جست‌وجوی استاد و پژوهشگر

• Statistical Machine Learning
• Signal Processing
• Data Fusion
• +۱۷ مورد دیگر

Salvo Rossi is a Full Professor of Statistical Machine Learning for Signal Processing at the Department of Electronic Systems, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU). He holds leadership roles as Deputy Director for Research and Deputy Manager of the Centre for Green Shift in the Built Environment. Additionally, he serves as a part-time Senior Research Scientist at SINTEF Energy Research. His work bridges academia and industry, with significant contributions to signal processing, machine learning, and IoT systems. Research Interests: Statistical Machine Learning Data Fusion in Wireless Sensor Networks Digital Twins and Industrial IoT Anomaly Detection and Fault Diagnosis Federated and Distributed Learning Graph Signal Processing His recent publications reflect a strong trend toward intelligent, distributed systems for industrial monitoring, particularly in energy and safety-critical environments. The integration of machine learning with signal processing for decision fusion in sensor networks is a central theme. Scientific Awards and Recognition: Exemplary Senior Editor, IEEE Communications Letters (2018) Department Ambassador, NTNU (2016) IEEE Senior Member (since 2011) Editorial and Professional Service: Senior Area Editor, IEEE Transactions on Signal and Information Processing over Networks (since 2025) Topical Editor, IEEE Sensors Journal (since 2022) Area Editor, IEEE Open Journal of the Communications Society (2019–2023) Executive Editor, IEEE Communications Letters (2019–2021) General Chair, IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), Trondheim, 2022 He has advised numerous PhD and master’s students (not explicitly listed), led major research projects in IoT and green energy, and contributed to national and international research initiatives. His lab, SPIN (Signal Processing Group), focuses on intelligent signal processing for real-world applications in smart environments and Industry 4.0.

• Cloud computing
• Cybersecurity
• Software Engineering
• +۷ مورد دیگر

Paal Engelstad is a Professor at Oslo Metropolitan University's Faculty of Technology, Art and Design, Department of Computer Science. His research focuses on Cloud computing , Cybersecurity , and Software Engineering , with significant contributions to Graph Neural Networks (GNNs) and their applications in network analysis, renewable energy forecasting, and telecom systems. Research interests span Applied Artificial Intelligence and Autonomous Systems and Networks . Recent publications address spatio-temporal wind forecasting , GNN propagation schemes , and 5G network security . He contributes to advancements in renewable energy integration and telecom protocol analysis .

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

Muhammad Mudassar Yamin serves as an Associate Professor in the Department of Information Security and Communication Technology at the Norwegian University of Science and Technology (NTNU), Gjøvik campus. His academic work spans cybersecurity research, artificial intelligence applications, and cyber range development, with significant contributions to security exercise frameworks and adversarial machine learning. His research interests focus on Cybersecurity , particularly in cyber ranges, security assurance, and AI-driven threat detection. Key areas include Kill Chain analysis, adversarial machine learning for hate speech detection, and security exercise scenario generation. Recent work demonstrates increasing integration of large language models for cybersecurity training and multilingual threat detection systems. Yamin's publication trends show consistent output in top cybersecurity venues, with recent emphasis on AI applications for security (2022-2025). His work bridges theoretical frameworks and practical implementations, particularly in cyber range technologies and threat intelligence systems. Collaborations with researchers like Basel Katt and Ehtesham Hashmi appear frequently across publications. He teaches courses including IIK3100 - Ethical Hacking and Penetration Testing and TTM4175 - Introduction to Cyber Security and Data Communication, reflecting his expertise in practical security education. His doctoral thesis (2022) focused on modeling and analyzing attack-defense scenarios for cyber ranges, establishing the foundation for his current research trajectory.

Anamaria Costacheمشاهده پروفایل

دانشیار

• Fully Homomorphic Encryption
• Privacy-Preserving Machine Learning
• Lattice-Based Cryptography
• +۳ مورد دیگر

Anamaria Costache is an Associate Professor at the Norwegian University of Science and Technology (NTNU) in the Department of Information Security and Communication Technology. Previously, she held roles including a postdoc at Royal Holloway, University of London (RHUL), a Research Scientist at Intel AI Research, and a part-time position at Intel. She earned her PhD from the University of Bristol and undergraduate/master's degrees from the University of Warwick, focusing on number theory and algebraic structures like Brauer Groups. Her research interests center on fully homomorphic encryption (FHE), privacy-preserving machine learning, lattice-based cryptography, and post-quantum security protocols. Her work is supported by Intel, addressing challenges in secure computation on encrypted data. She has authored numerous publications in top venues like CRYPTO, PKC, and IEEE conferences, focusing on FHE optimizations, biometric systems, and cryptographic security analyses. Teaching includes courses on applied cryptography, network security, and quantum-safe encryption at NTNU. She co-chairs NIKT 2019 and the WAHC workshops (2022–2024), and serves on editorial boards for journals like IACR Communications in Cryptology. She actively participates in program committees for conferences such as MathCrypt and FHE.org. Professional service includes the Lattigo Advisory Committee and contributions to the Bristol Crypto Blog. Her current projects emphasize verifiable computation over encrypted data and resilient authentication mechanisms in biometric systems.

Anna Plaksienkoمشاهده پروفایل

پژوهشگر ارشد

• High-dimensional statistics
• Omics data integration
• Multi-omics analysis
• +۴ مورد دیگر

Dr. Anna Plaksienko is a Research Fellow in High-dimensional Statistics at the University of Oslo's Institute of Basic Medical Sciences, Faculty of Medicine. Her work focuses on statistical methods for omics data integration, particularly in multi-source high-dimensional regression and graphical models. She develops computational tools like the methyLImp2 and jewel R packages for missing value imputation and joint network estimation. Education: PhD in Mathematics (2021) - Gran Sasso Science Institute, Italy BSc in Mathematics (2016) - Novosibirsk State University, Russia Research Interests: She explores differential network estimation, stability selection, and error control in omics data (RNA-Seq, methylation, lipidomics). Her methods aim to uncover biological insights from multi-omics integration and high-dimensional datasets. Labs/Teams: She is part of the High-dimensional Statistics research group at the Institute of Basic Medical Sciences, collaborating on projects involving statistical methodology and computational biology.

• Recommender Systems
• Privacy Issues in AI
• Fake News Detection
• +۳ مورد دیگر

Özlem Özgöbek is an associate professor at the Department of Computer Science at NTNU, focusing on recommender systems, privacy in AI, and fake news detection. She holds a part-time role as a program manager at the Norwegian Research Center for AI Innovation (NorwAI) since November 2023. She coordinates the International Work-Integrated-Learning in Artificial Intelligence (IWIL AI) project and actively participates in organizing committees such as the INRA Workshop Series and the Norwegian Big Data Symposium (NOBIDS). She collaborates with the Center for Excellent IT Education (Excited) center, contributing to advancements in semantic web technologies and machine learning applications in education. Her research bridges academic and industrial challenges, emphasizing ethical AI deployment and user-centric systems. Key contributions include multimodal fake news detection frameworks, educational technology innovations, and sustainable wardrobe recommendation systems leveraging Linked Open Data. She has organized multiple international workshops (e.g., INRA 2021–2023) and contributed to benchmarking initiatives like MediaEval’s news image analysis tasks. Her academic work spans over 30 peer-reviewed publications since 2014, including studies on classroom interaction tools, privacy perceptions in recommender systems, and the Adressa news dataset for evaluation purposes. She maintains strong ties with collaborative projects such as the IWIL AI initiative, fostering international education-industry partnerships in AI.

Paal Engelstadمشاهده پروفایل

استاد

• Machine Learning
• Wireless Communications
• Renewable Energy Systems
• +۴ مورد دیگر

Paal Engelstad is a Professor at the University of Oslo, affiliated with the Section for Autonomous Systems and Sensor Technologies at the Institute of Transport Economics (ITS). He holds a full-time academic position. His research focuses on machine learning, wireless communications, renewable energy systems, and network security, with applications in IoT, reinforcement learning, and graph neural networks. His work addresses challenges in energy forecasting, autonomous systems, and secure information exchange. Key research interests include: Deep learning for solar and wind energy forecasting Graph neural networks and their applications in recommendation systems and network analysis Non-orthogonal multiple access (NOMA) in IoT and wireless networks Reinforcement learning for autonomous systems and control Security classification and anomaly detection in networks His recent publications highlight contributions to probabilistic solar irradiance forecasting, graph-based wind prediction models, and reinforcement learning algorithms for autonomous vehicles. He collaborates extensively with international researchers and has led projects like DESSI (Distributed Energy System and Security Infrastructure). Engelstad is involved in the following initiatives: Development of secure cross-domain information exchange frameworks Advances in energy-efficient IoT and sensor networks Applications of machine learning in cybersecurity and network optimization His research lab focuses on interdisciplinary solutions bridging transport economics, energy systems, and intelligent sensor technologies.

• Systems Biology
• Semantic Web
• Knowledge Management
• +۵ مورد دیگر

Martin Kuiper is a Professor in Systems Biology at the Norwegian University of Science and Technology (NTNU). He transitioned from industry to academia in 2008, previously working at VIB Institute in Gent, Belgium. His research focuses on integrating biological knowledge using ontologies, semantic web technologies, and logical modeling. The Kuiper lab develops tools like BioGateway and CausalBuilder, and contributes to initiatives such as DrugLogics and GRECO/GREEKC. Education: M.Sc. (1982) and PhD (1987) in Biology from the University of Groningen, followed by postdoctoral work at Ohio State University and Utrecht University. Career highlights include roles in industry (KeyGene NV, GenScope, Aventis CropScience) before joining academia. Research interests emphasize knowledge management, bioinformatics tools, and systems biology approaches to drug development and gene regulation. Key projects include modeling drug resistance in colon cancer (COLOSYS) and advancing causal interaction standards (MI2CAST). Publications span bioinformatics, computational biology, and systems medicine, with contributions to journals like iScience , Nucleic Acids Research , and Bioinformatics . Collaborations involve interdisciplinary teams from medicine and humanities at NTNU. Lab activities include developing ontologies for gene regulation and drug target identification, with applications in cancer therapy and systems medicine. Active in open science initiatives like the Gene Regulation Knowledge Commons.

• Artificial Intelligence
• Machine Learning
• Graph Neural Networks
• +۵ مورد دیگر

Gustavo Borges Moreno e Mello is an Associate Professor in the Department of Computer Science at Oslo Metropolitan University's Faculty of Technology, Art and Design. Based in office SG209 at Stensberggata 29, Oslo, he actively contributes to the Applied Artificial Intelligence research group with focus on cutting-edge computational methodologies. His research spans artificial intelligence with emphases on graph neural networks, multilingual NLP, reinforcement learning, and medical imaging. Key interests include developing propagation schemes for GNNs, cross-lingual BERT adaptations, rodent fMRI analysis, and empathy assessment in human-computer interaction systems. His work bridges theoretical AI with practical healthcare and communication applications. Analysis of his 2020-2024 publications reveals strong thematic continuity in neural architecture design, particularly HITS-GNN variants for graph analytics and time-representation models. Recent work increasingly integrates neuroscience principles with reinforcement learning while maintaining Nordic language processing applications, notably in English-Norwegian BERT alignment. No information regarding graduate student supervision or research grants was found in the provided materials. He maintains active collaboration within OsloMet's Applied Artificial Intelligence research group, focusing on interdisciplinary projects that connect computer vision, natural language processing, and neuro-inspired computing with real-world healthcare and communication challenges.

Xing Caiمشاهده پروفایل

استاد

• Parallel programming
• High-performance computing
• Performance modeling
• +۸ مورد دیگر

Xing Cai is a Professor at the Department of Informatics, University of Oslo, specializing in Scientific Computing and Machine Learning. His academic career spans several decades with a consistent focus on high-performance computing and its applications to complex scientific problems. He maintains an active research profile with numerous publications in top-tier journals and conferences. Professor Cai's research interests encompass parallel programming and high-performance computing, performance modeling and optimization, automated code generation, heterogeneous computing, and numerical methods for solving partial differential equations. His work extends to specialized applications in computational cardiology, computational geoscience, and biomedical computing. His research bridges theoretical computer science with practical applications in medicine and earth sciences, demonstrating exceptional interdisciplinary reach. An analysis of his recent publications (2019-2024) reveals a strong trend toward leveraging novel hardware architectures (GPUs, AI processors, specialized accelerators) for scientific computing, with particular emphasis on cardiac modeling applications. His work shows increasing sophistication in hardware-aware algorithm design, with publications spanning from fundamental performance modeling to domain-specific applications. The interdisciplinary nature of his work is evident in the diverse range of journals and conferences where he publishes, from computer science venues to specialized medical and geoscience publications. Professor Cai leads or participates in several significant research projects including the EuroHPC Centre of Excellence: Numerical Modeling of Cardiac Electrophysiology at the Cellular Scale (MICROCARD-2), High resolution simulation of cardiac electrophysiology on realistic whole-heart geometries, Maelstrom Associate Team, ODISSEE, Simula-Berkeley Education and Research collaboration (SIMBER), and aCG eX3: Experimental Infrastructure for Exploration of Exascale Computing. These projects reflect his leadership in both computational methodology development and domain-specific applications. His research group maintains strong collaborations with medical researchers, particularly in cardiac electrophysiology, and with geoscientists working on reservoir simulation. The publications list demonstrates consistent mentorship of junior researchers, with frequent co-authorship patterns suggesting an active supervision of PhD and postdoctoral researchers. His work on the EMI model for cardiac tissue represents a significant contribution to computational cardiology with potential clinical applications. The laboratory environment surrounding Professor Cai's work appears to be well-equipped for high-performance computing research, with access to advanced hardware platforms including GPU clusters, AI processors, and specialized accelerators. His publications on the use of Graphcore IPUs, Xeon Phi processors, and NVIDIA architectures indicate a well-resourced research environment capable of experimenting with cutting-edge hardware.

Carsten Griwodzمشاهده پروفایل

استاد

• Computer Networks
• Distributed Systems
• Network Security
• +۵ مورد دیگر

Carsten Griwodz is a Professor in the Department of Informatics (IFI) at the University of Oslo, specializing in digital infrastructure and security. He leads the Distributed Infrastructure and Security (DIS) research group and contributes to several specialized labs including the Sustainable Immersive Networking Lab (SINLAB), Imagine Beyond 5G Blockchain Lab, and the AliceVision Association. Professor at Department of Informatics, University of Oslo Section leader for DIS: Distributed Infrastructure and Security Group member in Networks and Distributed Systems (ND) Active in sustainable immersive networking and blockchain labs Co-founder of AliceVision open-source photogrammetric framework His research focuses on network performance, edge computing, and immersive technologies. He has pioneered work in cloud gaming QoE, real-time 3D reconstruction, and low-latency systems. Current projects explore redirected walking in VR, GPU programming for tracking, and sustainable networking solutions. Recent publications (2021-2024) demonstrate his expertise in network delay analysis, point cloud compression, virtual reality environments, and GPU-accelerated systems. His work bridges computer science fundamentals with cutting-edge applications in multimedia, security, and health informatics. He supervises numerous master's theses covering diverse topics from wireless streaming challenges to medical imaging advancements, consistently mentoring on topics related to network optimization, immersive technologies, and GPU computing. Supervised 40+ master's theses (2004-2024) Thesis topics span network performance, GPU programming, VR systems, and multimedia processing Active in both theoretical and applied research domains Mentoring interests include HCI, distributed systems, and real-time processing Contributes to education through research-led supervision As a permanent faculty member with extensive publication records and active research groups, Griwodz maintains significant influence in academic circles through both his technical contributions and educational mentorship.

• Machine Learning
• Information Theory
• Neural Networks
• +۳ مورد دیگر

Shujian Yu is an Associate Professor II (part-time) in Machine Learning at the Department of Physics and Technology, UiT The Arctic University of Norway, located in Tromso. He specializes in developing machine learning methodologies with applications spanning neuroscience, computer vision, and industrial processes. Research Focus His research integrates information theory with machine learning, focusing on: Novel divergence measures (Cauchy-Schwarz, Rényi entropy) for time-series analysis Interpretable neural networks for brain network-based psychiatric diagnosis Causal discovery methods for industrial fault detection Information bottleneck approaches for feature selection Publication Trends Recent works (2022-2025) demonstrate strong emphasis on information-theoretic learning, graph neural networks, and applications in neuroscience and computer vision. Publications appear in premier venues including IEEE Transactions, Neural Networks, and ICLR, with consistent focus on model interpretability and efficiency. Research Group Member of UiT's Machine Learning Group, collaborating with researchers in neuroscience, signal processing, and data science.

• Data Science
• Machine Learning
• Graph Signal Processing
• +۵ مورد دیگر

Prof. Baltasar Beferull-Lozano is Professor, Chief Research Scientist/Research Professor and Head of the Signal and Information Processing for Intelligent Systems Department at Simula Metropolitan . His leadership role spans directing cutting-edge research at the intersection of data science, networked systems and artificial intelligence. Research Interests: His group pursues a broad agenda that includes Data science and machine learning Online optimization and streaming algorithms Graph signal processing and higher-order networks Intelligent sensing, signal processing and inference Cyber-physical systems and IoT In-network distributed and cooperative intelligence AI-driven networks and communication systems Across these themes, his work consistently develops mathematically rigorous yet computationally efficient frameworks that enable learning and inference on complex, dynamic networked data. Publication Trends: Over 2021-2025 he has produced a prolific stream of journal and conference papers. Recurrent topics include topological signal processing , graph neural networks , transfer learning for wireless radio mapping , online kernel methods , simplicial and higher-order models , and non-linear topology identification . These works collectively advance both theoretical foundations and practical algorithms for next-generation networked AI systems. Scientific Awards: No awards are listed in the provided text. Advising & Grants: While no explicit grants or student lists are supplied, the volume and senior authorship of publications suggest Prof. Beferull-Lozano leads a sizeable research group comprising PhD candidates and post-doctoral researchers working on externally funded projects. Labs & Teams: He heads the Signal and Information Processing for Intelligent Systems Department at Simula Metropolitan, a multidisciplinary unit focused on machine learning, signal processing and networked intelligence.

• Signal Processing
• Machine Learning
• Graph Theory
• +۵ مورد دیگر

Rohan Thekkemarickal Money is a Postdoctoral Fellow at Simula, affiliated with the Department of Signal and Information Processing for Intelligent Systems within the Simula Metropolitan Centre. His research focuses on advanced signal processing, graph theory, and machine learning applications to dynamic networks and topological data analysis. Recent research trends include developing simplicial vector autoregressive models for network dynamics, online topology identification for higher-order systems, and privacy-aware learning frameworks . His work addresses challenges in time-series forecasting, edge flow backcasting, and capacity estimation for lithium-ion batteries using graph-based methodologies. Rohan collaborates with researchers like Dr. B. Beferull-Lozano and Dr. E. Isufi, contributing to interdisciplinary projects at the intersection of signal processing , network science , and topological data analysis . His publications demonstrate expertise in handling nonlinear systems with sparse data and designing scalable algorithms for real-time applications.

• Robotics
• Resilient Robotic Autonomy
• Robot Control
• +۵ مورد دیگر

Prof. Konstantinos Alexis is a Professor in the Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU). His research focuses on resilient robotic autonomy, emphasizing robot control, path planning, and Simultaneous Localization and Mapping (SLAM). He leads major international grants from agencies like DARPA, NSF, and the Norwegian Research Council, advancing autonomous systems for extreme environments such as subterranean exploration and search-and-rescue operations. His work integrates advanced control strategies with field robotics, demonstrated through projects like the DARPA Subterranean Challenge. Key contributions include robust model predictive control for UAVs, contact-based inspection planning, and graph-based exploration path planning. Alexis has organized workshops on aerial robotics at top conferences like ICRA and ICUAS, and his team develops open-source tools for robotics autonomy. Award-winning publications include the IET 2014 Premium Award and multiple best-paper recognitions. His research extends to solar-powered UAVs, thermal-inertial localization, and multi-modal sensor fusion for degraded environments. The Autonomous Robots Lab at NTNU is central to his research activities.