Find Academic Supervisors

• Medical Image Analysis
• Alzheimer's Disease Research
• Computational Engineering
• +5 more

Hans Olofsen serves as a Senior Lecturer and Study Program Manager for Bachelor Computer Engineering and Computer Engineering - Further Education at the Department of Computer Science and Computational Engineering, UiT The Arctic University of Norway, located at the Narvik campus. His professional activities span both academic instruction and research leadership within computational engineering disciplines. Dr. Olofsen's research interests center on Medical Image Analysis with particular focus on Alzheimer's disease biomarkers, Computational Engineering applications in healthcare, and Artificial Intelligence solutions for medical image processing. His technical expertise includes developing algorithms for ventricular shape analysis, creating autonomous virtual robots for 3D medical image navigation, and designing wearable technology systems for fall risk assessment in elderly populations. Analysis of his publication record reveals a consistent research trajectory in computational medical imaging spanning over 15 years, with significant contributions in ventricular shape biomarkers for Alzheimer's disease, 3D medical image exploration systems, and wearable technology applications in geriatric care. His work demonstrates interdisciplinary collaboration across neuroscience, computer science, and biomedical engineering domains. As a member of the Simuleringer (Simulations) research group at UiT, Dr. Olofsen contributes to advancing computational methods in medical applications with a specialization in Artificial Intelligence techniques. His office is located in Campus Narvik D2210, where he manages both his teaching responsibilities for computer engineering programs and his research activities.

• Virtual Reality
• Eye Tracking
• 3D Computer Graphics
• +4 more

Atle Birger Geitung serves as Assistant Professor in the Department of Computer Science, Electrical Engineering and Mathematical Sciences at Western Norway University of Applied Sciences (HVL) in Bergen, with office location at KRONSTAD D411. His academic responsibilities span both undergraduate and graduate education across multiple technical domains. Professor Geitung's research program centers on virtual reality applications with particular emphasis on eye tracking methodologies, therapeutic VR implementations, and educational technology development. His work bridges computer science with practical applications in clinical settings and educational environments, focusing on usability and effectiveness metrics for immersive technologies. His publication record from 2021-2023 reveals consistent research productivity in VR applications, with particular attention to exposure therapy protocols, simulation training methodologies, and cross-disciplinary educational implementations. The research demonstrates strong collaborative patterns with clinicians and educators across multiple institutions. Regularly supervises Bachelor's (DAT191) and Master's (DAT300) thesis projects Teaches core computer science courses including Database Systems (DAT107), Programming and Web Applications (DAT108), and Software Development (DAT109) Specialized instruction in 3D Computer Graphics (DAT155) and Programming for Engineering (ING202) Professor Geitung maintains active research collaborations with Ilona Heldal, Harald Soleim, and other colleagues on VR applications spanning clinical therapy and educational technology domains. His work demonstrates practical implementation focus with direct applications in healthcare training and therapeutic interventions.

• Multispectral colour imaging
• Print and Image quality
• Colorimetric device characterization
• +3 more

Jon Yngve Hardeberg is a Professor at the Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU). He is a member of the Norwegian Colour and Visual Computing Laboratory, where he supervises students, manages international programs, and leads research projects. His career spans roles at Gjøvik University College and NTNU, with visiting positions at institutions like the University of Washington and French universities. MSc in Signal Processing from NTNU PhD in Signal and Image Processing from École Nationale Supérieure des Télécommunications, Paris Research Interests: Multispectral colour imaging, cultural heritage documentation, medical imaging, and material appearance modeling. His work integrates optics, signal processing, and computational methods for applications like gilded surface analysis, 3D printing, and translucency perception. Recent publications focus on advanced imaging techniques (e.g., spectral reconstruction, reflectance transformation imaging), material analysis (pigment mapping, skin gloss), and 3D digitization. Keywords span Computer Science , Optics , and Medical Imaging , with subfields like Hyperspectral Imaging and Image Quality Metrics . Scientific Awards: Fulbright Scholar SPIE Senior Member Advising and Grants: Supervised 46 master students and 12 PhD graduates; currently guides 4 postdocs and 5 PhD candidates. Led major projects such as MUVApp (NOK 24.9M), IQ-MED (NOK 16.2M), and HyPerCept (NOK 33.6M), funded by the Research Council of Norway and EU. Labs/Teams: Director of The Norwegian Color Research Laboratory (2005-2012), member of the International Commission on Illumination (CIE) Division 8, and founder of Forum Farge (Norway’s color association). Collaborates with institutions like the Swiss National Museum and Edvard Munch Museum.

• 3D Object Retrieval
• Biometrics
• Shape Analysis
• +5 more

Theoharis Theoharis is a Professor at the Department of Computer Science (IDI), Norwegian University of Science and Technology (NTNU Trondheim), where he leads research in shape analysis and visual computing. He is a core member of the Visual Computing Lab , focusing on 3D object retrieval, biometrics, and cultural heritage applications. Research Interests : 3D Object Retrieval and Registration Biometric Identification (facial data, somatotype analysis) Shape Segmentation and Cultural Heritage Preservation Medical Imaging and Robotics Notable Projects : PRESIOUS : EU-funded initiative for cultural heritage using visual computing D4FLY : On-the-move biometric verification CHANGE ITN : Monitoring cultural heritage change with 15 PhD theses Publications highlight trends in: 3D shape descriptors (QUICCI, radial intersection count) Deep learning for 3D reconstruction and registration Applications in cultural heritage, medical imaging, and geophysics Education : BSc in Computer Science MSc in Computation DPhil in Computer Graphics and Parallel Processing Advising includes doctoral students like Evdokia Saiti (2023) and Peder Bergebakken Sundt (2024), with contributions to SHREC'16 and 3DOR2022 symposiums. Labs & Collaborations : Visual Computing Lab (NTNU) NTNU's Computing unit European Union FP7-ICT (D3.1 Deliverable)

Daniela BlauhutView profile

Associate Professor

• Interaction Design
• Human-Computer Interaction
• Sustainable Design
• +5 more

Daniela Blauhut is an Associate Professor at the Faculty of Information Technology, Engineering and Economics , University of Southeast Norway , specializing in interaction design and teaching design at the Department of Information Technology and Communication. With a background in graphic and product design and a PhD in interaction design, her research focuses on the human aspect of technological development. Current Courses : Design Methods (Fall), Sustainable Design and Creative Technologies (Spring), UI Design Project (Spring) Past Courses : Visual Communication, Design Process, Interaction Design, Bachelor's Thesis with Scientific Theory and Method Her research explores how technology integrates into human workflows, particularly in high-stakes environments like the oil and gas industry, and emphasizes democratizing design practices in the digital society. Publications include empirical studies on mobile-device use in industrial settings and critical reviews on digital fabrication in education. Blauhut supervises Bachelor's and Master's theses and serves as the 1st deputy representative on the faculty board (ITK), a member of the ITK program committee, and part of the National Professional Body for Design.

• Behavioral Economics
• Experimental Economics
• Energy Economics
• +1 more

Andrew Muteti Musau is a Professor in Business Economics at Molde University College, Faculty of Business Administration and Social Sciences. He joined as an associate professor in 2021 and was promoted to professor in 2024. His research spans behavioral economics, energy economics, and macroeconomics, with a focus on replication studies and experimental methods. PhD in Economics from the University of Trento (2021) Master’s in Business Administration from the University of Agder (2014) Postdoctoral research in energy economics at INN University (2014) His research interests include behavioral economics, experimental economics, energy economics, and macroeconomics. He has contributed to econometrics via Stata programming tools and explored inefficiency determinants in energy sectors, emotional responses in sports, and financial literacy. Recent work investigates personality impacts on financial knowledge gaps. Publications span econometrics, energy economics, behavioral and experimental economics, and replication studies. Teaching areas include econometrics, behavioral economics, and decision theory. Musau collaborates with institutions like the University of Agder, INN University, and the University of Trento.

Petra HatalovaView profile

Research Fellow

• Astronomy
• Astrophysics
• Planetary Science
• +3 more

Petra Hatalova is a Research Fellow at the Centre for Planetary Habitability at the University of Oslo. Her research focuses on planetary science, computational astrophysics, and exoplanet research, with particular emphasis on rocky exoplanets around K-dwarf stars and their compositional diversity. Her recent work involves studying planetary formation mechanisms and improving computational tools like the GENGA N-body integrator for simulating stellar and planetary systems. Publications highlight her contributions to understanding how planets of similar size can exhibit vastly different interior structures and geochemical properties. She can be contacted via email at petra.hatalova@geo.uio.no . No awards or students are mentioned in the available records.

• Machine Learning
• Predictive Analytics
• Time Series Analysis
• +5 more

Nurilla Avazov is an Associate Professor of Data Science at the Inland School of Business and Social Sciences, Inland Norway University of Applied Sciences. He holds dual PhDs: a PhD in Computer Science (2021) from the University of Auckland and a PhD in Information and Communication Technology (2015) from the University of Agder. His research focuses on machine learning, predictive analytics, time series analysis, wireless communication modeling, IoT systems, e-healthcare solutions, and human activity recognition through advanced signal processing techniques. Dr. Avazov’s academic career includes significant contributions to high-impact refereed journals and conferences. His work spans theoretical developments in algorithm design, practical implementations in sensor networks, and interdisciplinary applications in healthcare and smart environments. He has developed novel trajectory-driven channel models for mm-wave systems and pioneered backscattering-based human activity recognition methods. His expertise combines data science with telecommunications engineering, addressing challenges in non-stationary channel analysis, radar systems for indoor localization, and cybersecurity through wireless signal inference. Current research trends emphasize multimodal sensor fusion, real-time activity tracking, and privacy-aware IoT architectures. Dr. Avazov has advised no formally recorded students in the provided data. His research grants and funded projects are not explicitly detailed here, though his publications indicate sustained external collaboration and funding support. He is affiliated with the Business Analytics research group at his institution.

• Collaboration
• Interaction
• Computer Graphics

Atle Geitung is an Assistant Professor in the Department of Computing, Mathematics, and Physics at Western Norway University of Applied Sciences. No educational background information is provided. His research focuses on collaboration systems, interaction design, and computer graphics applications. Specific interests include human-computer interaction methodologies and visual computing techniques. No publications are listed in the provided text. No scientific awards are mentioned. No advising or funding information is documented. No laboratory or team affiliations are described.

• Collaboration
• Interaction
• Graphics

Assoc. Prof Carsten Helgesen is affiliated with the Department of Computing, Mathematics, and Physics at the Western Norway University of Applied Sciences. His research focuses on Collaboration, Interaction, and Graphics . Email: carsten.helgesen@hvl.no Office: E415

• Computer Graphics
• Geosciences Visualization
• Virtual Reality Applications
• +3 more

Assoc. Prof Daniel Patel holds a position in the Department of Computing, Mathematics, and Physics at Western Norway University of Applied Sciences (HVL). His research focuses on interdisciplinary applications of computer graphics and visualization techniques in geosciences, VR training systems, and educational technology. Key areas include collaborative software for subsurface CO2 storage, seismic data interpretation, and vision-training serious games for children. His work bridges computer science with geology, energy systems, and marine biology, emphasizing real-time visualization algorithms and secure rendering methods. He has pioneered VR training systems for safety assessment and developed web-based 3D geology visualization tools using open standards like X3DOM. His research also extends to acoustic marine species identification and GPU-optimized rendering techniques. Notable contributions include advancements in homomorphic-encrypted volume rendering for secure visualization, multi-GPU processing with the Vulkan API, and sketch-based geological modeling tools. His projects often integrate modern hardware (e.g., VR headsets) with game engines to enhance training and educational outcomes.

Jukka CoranderView profile

Professor

• Bayesian Statistics
• Inference Methods
• Stochastic Simulation Algorithms
• +7 more

Jukka Corander is a Professor at the University of Oslo's Department of Biostatistics, Faculty of Medicine, and concurrently holds positions at the University of Helsinki's Department of Mathematics and Statistics. He specializes in Bayesian statistics, microbial genomics, and evolutionary biology, with a focus on bacterial pathogen evolution and antibiotic resistance mechanisms. Affiliations: Professor, Department of Biostatistics, University of Oslo (since 2016) Professor, Department of Mathematics and Statistics, University of Helsinki (since 2009) Former roles at Åbo Akademi University and Churchill College, University of Cambridge Research Interests: Bayesian inference methods, stochastic simulation algorithms, machine learning applications in genomics, population genetics of bacteria, and forensic statistical analyses. Key Awards: 2015 Cozzarelli Prize (PNAS) for groundbreaking work on bacterial pathogen evolution 2008 Per Brahe Award (Young Scientist of the Year) Grants & Leadership: ERC StG Grant (SmartBayes, 2009–2014) COIN Centre of Excellence (Vice-Director, 2015–2017) Leadership roles in Helsinki Institute of Information Technology (HIIT) Labs & Collaborations: Leads the Corander Lab, focusing on computational methods for microbial genomics and evolutionary analysis. Collaborates globally on projects involving antibiotic resistance, pathogen transmission, and epidemiological modeling.

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.

• 3D Modeling
• Animation
• Digital Art
• +7 more

Fred Fröhlich is an Associate Professor at the Department of Game Development – The Game School within the Faculty of Film, TV and Games at the University of Innlandet. He specializes in 3D modeling, animation, and digital art, with over two decades of experience in computer graphics. His research focuses on the intersection of reality and digital representation, emphasizing real-time visualization, serious games, AR/VR, simulation, and motion capture. He leads development in the GoGreenRoutes (Horizon 2020) and Go Green Next (Horizon Health 2023) projects, creating VR urban landscapes based on real-world topographical data. Fröhlich previously collaborated with renowned artist studios like Alicja Kwade and Carsten Höller, providing CAD data and simulations for commissioned projects. He is affiliated with research groups such as UPEXRE (User Perception and Engagement in XR Experiences) and contributes to Technology for Health and Physical Activity initiatives. His work explores how immersive technologies can reconnect people with nature, as demonstrated in his 2025 mixed-methods study on VR in public engagement events and a 2023 trial on visualization techniques for nature connectedness. He holds a strong academic portfolio with publications in journals like Wellbeing, Space and Society and Journal of Environmental Psychology .

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.