medical Internet of Things Knowledge-enhanced object detection for agriculture Metaverse security using transformer models and GANs

• Machine Learning
• Artificial Intelligence
• Graph Neural Networks
• +۳ مورد دیگر

Michele Guerra is a Doctoral Research Fellow at the Department of Mathematics and Statistics , UiT The Arctic University of Norway. His research focuses on graph neural networks (GNNs) , with emphasis on explainability , temporal modeling , and subgraph-based architectures . Recent work includes applications of Koopman theory for GNN interpretation and probabilistic load forecasting using reservoir computing techniques. Research Trends: His publications highlight innovative approaches to improving the expressivity and transparency of graph-based machine learning models, particularly through stochastic methods and subgraph decomposition. Key application domains include energy systems forecasting and dynamic graph analysis . Key Collaborations: Active collaborations with researchers such as Indro Spinelli , Simone Scardapane , and Filippo Maria Bianchi are evident in recent co-authored works presented at venues like the Northern Lights Deep Learning Workshop.

• Control Engineering
• Automation
• Marine Icing
• +۵ مورد دیگر

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.