جست‌وجوی استاد و پژوهشگر

• Privacy
• Usability
• Artificial Intelligence
• +۷ مورد دیگر

Johanna Johansen is an Associate Professor in the Department of Computer Science at the Norwegian University of Science and Technology (NTNU) . She holds a PhD in Computer Science from the University of Oslo (2021), with post-educational training from the IFIP Summer School and other interdisciplinary programs. Research focuses on privacy usability , AI bias , and sustainability in technology Developed novel concepts like Usable Privacy Cube and Privacy Labels Teaching responsibilities include web technologies and front-end development courses Research Highlights: Interdisciplinary work spans GDPR compliance, AI ethics, and IoT security with publications in venues like AI & Society and Information & Computer Security. Her groundbreaking research on human-to-computer bias transference has been cited in top venues including Communications of ACM. Scientific Awards: Recipient of the Kristine Bonnevie fellowship (2020) Collaborations: Partnerships with Chalmers University, Karlstad University, EuroPriSe, and industry leaders like Forsta AS and Tellu IoT AS. Active in European projects SCOTT and IoTSec with budgets exceeding 30 million Euros/NOK. Outreach: Developed accessible explanations of privacy concepts through video presentations and storytelling publications, including work with EuroPriSe on GDPR certification processes.

Vinicius Rezende Carvalhoمشاهده پروفایل

پژوهشگر ارشد

• Predictive Coding
• Cognitive Maps
• Brain Rhythms
• +۷ مورد دیگر

Vinicius Rezende Carvalho is a Research Fellow at the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion (University of Oslo). His work focuses on auditory predictive coding, cognitive maps, and seizure dynamics using intracranial EEG and computational models. Education : Bachelor's, MSc, and PhD in Electrical Engineering from Universidade Federal de Minas Gerais (UFMG), Brazil. Academic Interests : Predictive coding, brain rhythms, computational neuroscience, epilepsy, nonlinear dynamics, and speech decoding. His current research investigates the neurophysiological basis of auditory predictive coding through the AudioPred Project, analyzing single-unit recordings and local field potentials in response to auditory stimuli. Earlier work explored seizure prediction using stimulation probes and computational models. Publications highlight applications of delay differential analysis in speech decoding and seizure dynamics, alongside studies on neural entrainment and cognitive impairments in animal models. Scientific Award : 2020 Society for Neuroscience Latin American Training Program Fellow. Collaborations span institutions including the Salk Institute, University of California (Los Angeles), and Massachusetts General Hospital. He contributes to open-access datasets and participates in teaching programs like Neuromatch Academy.

Kai Olav Ellefsenمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Deep Learning
• Evolutionary Algorithms
• +۶ مورد دیگر

Kai Olav Ellefsen is an Associate Professor at the Department of Informatics (IFI), University of Oslo, specializing in biologically inspired artificial intelligence and robotics. He leads research within the Robotics and Intelligent Systems (ROBIN) research group, focusing on how biological principles can inform the development of more intelligent robotic systems. Ellefsen's research interests span multiple areas of artificial intelligence, with particular emphasis on evolutionary algorithms, reinforcement learning, and neuroevolution. His work explores how robots can learn from experience without disrupting previously acquired knowledge, adapt to new situations, interpret sensor data, and build environmental models. He investigates these challenges at various levels of abstraction, from simple simulations to real-world robotic implementations. His research often bridges computer science with biological inspiration to create more robust and adaptive intelligent systems. His recent publications demonstrate a strong focus on quality-diversity optimization, evolutionary robotics, and the application of AI to environmental monitoring and network quality assessment. The research shows a consistent pattern of exploring how diversity in evolutionary algorithms can lead to more robust solutions, how robots can adapt to changing environments, and how predictive models can be transferred across different tasks. There's a clear progression from theoretical exploration of evolutionary algorithms to practical applications in robotics and networking. Norwegian Artificial Intelligence Society Best Master Thesis Award (2010) Ellefsen actively supervises numerous PhD and Master's students, with a current focus on evolutionary robotics, neuroevolution, and AI safety. His supervision portfolio includes both theoretical work on evolutionary algorithms and practical applications in robotics. He has been involved in several research projects including the Predictive and Intuitive Robotic Assistant (PIRC) project. His teaching includes courses such as IN3050/4050 - Introduction to Artificial Intelligence and Machine Learning, and IN5490/9490 - Advanced Topics in Artificial Intelligence for Intelligent Systems. As part of the Robotics and Intelligent Systems (ROBIN) research group, Ellefsen collaborates with researchers working on various aspects of intelligent systems, from theoretical foundations to practical implementations. His work often involves interdisciplinary collaboration, particularly with researchers in mechanical engineering, biology, and environmental science to develop robotic systems with real-world applications.

Arild Torolv Søetorp Waalerمشاهده پروفایل

استاد

• Semantic Web
• Ontology-Based Data Access
• Knowledge Graphs
• +۳ مورد دیگر

Arild Torolv Søetorp Waaler is a Professor in the Reliable Systems group at the Department of Informatics , University of Oslo. His research focuses on semantic technologies, ontology-based data access (OBDA), and systems engineering for industrial applications, particularly in the oil & gas and manufacturing sectors. Research Interests: Semantic Web, Knowledge Graphs, Ontology Engineering, Machine Learning for Condition Monitoring, Data Variety Challenges, and Industrial Systems. Waaler has co-authored 15 recent publications (2017–2024) spanning semantic frameworks for digital twins, identifier management, and analytics-aware ontologies. These works emphasize data integration , query optimization , and sustainable data systems in collaboration with industry partners like Statoil and Siemens. Contact: arild@ifi.uio.no | Room 9465, Gaustadalléen 23B, Oslo.

Martin Gieseمشاهده پروفایل

استاد

• Applied Logics
• Formal Methods
• Semantic Web/Semantic Technologies
• +۹ مورد دیگر

Martin Giese is a Professor at the Department of Informatics (IFI), University of Oslo , specializing in applied logics , formal methods , and semantic technologies . He is affiliated with the Data and Knowledge Systems (DKM) research group and has held postdoctoral positions at RICAM, RISC, and Chalmers University of Technology. PhD and Diploma in Computer Science from Universität Karlsruhe (2002/1998) Secretary of the Association of Automated Reasoning and CADE Inc. (2010–2018) Steering Committee member of Tableaux (2009–2017), Vice President (2013–2017) His research focuses on semantic web technologies , ontology-based data access (OBDA) , and industrial applications of formal methods. Key contributions include the development of OptiqueVQS (visual query system for big data) and GeoFault (ontology for geological modeling). Recent publications address SPARQL optimization with SHACL , digital twins , and geological process simulation . Scientific collaborations span institutions like Statoil , Siemens , and ACM/IEEE conferences. His work bridges theoretical logic with practical industrial data challenges , emphasizing user-centric semantic systems and scalable solutions for cross-linked datasets.

Jan Tore Lønningمشاهده پروفایل

استاد بازنشسته

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Jan Tore Lønning is a Professor Emeritus at the Department of Informatics, University of Oslo (UiO). His academic journey includes roles as Professor of Informatics (2006–present) and Linguistics (1994–2005), with prior positions as Associate Professor in Linguistics and Philosophy (1989–1994). He holds a Dr. philos in Mathematics from UiO (1989) and has a multidisciplinary background in Mathematics, Philosophy, Informatics, and Psychology. His research focuses on Natural Language Processing (NLP) , Artificial Intelligence , and Machine Learning , with contributions to computational linguistics, machine translation, and semantic analysis. He has taught courses like Introduction to Artificial Intelligence and Machine Learning and Natural Language Processing , as well as master’s programs in Language Technology. Lønning’s publications span hybrid machine translation frameworks, semantic parsing (e.g., Minimal Recursion Semantics), and NLP applications like named entity recognition and domain-specific word embeddings. His work bridges linguistic theory with computational methods, emphasizing quality-oriented MT systems and interdisciplinary language science. No scientific awards are explicitly listed, but his extensive publication record reflects significant contributions to NLP and computational linguistics. His advising and grant activities are not detailed in the provided texts, though collaborations with researchers like Farhad Nooralahzadeh and Lilja Øvrelid are evident from co-authored works.

Alireza David Anisiمشاهده پروفایل

دانشیار

• Autonomous Systems
• Robotics
• Agri-tech
• +۷ مورد دیگر

Alireza David Anisi is an Associate Professor at the Department of Engineering Sciences , University of Agder. With a PhD in Optimization and Systems Theory and an M.Sc. in Engineering Physics, he specializes in autonomous systems and robotics , particularly in agri-tech and harsh environments . His industrial experience spans 15+ years in defense and oil & gas sectors. Academic Background: PhD: Optimization and Systems Theory M.Sc.: Engineering Physics Research Interests include formal verification and learning for autonomous systems, combinatorial optimization for multi-robot task/path planning, computational optimal control for trajectory optimization, and nonlinear observer design. His work bridges academic research with industrial R&D. Selected Publications highlight trends in robotics safety assurance, formal verification methods, and industry-specific applications (defense, oil & gas, agriculture). Recent works focus on runtime verification in ROS 2 and RoboStar technology integration. Patents: Power system optimization (EP18162642.5, 2018) Mechanical grip system (SE1300179, 2013) Sensor arrangement for machine vision (WO2014026711, 2012) Tool changer for explosive environments (WO2012007188, 2011) Industrial robot control method (EP2466404, 2010) Harsh environment mobile robot (WO2011107137, 2010) Anisi contributes to the Robotics and Automation research group, focusing on real-world applications in agriculture, energy, and industrial sectors. He teaches MAS221 Industrial IT and Robotics and emphasizes collaboration between academia and industry.

Ana Ozakiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Knowledge Representation
• Machine Learning Theory
• +۳ مورد دیگر

Ana Ozaki is an Associate Professor at the University of Oslo (full-time) and holds a part-time position at the University of Bergen. Her research focuses on Artificial Intelligence, particularly knowledge representation, machine learning theory, and algorithms for learning logical theories in description logic. Her primary research interests include the formalization of learning phenomena to investigate questions of learnability, complexity, and reducibility. Ozaki specializes in algorithms for learning logical theories using description logic and related formalisms. Her work bridges theoretical foundations with practical applications in knowledge representation. Ozaki's publications predominantly explore themes in computational logic, knowledge representation, and machine learning theory. Recent works focus on ethical AI applications, hybrid logic systems, and knowledge extraction techniques from language models, demonstrating consistent innovation at the intersection of formal methods and practical AI systems. She has led significant research projects including 'Learning Description Logic Ontologies' (funded by RCN) and 'Apprendimento PAC di Ontologie in Logica Descrittiva' (PACO), and has collaborated internationally on graph-based computation models. Ozaki serves the AI community through editorial roles at the Journal of Machine Learning Research and Journal of Web Semantics, and has chaired program committees for major conferences including the International Joint Conference on Rules and Reasoning and the International Description Logic Workshop.

Lars Tveitoمشاهده پروفایل

مدرس

• Formal Methods
• Software Engineering
• Distributed Systems
• +۳ مورد دیگر

Lars Tveito is a Lecturer in Programming Technology at the Department of Informatics, University of Oslo. His research focuses on formal methods, distributed systems, and concurrency theory, with contributions to tools like OnlineProver for teaching formal proofs. He is affiliated with the Programming Technology research group and has published widely in venues such as NIKT, OpenAccess Series in Informatics, and Lecture Notes in Computer Science. Key research interests include partial order reduction techniques, formal verification of distributed systems, and virtualized system analysis. Recent work explores reproducibility in distributed active objects and symbolic execution combined with concurrency analysis. His teaching emphasizes practical applications of formal logic and software engineering principles. Collaborations with researchers like Einar Broch Johnsen and Rudolf Schlatte highlight his involvement in advancing methodologies for system design and verification. The lack of explicit awards indicates a focus on impactful technical contributions over accolades.

• Cybersecurity
• Internet of Things (IoT)
• Sensor Networks
• +۳ مورد دیگر

Mohammad Mushfiqur Rahman Chowdhury is an Associate Professor at the University of Oslo's Institute of Transport Economics (ITS), holding a 10% part-time position. He is affiliated with the Section for Autonomous Systems and Sensor Technologies. His research focuses on cybersecurity, Internet of Things (IoT), sensor networks, privacy in social networks, critical infrastructure security, and data security. His work explores interdisciplinary challenges in securing interconnected systems, including IoT interoperability, critical infrastructure protection using semantic technologies, and privacy frameworks for social communities. Recent publications address topics like 5G service continuity in heterogeneous networks and user-centric privacy-aware systems. Chowdhury's articles emphasize practical solutions for real-world security challenges, combining theoretical advancements with applied research in wireless services and ambient computing environments. No awards or grants are explicitly mentioned, though his contributions reflect sustained engagement in sensor-based security and privacy-preserving technologies.

Delphine Martresمشاهده پروفایل

پژوهشگر ارشد

• Quantum Computing
• Quantum Error Correction
• Computational Physics
• +۳ مورد دیگر

Delphine Martres is a Doctoral Research Fellow at the University of Oslo, affiliated with the Faculty of Mathematics and Natural Sciences and the Department of Physics. Her research focuses on quantum error correction and variational quantum algorithms within the broader field of quantum computing. She holds an MSc in Physics from École Polytechnique Fédérale de Lausanne (EPFL), Switzerland (2022). Her academic work is embedded in the QOMBINE project (Mathematics for quantum computation and many-body theory) and the Operator Algebras research group. This interdisciplinary effort bridges theoretical physics and computational methods to advance quantum technologies. No specific awards or publications are listed in the provided materials. Delphine’s current position emphasizes foundational research in quantum systems, leveraging her background in computational physics to address challenges in error correction—critical for scalable quantum computing. Her affiliation with UiO positions her within Norway’s vibrant physics research ecosystem.

• Artificial Intelligence
• Computational Physics
• Machine Learning
• +۴ مورد دیگر

Anders Malthe-Sørenssen is a Professor at the University of Oslo’s Center for Computing in Science Education within the Faculty of Mathematics and Natural Sciences. His research integrates artificial intelligence (AI) with neuroscience to develop advanced machine learning techniques and study brain-inspired AI systems. He also focuses on computational methods in materials science, particularly in frictional dynamics and nanoscale processes, alongside computational education research to enhance science teaching. Malthe-Sørenssen teaches courses including Fys1120 (Electromagnetics), HON1000 (Interdisciplinary Honors), and Fys4460 (Disordered Systems and Percolation), emphasizing active learning and technology integration. His past industrial experience includes tech startups and large-scale industry-funded projects, leading to patented technologies. His research interests span computational physics, machine learning, neuroscience, and education innovation. Notable projects include studies on neural circuitry, AI robustness, and material fracture mechanics. He has received awards such as the HMK Gold Medal (1999), Nansen Prize (2001), and multiple teaching excellence prizes (2011–2015). Malthe-Sørenssen leads initiatives like the CO2Basalt project for carbon storage and contributes to international collaborations. His work bridges AI, materials science, and education, fostering interdisciplinary innovation.

Reidar Johannes Nordbyمشاهده پروفایل

استادیار

• Embedded systems
• Radio communication technology
• Embedded computing
• +۳ مورد دیگر

Reidar Johannes Nordby is an Assistant Professor at the Department of Engineering, Østfold University College. His academic background includes a Norwegian 'Electronikkingeniør' (1985), a Bachelor's in Electronic Engineering (2002), and a Master's in Mobile Radio Communication (2008). His research focuses on embedded systems and radio communication technology integrated with embedded computing, particularly in healthcare and gadget applications. He teaches courses in digital technology and digital radio communication (in Norwegian). His publications include work on technology in healthcare systems. He has been affiliated with the university since at least 2018 and remains active in academic roles.

Håkon Kvale Stenslandمشاهده پروفایل

دانشیار

• Machine Learning
• Multimedia Systems
• Real-Time Processing
• +۵ مورد دیگر

Håkon Kvale Stensland is an Associate Professor in the Department of Networks and Distributed Systems at the University of Oslo (UiO), affiliated with Simula Research Laboratory. His work focuses on multimedia systems, machine learning applications in medical imaging and sports analytics, distributed computing, and GPU optimization. He has contributed to projects like HyperKvasir (a gastrointestinal dataset) and SmartIO (PCIe networking for device sharing). Key research areas include real-time video processing, 3D convolutional neural networks for event detection in soccer, and energy-efficient multimedia workloads. He has co-authored over 50 publications in venues like ACM Multimedia, IEEE Transactions, and Nature Communications. His tools and datasets, such as Saga and Bagadus, emphasize collaborative machine learning and real-time sports analytics. Recent work (2024-2025) explores prompt generation for medical segmentation and multi-host device sharing in high-performance clusters. His research bridges theoretical computer science with practical applications in healthcare, sports, and distributed systems.

Kosio Beshkovمشاهده پروفایل

پژوهشگر ارشد

• Topological Data Analysis
• Deep Neural Networks Theory
• Neural Manifold Analysis
• +۳ مورد دیگر

Kosio Beshkov is a Postdoctoral Fellow in Condensed Matter Physics at the University of Oslo, specializing in the intersection of topological data analysis and machine learning. His research focuses on theoretical frameworks for understanding neural network representations and biological neural systems. His primary research interests include: Theory of deep neural networks in overparametrized regimes Topological data analysis of neural manifolds De novo protein design using evolutionary algorithms and geometric modeling Connections between network representations and topological spaces Recent publications demonstrate strong trends in computational neuroscience, with 7 papers from 2021-2025 spanning journals like PLoS Computational Biology and iScience. His work consistently applies polyhedral geometry, quotient spaces, and homology to neural representation problems, while expanding into protein language models and gene therapy applications. Current technical approaches combine: Topological data analysis for high-dimensional neural data Geometric deep learning for robust representations Biophysically-detailed neuron modeling Protein structure-geometry relationships