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Franz Georg FuchsView profile

Associate Professor

• Quantum Computing
• Quantum Error Correction
• Several Complex Variables
• +3 more

Franz Georg Fuchs is an Associate Professor at the Department of Mathematics, University of Oslo. He holds a PhD in Applied Mathematics (2009) from the University of Oslo and a Master's degree in Mathematics from the Technical University of Munich (2006). His research focuses on developing quantum computing algorithms and quantum error correction, with contributions to numerical methods in fluid dynamics and magnetohydrodynamics. Education: PhD in Applied Mathematics, University of Oslo, 2009 Master's in Mathematics, Technical University of Munich, 2006 Research Interests: Quantum computing algorithms, quantum error correction, numerical methods for PDEs, computational fluid dynamics, and operator algebras. His work bridges theoretical foundations with practical applications, including software development for quantum reservoir computing and optimization frameworks like QAOA. Publications: Over 20 peer-reviewed articles in journals like Quantum , Journal of Computational Physics , and SIAM Journal on Scientific Computing . Recent work emphasizes quantum software tools and constrained optimization techniques. Awards: None explicitly listed. Active in research projects like the Gemini Center on Quantum Computing and the QOMBINE initiative. Labs/Teams: Involved in the Mathematics for Quantum Computing and Many-Body Theory (QOMBINE) project, collaborating with SINTEF and international researchers.

Jan Arne TelleView profile

Professor

• Algorithms
• Computational Complexity
• Graph Theory
• +3 more

Jan Arne Telle is a Professor in the Department of Informatics at the University of Bergen. His research focuses on algorithms, computational complexity, and graph theory, with recent contributions to explainable AI (XAI) and machine teaching. He leads the Norwegian Research Council-funded project 'Machine Teaching for Explainable AI' and has taught courses such as INF339 on Algorithmics of Causality. His work spans graph algorithms, parameterized complexity, and combinatorial optimization, with notable contributions to graph decomposition techniques like mim-width, cliquewidth, and boolean-width. Collaborations include researchers from institutions like the University of Rostock and the University of Montpellier. Key research trends include analyzing time series classification interpretability, optimizing robust simplifications for machine learning models, and solving combinatorial problems in machine teaching. Telle's algorithms for problems like Feedback Vertex Set and Perfect Matching Cut have advanced the field of parameterized complexity. Despite no listed awards, his extensive publication record reflects significant academic impact. He is involved in the Algorithms Research Group at UiB and contributes to computational theory education and outreach, including participation in programming competitions and academic leadership roles.

• Real Algebraic Geometry
• Optimization
• Computational Complexity
• +5 more

Cordian Riener is a Professor of Mathematics at UiT The Arctic University of Norway, serving as Pro-Dean for PhD education within the Faculty of Science and Technology. He leads the Algebra group and co-directs the Lie-Størmer Center for Fundamental Structures in Computational and Pure Mathematics. His academic roles include chairing the Norwegian Mathematics Council and membership on the Abel Prize board. Riener holds a Habilitation in Mathematics from Universität Konstanz (2018) and a PhD from Goethe University Frankfurt (2011), with prior roles including Acting Professor at Universität Konstanz and Aalto Science Institute Fellow in Helsinki. His research focuses on real algebraic geometry, optimization, computational complexity, and symmetry exploitation in algorithms. Notable contributions include work on semidefinite programming, polynomial optimization under symmetry, and cohomology of semi-algebraic sets. Recent publications address connectivity in symmetric sets, orbit space descriptions, and applications of symmetry in trigonometric polynomials. Riener actively organizes conferences like ISSAC and MEGA, and contributes to educational initiatives such as the Norwegian Mathematics Council.

Michael KampffmeyerView profile

Professor

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +5 more

Michael Kampffmeyer is a 33-year-old Professor at the Machine Learning Group at UiT The Arctic University of Norway in Tromsø, where he researches the development of deep learning algorithms that learn with limited data and their explainability. Born in 1991 in Hamburg, Germany, he developed an early relationship with Norway through family holiday trips, spending six months as an exchange student in Alvdal at age 15 before completing high school in the UK. Remarkably, just 14 years after beginning his integrated master's degree in Energy, Climate and Environment at UiT at age 18, he achieved the position of professor. Kampffmeyer's research focuses on addressing two critical challenges in contemporary AI: the interpretability aspect and the inefficient training processes that require massive amounts of data, computing power, and energy. His work aims to develop more efficient and explainable AI models that can function effectively with limited data, with particular applications in medical image analysis where he collaborates with Universitetssykehuset Nord-Norge (UNN). His team has grown significantly from 5 to around 35 members since he began his PhD in 2015. The analysis of Kampffmeyer's recent publications reveals a strong focus on medical imaging applications, particularly in cardiac analysis and mammography, alongside work on making AI models more efficient and explainable. His research spans few-shot learning techniques for medical image segmentation, uncertainty modeling in graph networks, and super-resolution techniques for satellite imagery. A consistent theme across his work is the development of methods that require less data while maintaining or improving performance, addressing his core research mission of creating more efficient AI systems. Kampffmeyer is actively involved in securing significant research funding, with his team working on a proposal to join a new national AI center that would distribute approximately 200 million NOK, with a substantial portion allocated to UiT. He emphasizes the importance of international visibility, recognizing that continued representation at major international conferences is essential for maintaining their position at the forefront of AI research. His vision for the future of AI includes multimodal approaches that incorporate various types of data beyond just text and images, inspired by how humans use multiple senses to understand the world.

Roland OlssonView profile

Associate Professor

• Machine Learning
• Automatic Programming
• Artificial Neural Networks
• +3 more

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

• Marine Engineering
• Hybrid Power Systems
• Dynamic Simulation
• +2 more

Professor Eilif Pedersen heads the Master Programs in Marine Technology at NTNU. His research encompasses mathematical modeling of marine machinery systems, hybrid power plants, and dynamic simulation methodologies. He teaches courses on system dynamics, marine machinery, and mechatronics. Current research projects include SFI Smart Maritime and Virtual Prototyping of Marine Systems, involving multiple PhD candidates and postdoctoral researchers.

• Signal Processing
• Machine Learning
• Remote Sensing
• +3 more

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

Anne-Marie GeorgeView profile

Associate Professor

• Preference Learning
• Preference Modeling
• Computational Social Choice
• +4 more

Anne-Marie George is an Associate Professor at the University of Oslo, affiliated with the Scientific Computing and Machine Learning department. Her research focuses on handling user preferences through modeling, elicitation, inference, and fairness in group decision-making systems. Academic Rank: Associate Professor Department: Scientific Computing and Machine Learning Email: annemage@ifi.uio.no Her work spans computational social choice, preference learning, and algorithmic fairness, with recent publications addressing dynamic resource allocation, robust recourse in binary problems, and fair voting procedures. Collaborations with researchers like Christos Dimitrakakis highlight interdisciplinary efforts in AI ethics and decision theory. Recent trends in her publications include: Dynamic allocation fairness Preference-based decision systems Explainable AI for resource distribution Ontology engineering applications Robustness in allocation algorithms Multiwinner voting optimization

• Theoretical Computer Science
• Parameterized Complexity
• Graph Algorithms
• +3 more

Brage Isak Bakkane Keiserås is a Doctoral Research Fellow in the Department of Informatics at the University of Oslo, Faculty of Mathematics and Natural Sciences. His research is centered on theoretical aspects of computer science, particularly algorithmic graph theory and parameterized complexity. His research interests include: Theoretical Computer Science Parameterized Complexity Graph Algorithms Discrete Mathematics Algorithm Design Computational Complexity The available publication indicates a strong focus on hardness results for domination problems under structural graph parameters, particularly using Mim-Width as a complexity measure. This aligns with current trends in fine-grained complexity and structural parameterizations in algorithm design. He is actively involved in the research project Leveraging Energy-Aware Programming (LEAP) , which suggests an interest in broader programming technology applications, possibly connecting theoretical foundations with energy-efficient computing paradigms. There are no scientific awards listed in the provided text. There is no information available about student advising or research grants beyond project affiliation. No lab or team name is explicitly mentioned, though his work is likely conducted within the Programming Technology research group at UiO.

• Bioinformatics
• Genomics
• Evolutionary Biology
• +9 more

Lex Nederbragt is an Associate Professor at the University of Oslo , affiliated with the Faculty of Mathematics and Natural Sciences and the Centre for Ecological and Evolutionary Synthesis . His work bridges genomics , bioinformatics , and STEM education , focusing on integrating computational methods into bioscience teaching. Email: lex.nederbragt@ibv.uio.no Phone: +47 22844132 Room: 3404 Lex specializes in genomics , particularly teleost fish genetics , genome assembly , and bioinformatics tool development . His research explores evolutionary biology , including immune gene evolution , hybrid speciation , and horizontal gene transfer . He also advances computational education through active learning , reverse instructional design , and Software Carpentry . His recent publications address graph-based genome analysis , genomic repeat landscapes , and pedagogical strategies . He received the Olav Thon Foundation national award for excellence in teaching (2025) and was recognized as a Merittert Utdanner (2023). Co-developer of tools like NucBreak , NucDiff , and khmer . Key contributor to Atlantic cod and grayling genome projects.

Baltasar Enrique Beferull LozanoView profile

Professor

• Data Science
• Machine Learning
• Signal Processing
• +3 more

Baltasar Enrique Beferull Lozano is a tenured Professor at the University of Agder , leading the Center Intelligent Signal Processing and Wireless Networks (WISENET) since 2015. With a PhD in Electrical Engineering from USC (2002) and prior roles at EPFL, AT&T Shannon Labs, and University of Valencia, his career spans 20+ years of academic and industrial research in signal processing, wireless systems, and AI. Education: PhD (USC), MSc (USC), MSc (University of Valencia) Expertise: Data Science, Machine Learning, Graph Signal Processing, Cyber-Physical Systems His research focuses on AI-driven wireless networks and in-network collective intelligence , addressing fundamental and applied challenges in smart water systems , energy management , and next-gen 5G/6G . He has secured 20+ international projects including 10 EU-funded initiatives (HYDROBIONETS, SENDORA) and 5 RCN-funded projects. Recent publications emphasize dynamic graph learning from time series data, quantized graph filters , and multi-agent reinforcement learning for networked environments. Awards include IEEE Best Paper Awards (2012, 2021), TOPPFORSK Grant (2015), and Ramón y Cajal Program Rank #1 (2005). As a Senior IEEE Member , he serves as Area Editor for IEEE Transactions on Signal Processing and evaluates research proposals for the European Commission , NSF , and Qatar National Research Fund . His lab has produced 15 PhD graduates and collaborates with 12+ industry partners including Telenor, IBM, and SINTEF.

Stefan GrunerView profile

Professor

• Formal Methods
• Software Engineering
• Model Checking
• +9 more

Stefan Gruner is a Professor in the Department of Computer Science, Faculty of Engineering, Built Environment and Information Technology, at the University of Pretoria, South Africa. He holds advanced degrees in Informatics (PhD, MSc) and Philosophy (MA), reflecting his interdisciplinary research that bridges formal software engineering, digital forensics, and the philosophy of technology. His work is widely published in international journals and conferences, and he is deeply involved in academic service, including supervision, examination, and program committees. Education: MA in Philosophy, Germany, 2010 PhD in Informatics, RWTH Aachen, Germany, 1999 MSc (Dipl.-Inform.) in Informatics, Germany, 1996 His research interests center on Formal Methods , Software Engineering , and the Philosophy of Computer Science . He has made significant contributions to parameterized and three-valued model checking, formal methods for railway systems, and the scientific foundations of digital forensics. His recent work explores historical semantics in software architecture and the philosophical underpinnings of informatics. His publications from the last few years reveal a strong trend towards formal verification, safety-critical systems, and the epistemological aspects of digital investigation and software engineering. Scientific Recognition: Biographic entry in Who's Who in the World (2010 edition) Member of the selection committee for the Annual Best Dissertation Award (EAPLS) Stefan Gruner is an active advisor and supervisor, having guided numerous MSc and PhD students to completion. He has secured research funding from the South African National Research Foundation (NRF) and international partners for projects on model-driven development, CSP protocol verification, and visualization of distributed algorithms. His academic service is extensive, including roles as program chair for workshops like FM+AM and SEFM, editor for conference proceedings, and external examiner for multiple universities. He has also been a visiting researcher at institutions in Bordeaux and Swansea. He was a central organizer of the Distributed Systems Research Group at the University of Pretoria and has fostered international collaborations, particularly within the formal methods and software engineering communities.

Étienne AndréView profile

Professor

• Formal Methods
• Model Checking
• Verification of Concurrent Systems
• +12 more

Étienne André is a Full Professor at Université Sorbonne Paris Nord , affiliated with the Institut Galilée and the Laboratoire d’Informatique de Paris Nord (LIPN) . He leads the SAFER research team and has previously held professorial positions at Université de Lorraine . His work bridges formal verification and real-world applications in cyber-physical and distributed systems. PhD from ENS Cachan (2010) Postdoc at National University of Singapore (2010–2011) Associate Professor at Université Sorbonne Paris Nord (2011–2019) Full Professor at Université de Lorraine (2019–2022) Full Professor at Université Sorbonne Paris Nord (2022–present) His research centers on formal verification of concurrent and real-time systems, with a focus on parametric timed model-checking . He investigates how systems behave under timing uncertainty and develops methods to synthesize safe timing parameters. His work extends to monitoring cyber-physical systems and detecting side-channel attacks using quantitative formal methods. He is a strong advocate for sustainable research , minimizing carbon footprint through reduced air travel. The recent publications reflect a consistent trajectory in parametric verification , real-time systems , and model checking , with increasing emphasis on distributed and probabilistic approaches. His tools like IMITATOR and InSPEQTor enable practical application in industrial and academic settings. PhD Award, Académie Lorraine des Sciences (2024) Étienne André has supervised several PhD students and postdoctoral researchers. He leads major research projects such as ProMiS , MoCcA , and PACS , funded by ANR, PHC, and other international agencies. His collaborative work spans France, Singapore, Poland, and Japan. He is deeply involved in the formal methods community, serving on steering committees for SynCoP and Petri Nets , and organizing conferences such as ETAPS and ICFEM . He has participated in over 30 program committees, including FORTE , TACAS , and HSCC .

Svein Olav Glesaaen NybergView profile

Associate Professor

• Bayesian Statistics
• Probability Theory
• Stochastic Processes
• +7 more

Svein Olav Glesaaen Nyberg is an Associate Professor at the Department of Engineering Sciences , University of Agder , Norway. He has been affiliated with the university since 2003, contributing to research and education in Bayesian statistics, probability theory, and stochastic processes. His work spans interdisciplinary domains including Artificial Intelligence , Biomechatronics , and Collaborative Robotics , alongside Physics and Applied Mathematics . Education: PhD (University of Oslo, 1996), MSc (University of Oslo, 1991). Prior Roles: Postdoc at University of Edinburgh (1996-98), Senior Knowledge Engineer at Computas AS (1998-2002), and teaching positions at Volda University College (2002-03). His research integrates Bayesian statistical inference with practical applications in energy systems, robotics, and materials science. Recent work includes modeling human trajectories in industrial settings using Ornstein-Uhlenbeck processes , EEG-EMG signal fusion for rehabilitation robotics, and Bayesian analysis of power grid stability . He has also published on ecological trends in Norwegian smooth snakes, linking climate change to reproductive behaviors. Scientific Awards and recognitions include authoring the textbook The Bayesian Way: Introductory Statistics for Economists and Engineers (2018) and its Norwegian counterpart Statistikk - en bayesiansk tilnærming (2016). He holds a certificate in university pedagogy (UniPed, 2009).

• Artificial Intelligence
• Medical Technology
• Knowledge-Based Systems
• +3 more

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