جست‌وجوی استاد و پژوهشگر

Thomas Peter Plagemannمشاهده پروفایل

استاد

• Data Stream Processing
• Sensors and AI for Health
• Internet of Things
• +۳ مورد دیگر

Thomas Peter Plagemann is a Professor in the Department of Informatics at the University of Oslo , specializing in Data and Knowledge Systems . His research focuses on data stream processing , sensors and AI for health , Internet of Things , and mobile network applications . Current projects: Parrot (Privacy Engineering for IoT), Respire (Explainable ML for Sleep Disorders), and TRAMP (Real-Time Application Mobility) Editorial leadership: ACM Transactions on Multimedia Computing and past roles at Computer Communications and Multimedia Systems Recent publications analyze operator migration in stream processing , explainable AI for pediatric sleep apnea , and privacy-preserving healthcare systems . His work combines distributed computing , sensor networks , and machine learning applications in medical and mobile environments.

Shiliang Zhangمشاهده پروفایل

پژوهشگر ارشد

• Smart Grid
• Transactive Energy Management
• Privacy Preservation
• +۴ مورد دیگر

Shiliang Zhang is a Postdoctoral Fellow at the University of Oslo working with the Networks and Distributed Systems research group. His research focuses on privacy preservation in smart grid and transactive energy management systems, including the Privacy preserving Transactive Energy Management (PriTEM) project. He teaches Energy Informatics and Artificial Intelligence for Energy Informatics courses while developing interactive visualization tools for Norwegian energy infrastructure. His primary research areas encompass Smart Grid systems, Transactive Energy Management, and Privacy Preservation through Differential Privacy techniques. He applies Artificial Intelligence and Energy Informatics to address renewable energy integration challenges, grid resilience, and autonomous navigation systems. Recent work explores bionic data-driven approaches for underwater navigation and anomaly-resistant control mechanisms. Publications from 2022-2025 reveal strong interdisciplinary focus on machine learning applications for energy systems and navigation. Key themes include privacy-preserving pricing schemes for smart grids, robust control under system uncertainties, and deep reinforcement learning for geomagnetic navigation. His work spans IEEE Transactions, SmartGridComm, and Mathematics journals with significant contributions to resilient energy infrastructure. No scientific awards were mentioned in available sources. Zhang has no listed advisees but actively contributes to the PriTEM project within Networks and Distributed Systems. His research leverages collaborations across energy informatics domains, evidenced by publications in high-impact venues and development of practical tools like Norway's energy consumption and solar panel distribution maps. He operates within the Networks and Distributed Systems research group, developing interactive visualizations for Norwegian energy infrastructure including municipal energy consumption (May 2025), Oslo's solar panel distribution (April 2025), and national power lines (May 2025). His work bridges theoretical control systems with real-world energy applications through the PriTEM project.

Vladimir Oleshchukمشاهده پروفایل

استاد

• Blockchain Technology
• Attribute-Based Access Control
• E-Health Security
• +۵ مورد دیگر

Vladimir Oleshchuk is a Professor at the Department of Information and Communication Technology, University of Agder. His research focuses on Blockchain technology Attribute-based access control E-health security Trust management in networks Privacy-preserving protocols Wireless sensor network security His recent publications emphasize integrating blockchain with large language models (LLMs) for Web3 security, lightweight access control for IoT, and decentralized authentication frameworks. Key contributions include Delegatable attribute-based encryption schemes Privacy-preserving mechanisms for collaborative environments Trust-aware RBAC models He collaborates with researchers like Harsha Gardiyawasam Pussewalage and Ole-Christoffer Granmo, working within the Centre for Integrated Emergency Management (CIEM) research group. Current projects address security challenges in mobile and distributed systems, including unattended wireless sensor networks and 5G infrastructure.

• Deep Learning
• Medical Multimedia
• Explainable AI
• +۵ مورد دیگر

Steven Hicks is a Senior Research Scientist in the Holistic Systems department at Simula Metropolitan. His work focuses on applying artificial intelligence techniques to medical and multimedia applications, with particular emphasis on explainability and transparency in AI systems. Dr. Hicks' research spans multiple domains including medical imaging, particularly in gastrointestinal endoscopy and reproductive medicine. His primary interests include Deep Learning , Medical Multimedia , Explainable AI , and Computer Vision . His work has significant applications in polyp detection, sperm analysis, ECG interpretation, and medical image segmentation, contributing to advancements in diagnostic accuracy and clinical decision support systems. Analysis of Dr. Hicks' recent publications reveals a strong focus on medical applications of AI with emphasis on transparency and explainability. His work frequently addresses challenges in gastrointestinal endoscopy, reproductive medicine, and cardiology. A notable trend is his involvement in creating benchmark datasets like Kvasir-VQA and VISEM-Tracking, and organizing challenges through MediaEval and ImageCLEF to advance the field of medical AI and establish standardized evaluation frameworks. Dr. Hicks actively collaborates with medical professionals and researchers across multiple institutions, contributing to the development of practical AI solutions for real-world medical problems. His work bridges the gap between theoretical AI research and clinical applications, with significant contributions to both technical innovations and the creation of essential resources for the medical AI community.

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.

Jill Walker Rettbergمشاهده پروفایل

استاد

• Digital Culture
• Machine Vision
• Artificial Intelligence Narratives
• +۵ مورد دیگر

Jill Walker Rettberg is a Professor of Digital Culture at the University of Bergen, Norway, and Co-Director of the Center for Digital Narrative (CDN), a Norwegian Center of Excellence funded by a €15M grant (2023–2033). She leads the ERC Advanced Grant project AI STORIES (2024–2029) and previously directed the ERC Consolidator project Machine Vision in Everyday Life (2018–2024). Her research focuses on how technologies like AI and machine vision shape narratives and cultural practices. Education and Background: Rettberg holds a PhD in Humanistic Informatics from the University of Bergen and has a background in Comparative Literature. She has been a pioneer in digital culture studies since the late 1990s, winning awards such as the Ted Nelson Newcomer Award (1999) and the John Lovas Memorial Award (2017). Research Interests: Her work spans AI narratives, machine vision ethics, social media storytelling, electronic literature, and the societal impacts of algorithms. Recent projects include analyzing generative AI’s cultural biases and exploring how machine vision influences human perception. Publications: Rettberg authored Machine Vision: How Algorithms Are Changing the Way We See the World (2023), Seeing Ourselves Through Technology (2014), and Blogging (2008). Her articles and datasets investigate topics like AI-generated narratives, facial recognition bias, and digital art interfaces. Grants and Awards: Besides ERC grants, she has been recognized with the Meltzer Foundation Prize (2006) and serves in leadership roles at UiB AI and LEAD AI networks. Teaching and Mentoring: Rettberg supervises PhD students in digital culture, including work on conversational apps for chronic patients and haptic interfaces in digital art. She teaches courses on machine vision, critical digital theory, and AI ethics.

• Computer Vision
• Biometrics
• Artificial Intelligence
• +۸ مورد دیگر

Kiran Raja is an Associate Professor at the Department of Computer Science within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology . With a focus on biometric security , computer vision , and educational technology , Raja's academic career spans both technical innovation and pedagogical research. Research interests include: Biometric Security – Face morphing attack detection, deepfake defense, and cancelable template protection Computer Vision – Facial recognition, image generation, and UAV-based person re-identification Medical AI – Healthcare data protection and medical image super-resolution Educational Reform – Redesigning computer science curricula and cross-campus teaching methodologies Recent work trends show a strong emphasis on privacy-preserving AI , ethical biometric systems , and educational technology . Key technical contributions include Vision Transformers for morphing attack detection GAN-based synthetic face morphing datasets Quantization-enhanced models for edge computing Graph Neural Networks for activity recognition Collaborations span institutions like IEEE, Springer, and SPIE, with consistent co-authorship with researchers such as Christoph Busch , Raghavendra Ramachandra , and Haoyu Zhang .

• Computer Science
• Information Security
• Machine Learning
• +۳ مورد دیگر

Ahmad Hassanpour is a Research Fellow at the Norwegian University of Science and Technology (NTNU), affiliated with the Department of Information Security and Communication Technology. His research focuses on privacy-preserving technologies, machine learning, and computer vision, with particular expertise in facial recognition systems, generative adversarial networks (GANs), and differential privacy. Key research interests include addressing privacy-accuracy-fairness trade-offs in AI systems, developing advanced inpainting techniques for face reconstruction, and enhancing privacy in online social networks. He has contributed to frameworks like E2F-Net and E2F-GAN for eyes-to-face generation, as well as IRL-Net for localized image inpainting. Hassanpour has presented at prominent conferences such as IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), highlighting his work on privacy leakage analysis (2023) and privacy measurement frameworks (2022).

• Lattice-based Cryptography
• Homomorphic Encryption
• Privacy-Preserving Protocols
• +۳ مورد دیگر

Jeongeun Park is an Associate Professor at the Department of Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU). Her research focuses on lattice-based cryptography, homomorphic encryption, and privacy-preserving protocols, with a particular emphasis on fully homomorphic encryption (FHE) and its applications. She holds a PhD in Mathematics from Ewha Womans University (2021) and was a postdoctoral researcher at KU Leuven (2021–2024). Her educational background includes a BSc in Mathematics (2015) and a combined master-doctoral degree in Cryptology (2021), both from Ewha Womans University. Research interests span theoretical and practical advancements in cryptography, including FHE algorithms, privacy-preserving machine learning, and secure multiparty computation. She has contributed to foundational work on NTRU-based FHE, oblivious RAM, and privacy-preserving protocols like Pancea and SortingHat. Key awards include the 2017 Crypto Contest Special Award and NRF-funded research grants. Her work has been published in top venues such as Asiacrypt, SCN, and IEEE Transactions. She actively participates in academic governance, serving on program committees for PETS 2026, ISC 2025, and others, and reviews for major conferences and journals.

• Robotics
• Human-Robot Interaction (HRI)
• Machine Learning
• +۵ مورد دیگر

Weria Khaksar is an Associate Professor at the Department of Mechanical Engineering and Technology Management at the Norwegian University of Life Sciences (NMBU), Faculty of Engineering, Science and Technology. Her research focuses on advanced robotics applications, particularly in healthcare, navigation, and sensor technologies. She specializes in Human-Robot Interaction (HRI), Machine Learning, and SLAM (Simultaneous Localization and Mapping) systems. Her work addresses ethical challenges in care robotics, privacy-preserving algorithms, and the integration of autonomous systems in healthcare and agriculture. Key research interests include: Development of care robots for elderly healthcare environments Integration of vision-based sensors and multi-modal datasets for human monitoring Optimization of motion planning algorithms using reinforcement learning and MPC Ethical and privacy considerations in human-robot collaboration Agricultural robotics for precision farming and forest mapping Her recent work (2021–2025) emphasizes: A qualitative review of 20 European elderly care robotics projects Advances in privacy-preserving HRI through multi-modal data Real-time SLAM systems for GNSS-interrupted environments Collision-free manipulation strategies for robotic arms No scientific awards are explicitly mentioned, but her extensive publication record demonstrates significant contributions to robotics research. She has advised no formally listed students, but her work engages interdisciplinary teams across engineering, healthcare, and environmental science. Her research is applied in: Robotics labs focusing on care systems and precision agriculture Multi-sensor integration for autonomous navigation Development of ethical frameworks for care robots

Poushali Senguptaمشاهده پروفایل

پژوهشگر

• Explainable AI
• Distributed Machine Learning
• Data privacy
• +۳ مورد دیگر

Poushali Sengupta joined the University of Oslo's Department of Informatics as a Researcher in November 2021. Her work focuses on Explainable Artificial Intelligence (XAI), Distributed Machine Learning, and privacy-enhancing techniques, supervised by Prof. Yan Zhang with co-supervisors Prof. Sabita Maharjan and Frank Eliassen. Research Interests: Explainable AI (XAI) for balancing accuracy and model transparency Differential privacy in distributed learning systems Security mechanisms for federated learning environments Privacy-utility tradeoffs in machine learning Applications in energy informatics and networked systems Publications: Her recent work includes advancements in differentially private data shuffling (BUDS/BUDS+), federated learning scalability (Flaps), and fairness-aware privacy frameworks. Labs & Teams: Affiliated with the Networks and Distributed Systems (ND) research group within the Faculty of Mathematics and Natural Sciences.

• Cryptology
• Intrusion detection systems
• Wireless communication security
• +۳ مورد دیگر

Slobodan Petrovic is a Professor of Information Security at the Norwegian University of Science and Technology (NTNU), specifically within the Department of Information Security and Communication Technology (IIK) at the Faculty of Information Technology and Electrical Engineering (IE) in Gjøvik, Norway. His academic career demonstrates a strong commitment to both research and education in cybersecurity disciplines. Professor Petrovic's research interests span across multiple critical domains in information security, with primary focus on Cryptology , Intrusion detection systems , and Wireless communication security . His work consistently bridges theoretical foundations with practical applications, particularly in digital forensics, cryptographic techniques, and network security protocols. The breadth of his research is evident in his extensive publication record spanning nearly two decades. His recent publications (2022-2024) reveal an expanding research scope into emerging security challenges including autonomous vehicle security, 5G network protection for social robots, and physical-layer security implementations for wireless communications. These articles demonstrate his ability to adapt research focus to address contemporary security threats in evolving technological landscapes. Professor Petrovic teaches cryptology and intrusion detection courses at both the Master in Information Security (MIS) program and the PhD program in Information Security at NTNU. He is an active member of the Cryptology Research Group (Norwegian Applied Crypto Lab - NaCl), contributing to Norway's cybersecurity research ecosystem. His collaborative research approach is evident through numerous co-authored publications with researchers including Nguyen Hai Thanh, Katrin Franke, Michael Cheffena Gebresilassie, and others. This collaborative network spans multiple institutions and research domains within information security.

• Machine Learning
• Reinforcement Learning
• Medical Informatics
• +۵ مورد دیگر

Phuong Dinh Ngo is an Associate Professor in Machine Learning at the Department of Physics and Technology , UiT The Arctic University of Norway. With extensive work in healthcare AI and reinforcement learning, their research spans clinical coding, diabetes management, and clinical text de-identification. Affiliation: UiT The Arctic University of Norway Email: phuong.ngo@uit.no Research Interests: Phuong's work focuses on applying machine learning to healthcare challenges, particularly in clinical coding automation, diabetes management systems, and privacy-preserving clinical language models. Their projects include ICD-10 code prediction, telehealth development, and reinforcement learning for blood glucose control. Collaborations: They collaborate with researchers across Scandinavia and internationally, including Miguel Angel Tejedor Hernandez, Taridzo Fred Chomutare, and Fred Godtliebsen. Their publications appear in journals like Journal of Medical Internet Research , Applied Sciences , and Diabetes Technology & Therapeutics . Recent Publications: Their 2025 work on NorDeClin-BERT for ICD-10 prediction in Norwegian clinical texts represents cutting-edge medical NLP research. Earlier studies address diabetes management systems, including risk-averse food recommendation algorithms and reinforcement learning frameworks for type 1 diabetes patients.

• Internet of Things
• Machine Learning
• Data Quality
• +۴ مورد دیگر

Arda Goknil serves as an Assistant Professor in the Department of Computer Science at the Faculty of Technology, Art and Design, Oslo Metropolitan University (OsloMet). His research focuses on critical challenges in Industrial Internet of Things (IIoT), sustainable computing, and data reliability within cyber-physical systems. Dr. Goknil's primary research areas include: Carbon-aware machine learning pipelines for environmental sustainability Intermittent computing solutions for batteryless IoT devices Industrial data quality assurance and repair mechanisms Edge-based AI for manufacturing applications Computer vision in retail technology His work addresses real-world industrial needs through publications in ACM conferences and journals, emphasizing practical implementations in manufacturing and resource-constrained environments. Recent publications (2023-2025) reveal a strong trend toward developing tools like 3D-DaVa for point cloud validation and REPTILE for continual learning in IIoT, demonstrating cross-cutting contributions to data reliability and energy efficiency. While specific advising relationships are unlisted, his research aligns with OsloMet's Innovation, Digital Transformation and Sustainability group. No scientific awards or grant details appear in the provided text, though his active publication record indicates ongoing research leadership in sustainable IoT systems.

Anders Andersenمشاهده پروفایل

استاد

• Adaptive System Architectures
• Multimedia Applications
• Mobility and Context-Sensitive Systems
• +۲ مورد دیگر

Anders Andersen is a Professor and Head of the Department of Computer Science at UiT The Arctic University of Norway. His research focuses on adaptive systems, multimedia applications, context-aware systems, security, and AI/ML. He leads projects like NUDGE (smart nudging for sustainable behavior) and NFC City (NFC-based services). He has extensive teaching experience, supervising numerous master's and PhD students. Affiliations: UiT Department of Computer Science, Open Distributed Systems (ODS) research group Education: Extensive academic background in computer science and related fields (details not explicitly listed). Research Interests: His work spans adaptive system architectures, multimedia systems, mobility/context-aware computing, privacy-preserving techniques, and AI integration. Key projects include secure distributed health data analysis (SNOOP), IoT applications for green transportation, and NFC infrastructure development. Articles Overview: Recent work emphasizes smart nudging methodologies, distributed deep learning optimization, and privacy-preserving frameworks. His publications address cross-platform multimedia systems, context-aware recommendation systems, and security in mobile and cloud environments. Grants/Projects: NUDGE, NFC City, SNOOP, PIA, and others focusing on IoT, health informatics, and adaptive middleware. Labs/Teams: Member of the ODS group, contributing to middleware for distributed applications, adaptive systems, and secure computation.