جست‌وجوی استاد و پژوهشگر

Johannes Skaarمشاهده پروفایل

استاد

• Quantum Field Theory
• Quantum Optics
• Electromagnetics
• +۳ مورد دیگر

Johannes Skaar is a Professor at the Department of Physics, University of Oslo (UiO). He holds a 100% position there since 2017, previously at NTNU. His research focuses on quantum field theory, quantum optics, electromagnetics, metamaterials, photonics, and quantum information. He teaches advanced courses such as FYS4170 Relativistic Quantum Field Theory and FYS1005 Classical Mechanics. His work spans theoretical physics with notable contributions to single-photon states, metamaterial properties, and quantum cryptography security. Skaar’s research integrates foundational physics with applied technologies like metamaterials and quantum communication systems. His studies on Fresnel equations and magnetic permeability have advanced electromagnetic theory. He frequently publishes in top journals like Physical Review A and Physical Review Letters . Research groups: Theoretical Physics at UiO.

• Cryptographic Voting Systems
• Key Exchange Protocols
• Cloud Cryptography
• +۳ مورد دیگر

Kristian Gjøsteen is a Professor at the Department of Mathematical Sciences within the Norwegian University of Science and Technology (NTNU) . He actively contributes to the Algebra Group and specializes in cryptographic systems with a focus on electronic voting , security proofs , and privacy-enhancing technologies . Educational Background: MSc and PhD from NTNU Research Interests: His work spans cryptography , key exchange protocols , cloud security , and formal verification of security mechanisms. Particular emphasis is placed on coercion-resistant voting systems , lattice-based encryption , and blockchain privacy models . Article Trends: Recent publications demonstrate expertise in post-quantum cryptography , machine-checked security , and privacy-preserving voting architectures . Collaborative efforts explore hybrid cryptographic schemes , verifiable decryption , and mix-net implementations for secure elections.

• Cryptography
• Blockchain Technology
• Network Security
• +۷ مورد دیگر

Danilo Gligoroski is a Professor at the Department of Information Security and Communication Technology at NTNU. His research focuses on cryptography, blockchain technology, network security, and 5G systems. He has collaborated extensively on projects involving decentralized ledgers, cybersecurity in healthcare, and hardware implementations of cryptographic algorithms. Key contributions include work on blockchain-based trust systems, secure network slicing, and privacy-preserving protocols. Affiliations: NTNU, Department of Information Security and Communication Technology Primary Research Areas: Cryptography, Blockchain, 5G Networks, Cybersecurity Notable Projects: GRAND Parallel Decoding Framework, GDPR-compliant healthcare blockchain Research interests span cryptographic protocol design, distributed ledger technologies, and their applications in healthcare, telecommunications, and IoT. His work often addresses scalability, privacy, and compliance challenges in decentralized systems. Publications highlight trends in blockchain consensus mechanisms, cryptographic hardware optimization, and network security for next-gen communication systems. Collaborations with industry and academic partners emphasize practical implementations and real-world use cases. Advising and grants include supervision of interdisciplinary projects and contributions to EU-funded cybersecurity initiatives. Active in open-source software development for network slicing and blockchain frameworks.

Audun Jøsangمشاهده پروفایل

استاد

• Cybersecurity
• Artificial Intelligence
• Trust and Reputation Systems
• +۴ مورد دیگر

Audun Jøsang is a Professor of Cybersecurity at the University of Oslo and holds a Visiting Professor position at Queensland University of Technology (QUT) . His work focuses on trust and reputation systems in digital environments, with notable contributions to subjective logic —a mathematical framework for reasoning under uncertainty. Education : MSc in Information Security from Royal Holloway, University of London , MSc and PhD in Telecommunications from Norwegian University of Science and Technology (NTNU) Books : Subjective Logic: A Formalism for Reasoning Under Uncertainty (2016), Cybersecurity: Technology and Governance (2011) His research spans cybersecurity , artificial intelligence , and risk assessment , with applications in online marketplaces , social media , and decision-making under uncertainty . He has developed mathematical models for uncertainty-aware influence propagation and competitive information spread using subjective logic. Audun Jøsang’s work bridges theoretical foundations and industrial applications of computational trust and security analytics . His recent publications explore ethical AI , fair federated learning , and dynamic intelligence assessment for artificial general intelligence development.

• Artificial Intelligence
• Neural Networks
• Cellular Automata
• +۳ مورد دیگر

Stefano Nichele is a Professor at Oslo Metropolitan University's Faculty of Technology, Art and Design, where he leads research in the Department of Computer Science with a focus on Artificial Intelligence. His laboratory investigates the intersection of biological and artificial intelligence through neural networks and cellular automata. Research Focus: Nichele's work bridges computational neuroscience, complex systems, and machine learning. Key themes include: Developing AI tools for neuroscience applications like dementia prediction Modeling neural network dynamics using biological and computational approaches Evolutionary algorithms and quantum computing hybrids Cellular automata frameworks for emergent intelligence Research Projects: AI-Mind: Developing AI-based dementia diagnostic tools FeLT: Human-machine-environment interactions in ecological contexts DeepCA: Biological-artificial intelligence integration SOCRATES: Efficient distributed data analysis Publications: His recent works (2024-2025) primarily explore neural network dynamics, cellular automata applications, and computational neuroscience models. The research demonstrates consistent focus on emergent behavior in complex systems and biological computation.

Linga Reddy Cenkeramaddiمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Autonomous Systems
• Robotics and Automation
• +۷ مورد دیگر

Linga Reddy Cenkeramaddi is a **Professor** in the Department of Information and Communication Technology at the University of Agder (UiA), Norway. He holds a PhD in Electrical Engineering from NTNU (Trondheim, Norway) and Master’s degrees from IIT Delhi and IIT Chennai. His research focuses on Cyber-Physical Systems (CPS), Autonomous Systems, Robotics, AI/ML, Wireless Sensor Networks, and 5G/6G-enabled technologies. He leads projects such as LUCAT (UAV communication tracking) and INCAPS (Indo-Norwegian collaboration in CPS). **Education**: PhD (NTNU), MSc in Physics (IIT Chennai), MSc in Integrated Electronics (IIT Delhi). **Research Interests**: Energy-efficient wireless communication, autonomous vehicles, edge intelligence, mmWave sensing, and smart cities. **Teaching**: Courses include embedded systems, mmWave sensing, and wireless prototyping. **Key Contributions**: Developed low-power embedded systems, contributed to UAV communication protocols, and pioneered AI-driven healthcare solutions. **Grants**: Co-PI in the INMOST offshore mechatronics project. **Labs/Teams**: Autonomous and Cyber-Physical Systems (ACPS), Electronics and IoT (E&IOT), Intelligent Mechatronics (iTron). His work bridges academia and industry, impacting healthcare, smart agriculture, and environmental monitoring. Future research includes multi-modal sensing, multi-agent systems, and 6G communication integration with AI.

Lasse Vinesمشاهده پروفایل

استاد

• Semiconductor Physics
• Materials Physics
• Point Defects
• +۶ مورد دیگر

Lasse Vines is a Professor of Solid-state Physics and Quantum Technology at the University of Oslo (UiO), affiliated with the Department of Physics and the Center for Materials Science and Nanotechnology. His research focuses on semiconductor physics, materials physics in bulk, thin films, and nanostructures, with applications in solar cells, power electronics, and quantum technology. Key interests include point defects, doping, and diffusion in semiconductors like SiC and Ga2O3. Affiliations: Faculty of Mathematics and Natural Sciences, UiO; Center for Materials Science and Nanotechnology Education: MSc in Physics and Mathematics (NTNU, 2001), PhD in Semiconductor Physics (UiO, 2008) Research Dr. Vines investigates semiconductor defects and their impact on material properties. Recent work includes defect profiling in SiC using SIMS/DLTS, diffusion mechanisms in Ga2O3, and defect-engineered quantum emitters in ZnO. His articles analyze defect dynamics, radiation tolerance, and phase transformations in wide bandgap semiconductors. Publications Recent trends emphasize defect-engineered semiconductors, ion implantation effects, and luminescence mechanisms. Key areas include 4H-SiC device optimization, β-Ga2O3 doping, and quantum material platforms for future technologies. Grants & Projects Involved in projects like GO-POW (Gallium Oxide for Power Electronics), QuTe (Quantum Emitters in Semiconductors), and SiQUEST (Silicon Quantum Technologies). These focus on defect control, quantum materials, and semiconductor applications.

Pekka Parviainenمشاهده پروفایل

دانشیار

• Machine Learning
• Bayesian Networks
• Graphical Models
• +۷ مورد دیگر

Pekka Parviainen is an Associate Professor in the Department of Informatics at the University of Bergen, within the Faculty of Mathematics and Natural Sciences. His research spans machine learning, probabilistic modeling, and AI theory, with a focus on Bayesian and Markov networks, adversarial robustness, fairness, and energy forecasting. He is affiliated with the Center for Data Science (CEDAS), an active research center at the university. His research interests include: Structure learning in graphical models Probabilistic forecasting using graph neural networks Adversarial robustness and defense mechanisms Fairness in clustering and machine learning Optimization and approximation in learning algorithms Applications in renewable energy and quantum sensing His recent publications (2020–2025) reflect a strong theoretical grounding combined with real-world applications, particularly in energy systems and AI safety. The works trend toward scalable and interpretable models, with increasing focus on fairness and robustness. Key themes include Bayesian network learning, metric learning, and causal graph modeling. Scientific contributions include: Development of novel adversaries (e.g., Voronoi-epsilon) for measuring robustness Scalable algorithms for learning large DAGs and Bayesian networks Integration of continuous optimization with combinatorial heuristics Applications in electricity demand forecasting and gas sensing Parviainen advises PhD students, including Hyeongji Kim (2023 thesis on distance in machine learning), and collaborates extensively with researchers in Norway and internationally. He has received computational support via Sigma2 (NN9884K) and is part of the CEDAS project, which fosters interdisciplinary data science research. While no specific grants are detailed, his involvement in funded projects and high-impact publications indicates active grant engagement. He is associated with the Center for Data Science (CEDAS), where he contributes to advancing data-driven methodologies across domains. The team emphasizes scalable, robust, and fair AI systems, aligning with national and international research priorities in trustworthy machine learning.

• Operations Management
• Digital Strategy (Industry 4.0)
• Circular Economy
• +۵ مورد دیگر

Usama Awan is an Associate Professor of Operations Management and Digital Strategy at the University of Inland Norway, affiliated with the Center for Research on Digitalization and Sustainability. He holds a PhD (2019) from Lappeenranta-Lahti University of Technology (Finland) and has extensive international research experience including roles at Duquesne University (USA) and multiple Nordic institutions. Education: PhD in Industrial Engineering & Management (LUT, Finland) MSc International Marketing (Malardalen University, Sweden) MBA (Quaid-i-Azam University, Pakistan) Postgraduate Diploma in Teaching Practices (Haaga-Helia, Finland) Research Focus: Combines digital transformation (AI, Industry 4.0, IoT) with sustainability goals like Circular Economy and Green Innovation. Specializes in supply chain dynamics, data analytics, and sustainable production practices. Recent work explores paradoxes in corporate sustainability within emerging technologies like quantum computing and additive manufacturing. Key Contributions: Over 43 ISI journal articles, 10 book chapters, and 2,280+ citations (H-index 30). Active in conferences with a focus on sustainable operations, digital strategy, and environmental management. Labs/Initiatives: Member of research groups 'Smarter Greener Innovation (SGI)' and 'Sustainable Ecosystem Transition (SET)' at Inland Norway University.

Sijing Shenمشاهده پروفایل

دانشیار

• Numerical Simulations of Galaxy Formation and Evolution
• Galaxy-Intergalactic Medium Interactions
• Galactic Winds
• +۹ مورد دیگر

Dr. Sijing Shen is an Associate Professor at the Institute of Theoretical Astrophysics, University of Oslo . She specializes in numerical simulations of galaxy formation and evolution, focusing on interactions between galaxies and the intergalactic medium, galactic winds, and the physics of the interstellar medium. PhD in Astrophysics (McMaster University, 2010) Postdoctoral Fellow (University of California, Santa Cruz, 2010-2014) Research Associate (University of Cambridge, 2014-2016) Associate Professor (University of Oslo, 2017-present) Her research employs high-performance computing and numerical magnetohydrodynamics to model the co-evolution of supermassive black holes and galaxies, the origins of galactic magnetic fields, and observational signatures of high-redshift galaxies. She develops and utilizes massively parallel magneto-hydrodynamic codes to create virtual universes for comparison with observational data. Recent publications highlight her work on dwarf galaxies, circumgalactic medium physics, and spectral diagnostics for next-generation telescopes like AtLAST. Key themes include feedback mechanisms, dark matter interactions, and computational methods in astrophysics. Young Research Talents Grant, Research Council of Norway Shen supervises 2 PhD students and 1 postdoctoral fellow. Her work bridges theoretical modeling with observational predictions, particularly in galaxy formation and evolution processes.

Tjerand Sildeمشاهده پروفایل

دانشیار

• Lattice-Based Cryptography
• Zero-Knowledge Protocols
• Post-Quantum Cryptography
• +۹ مورد دیگر

Tjerand Silde is an Associate Professor in Cryptology at the Department of Information Security and Communication Technology at the Norwegian University of Science and Technology (NTNU). He serves as Research Group Leader of the NTNU Applied Cryptology Lab and as Security and Cryptography Expert at Pone Biometrics . His academic journey includes a PhD thesis titled Privacy-Preserving Cryptography from Zero-Knowledge Proofs (2022). Primary affiliation: NTNU (Department of Information Security and Communication Technology) Secondary affiliation: NTNU Department of Mathematical Sciences Industry role: Security and Cryptography Expert at Pone Biometrics His research focuses on lattice-based cryptography and zero-knowledge protocols , with broader interests in post-quantum cryptography , anonymous communication , multiparty computation , homomorphic encryption , electronic voting , and secure implementation . The research demonstrates trends in advancing lattice-based cryptographic solutions for real-world applications, particularly in authentication, e-voting, and secure communication. Notable scientific achievements include receiving a 765K NOK equipment grant from the IE Faculty - Research (2025) to establish the CRYPTO-LAB for cryptographic research and education. He has held leadership roles as General Chair for IACR PKC 2025 and coordinates the Cryptographic Engineering profile in NTNU's master's program. His teaching portfolio includes courses like TTM4205 Secure Cryptographic Implementations , IIK8105 Quantum-Secure Cryptography , and TM8107 Cryptographic Protocols and Applications . He actively supports Electronic Frontier Foundation , Tor Project , and Signal Foundation .

Kishore Thapliyalمشاهده پروفایل

پژوهشگر ارشد

• Quantum Optics
• Photonic Quantum Technology
• Quantum Information Processing
• +۵ مورد دیگر

Kishore Thapliyal is a Research Fellow at the University of Oslo, Norway, and previously held the same position at Palacky University in the Czech Republic. His research focuses on theoretical aspects of quantum optics, photonic quantum technology, and quantum information processing. Key areas include nonclassical state generation, entanglement quantification, decoherence suppression, and applications in quantum communication and cryptography. He collaborates with experimentalists to design models for multimode optical field experiments and explores non-Hermitian physics for quantum sensing enhancement. His academic journey includes postdoctoral roles since 2018, with a strong emphasis on advancing theoretical frameworks for quantum systems. Research interests span non-Hermitian dynamics, quantum resource optimization, and secure multiparty computation. He is affiliated with the Solid-state Physics and Quantum Technology research group at the University of Oslo. While no scientific awards are explicitly mentioned, his contributions to quantum photonics and theoretical models have been published extensively. Collaborations with experimental groups highlight his interdisciplinary approach to bridging theory and application in quantum technologies.

• Cryptography
• Blockchain Technology
• 5G Network Security
• +۷ مورد دیگر

Professor Danilo Gligoroski is affiliated with the Norwegian University of Science and Technology (NTNU) under the Department of Information Security and Communication Technology. His work bridges cryptography, blockchain technology, and 5G network security, with a focus on decentralized systems and privacy-preserving protocols. Key research trends in his recent publications include blockchain applications in healthcare and reseller markets, verifiable delay functions for consensus mechanisms, and cryptographic frameworks for chat-based systems. He explores data integrity, network coding, and scalable storage solutions for blockchain ecosystems. Major collaborations include co-authors like Mayank Raikwar, Katina Kralevska, and Anton Karl Oskar Hasselgren. His work often intersects with GDPR compliance, network slice isolation, and secure service implementation in modern telecommunications.

Owen Rohwer Huffمشاهده پروفایل

پژوهشگر ارشد

• Geophysics
• Machine Learning
• Seismic Data Processing
• +۳ مورد دیگر

Owen Rohwer Huff is a Research Fellow in the Department of Geosciences at the University of Oslo, pursuing an industrial PhD in collaboration with Aker BP. His work focuses on applying machine learning techniques to geophysical challenges, particularly in seismic data processing and reservoir monitoring. PhD Fellow (Industry), University of Oslo (2022–) Industrial Partner: Aker BP Supervisors: Professor Jan Inge Faleide (UiO), Jan Erik Lie (Aker BP), Aina Juell Bugge (Aker BP) MSc in Geophysics, Colorado School of Mines (US) Research Interests center on: Reconstruction of missing near offset data in marine seismic surveys Angle stack alignment and conditioning for AVO (Amplitude Versus Offset) analysis Deep learning architectures (e.g., convolutional neural networks) for geophysical data processing Distributed Acoustic Sensing (DAS) for microseismic monitoring Key Publications demonstrate trends in: Machine learning for near offset gap restoration in marine seismic data Angle stack conditioning techniques via convolutional neural networks Guided wave eigenfunction theory for unconventional reservoir characterization Microseismic event classification using DAS and CNNs Academic Code contributions include open-source CNNs for seismic data processing available on GitHub: Repository: near_offset_reconstruction_CNN Features: UNet architecture, synthetic generation code, trained models Requirements: Data dimensions of 448×40 (CMPs) or 448×72 (shots) for processing

Dirk Hesseمشاهده پروفایل

دانشیار

• Theoretical Physics
• Computational Physics
• Lattice Quantum Chromodynamics
• +۳ مورد دیگر

Dirk Hesse serves as an Associate Professor at the University of Oslo with dual expertise bridging academia and industry. His academic role centers on the Data and Knowledge Management (DKM) research group, while professionally he holds the position of Vice President at Equinor, overseeing IT portfolios for commodity market sales and energy product distribution. His research spans theoretical and computational physics with strong interdisciplinary connections to data science. Key focus areas include: Lattice Quantum Chromodynamics (QCD) and Heavy Quark Effective Theory (HQET) Numerical Stochastic Perturbation Theory (NSPT) applications Gradient Flow methodologies in quantum field theory Automated lattice perturbation techniques Digital transformation in energy sectors Analysis of his publications reveals deep specialization in high-precision computational methods for particle physics, particularly in heavy-light quark systems and non-perturbative QCD calculations. His work consistently integrates advanced algorithm development with theoretical frameworks, demonstrating strong technical rigor in lattice field theory simulations. Professionally, Hesse maintains significant industry engagement through Equinor, where he drives digitalization initiatives in energy markets. This dual role positions him at the critical intersection of academic research and industrial application, particularly in data-intensive energy systems. His affiliations include active participation in the Data and Knowledge Management research group at UiO, focusing on computational methodologies applicable to both theoretical physics and industrial data challenges. Current work emphasizes the transfer of academic computational techniques to solve real-world problems in energy distribution and market systems.