Find Academic Supervisors

• computer vision
• computer graphics
• visual perception

Giuseppe Claudio Guarnera serves as a Senior Researcher at the Norwegian University of Science and Technology (NTNU) in the Department of Computer Science, leading the Research Council of Norway-funded "Spectraskin" project on spectral skin modeling. He concurrently holds a Lecturer position in Computer Vision and Graphics at the University of York (UK) and has been a Guest Researcher at Justus-Liebig University Giessen's Department of Psychology since 2017. His academic background includes: Ph.D. in Computer Science from the University of Catania, Italy, with a dissertation in computer vision and pattern recognition Foundational research experience at the USC Institute for Creative Technologies (US) Guarnera's research centers on computer vision, computer graphics, and visual perception applications, with specialized expertise in spectral skin reflectance modeling, BRDF representation, and material acquisition for virtual environments. His work integrates perceptual validation to enhance realism in digital face rendering and fabric modeling, bridging theoretical computer graphics with practical VR applications. Analysis of his 2017-2020 publications reveals a consistent focus on material and skin reflectance measurement techniques, perceptual validation frameworks, and cross-renderer parameter remapping. Key trends include the development of spectral skin modeling for realistic digital faces (notably in the Spectraskin project), practical BRDF acquisition methods, and single-image fabric reconstruction—demonstrating strong interdisciplinary connections between computer vision, graphics, and human perception studies. Guarnera secures competitive research funding as Principal Investigator of the "Spectraskin" young research talents project awarded by the Research Council of Norway, which investigates spectral skin modeling and rendering for realistic digital faces. He directs the Spectraskin project team at NTNU, which specializes in advancing spectral skin modeling techniques through interdisciplinary collaboration between computer vision, graphics, and perceptual psychology researchers.

Trygve Brauns LeergaardView profile

Professor

• Neuroanatomy
• Brain connectivity mapping
• Morphological phenotyping
• +5 more

Trygve Brauns Leergaard is a Professor at the Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo. His academic work focuses on neuroanatomy, brain connectivity mapping, morphological phenotyping, and neuroinformatics, particularly in rodent models. MD, University of Leiden (1996) PhD, University of Oslo (2000) Research Interests: Leergaard specializes in digital brain atlasing, histological validation of neuroimaging, and neuroinformatics. His work bridges computational methods with neuroanatomical studies, emphasizing standardization of brain mapping techniques. Recent Publications: His 2024–2025 research includes developing spatial integration workflows, comparative brain atlas metadata models, and developmental mouse brain atlases. Earlier work (2018–2023) explored neurodegenerative disease models, brain tumor growth patterns, and Parkinson's disease mechanisms. Laboratory & Collaborations: He co-leads the Neural Systems and Graphics Computing Laboratory and collaborates internationally with institutions including UC San Diego, Duke University, and NTNU.

• Robotic Vision
• Autonomous Vehicles
• Computer Vision
• +7 more

Dr. Annette Stahl is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). She is an Onsager Fellow and leads the Robot Vision Group, overseeing the AILARON project funded by the Research Council of Norway. Her roles include affiliation with NTNU AMOS and the SFI AutoShip Centre for Research-based Innovation. Stahl holds a PhD in Applied Mathematics (Computer Vision) from Heidelberg University. Affiliations: NTNU AMOS, SFI AutoShip, and AILARON project leadership Research Focus: Robotic vision, autonomous systems, underwater exploration, and aquaculture monitoring Education: PhD in Applied Mathematics (Computer Vision) – Heidelberg University Research Interests: Her work spans robotic and computer vision, control theory, autonomous vehicles, mathematical image analysis, and machine learning applications in aquaculture and maritime systems. Key focus areas include underwater SLAM, sensor fusion for autonomous ships, and plankton detection using AI. Articles: Recent publications emphasize underwater robotic systems, sensor fusion for autonomous navigation, and AI-driven aquaculture monitoring. Key themes include 3D reconstruction, visual SLAM, and maritime tracking algorithms. Grants & Projects: AILARON (FRINATEK/IKTPLUSS) AUTOSIGHT (IKTPLUSS) AROS (IKTPLUSS) SFI AutoShip (SFI) Advising: Main supervisor for 10+ PhD candidates (e.g., Trym Nygård, Mauhing Yip) Co-advisor for projects like INDISAL (fish identification) Labs/Teams: Robot Vision Group at NTNU Collaborations with SINTEF Ocean and Zebop AS

Kjell Gunnar RobbersmyrView profile

Professor

• Mechatronics
• Machine Design
• Condition Monitoring
• +5 more

Kjell Gunnar Robbersmyr is a Professor at the University of Agder's Department of Engineering Sciences and director of the Top Research Center in Mechatronics. With a Ph.D. in mechanical engineering from NTNU (1992), his career spans academic leadership, research management at Agder Research, and active contributions to IEEE. His work focuses on mechatronics, machine design, and condition monitoring, with special emphasis on fault diagnosis in electric motors and vehicle crash modeling. Senior Member of IEEE Member of Norwegian Academy of Technical Sciences Member of Agder Academy of Sciences Research interests include: Advanced fault diagnosis in electric drives using AI and signal processing Vehicle crashworthiness modeling with lumped parameter and finite element methods Optical measurement technology for machine monitoring Digital twin applications for infrastructure and wind energy systems Condition monitoring of low-speed bearings and rotating machinery Recent publications demonstrate expertise in: Deep learning for imbalanced motor fault datasets Transformer networks in power electronics diagnostics 3D reconstruction techniques for mechanical systems Multi-classifier decision fusion in power systems Dynamic operations modeling for electric vehicles Scientific contributions include: Over 50 peer-reviewed articles Leadership in the Intelligent Monitoring research group Development of novel inverter topologies Innovations in wind turbine condition monitoring Advancements in laser-based mechanical diagnostics

Morgan KonnestadView profile

Associate Professor

• Computer Graphics
• Computer-Aided Design
• Motion Capture Technology
• +5 more

Morgan Konnestad is an Associate Professor in the Department of Information and Communication Technology at the University of Agder's Faculty of Technology and Natural Sciences. He has been employed full-time at UiA since January 1, 1989, and holds a Master's degree in Marine Technology from NTNU with a focus on Computer-Aided Design and additional coursework from the ICT department. His career spans over three decades of dedicated service to the university, with significant contributions to multimedia education and technology. Education Background: Master of Science in Marine Technology from NTNU (Computer-Aided Design focus) Additional coursework from NTNU's Department of ICT University Pedagogics Program completed at UiA in 2006 Konnestad's research interests center on visual computing technologies and their educational applications. He has been responsible for Computer Graphics and CAD courses since joining UiA, later expanding into Video, DVD/Blu-ray Authoring, and e-learning material production. His work demonstrates a consistent focus on practical applications of digital media technologies in educational contexts, with particular emphasis on motion capture systems, 3D visualization, and emerging AR/VR technologies. He has developed numerous multimedia production techniques specifically tailored for academic use and student learning. Analysis of his recent publications (2014-2023) reveals a strong trajectory toward immersive technologies and collaborative learning environments. His work increasingly focuses on motion capture applications, AR/VR implementations in education, and interactive systems for specialized training. The publications show a clear evolution from technical demonstrations of multimedia capabilities toward research-oriented investigations of how these technologies enhance learning outcomes and professional training, particularly in emergency management contexts. Academic Supervision: Assistant supervisor for Maurice Isabwe's PhD completed in 2013 Konnestad actively contributes to the university's outreach efforts through numerous presentations at open house events, high schools, and educational institutions. He has been instrumental in demonstrating UiA's Motion Capture Laboratory capabilities, providing hands-on experiences that showcase the practical applications of multimedia technology. His work bridges academic research with real-world implementation, particularly through collaborations with regional businesses and emergency management stakeholders. Laboratory Focus: Konnestad has been a driving force behind UiA's Motion Capture Laboratory, developing demonstration protocols and practical applications that support both educational and research objectives. His work with this facility has enabled innovative approaches to multimedia production, 3D visualization, and interactive learning environments.

• Embedded computer systems
• Real-time systems
• Technical cybernetics
• +5 more

Kristian Muri Knausgård is a Lecturer at the Department of Engineering Sciences , University of Agder , Norway. He teaches courses in embedded systems, software development, and robotics. Current courses: MAS245 (Embedded Computer Systems), MAS417 (Software Development), MAS418 (Robotics Programming) Previous courses: MAS218 (Electrical Circuits), MAS234 (Embedded Systems) His research focuses on embedded systems , real-time systems , and technical cybernetics , with applications in artificial intelligence , computer vision , and systems engineering . He contributes to the university's research groups on Robotics and Automation and Systems Engineering and Modeling . Recent publications show a strong emphasis on: Autonomous systems (robotics, docking algorithms) Deep learning applications in marine ecology 3D reconstruction and computer vision techniques Fish detection/classification using neural networks Industrial automation for aquaponic systems His work bridges theoretical research with practical implementations in mechatronic systems and environmental monitoring.

Allah BuxView profile

Research Fellow

• Computer Vision
• Medical Image Analysis
• Deep Learning
• +6 more

Allah Bux is a Research Fellow at the Department of Linguistic, Literary and Aesthetic Studies, University of Bergen. His work focuses on advanced computer vision techniques, medical image analysis, and deep learning applications. He holds expertise in 3D reconstruction, anomaly detection, and surveillance systems. His research interests include developing novel neural network architectures for tasks such as skin cancer diagnosis, head pose estimation, and human action recognition. He also explores spatiotemporal analysis and attention mechanisms in CNN models. Recent trends in his publications emphasize integration of vision transformers, dual attention networks, and transfer learning for solving real-world problems in healthcare, security, and education. No scientific awards or grants are explicitly listed in the provided text. He has no documented advisees. His research involves collaborations in medical imaging, autonomous surveillance systems, and educational data analysis, with a focus on practical applications of AI.

Mikhail BarashView profile

Associate Professor

• Software Language Engineering
• Domain-Specific Languages (DSLs)
• Graphical User Interface (GUI) Frameworks
• +3 more

Mikhail Barash is an Associate Professor at the Department of Informatics, University of Bergen. His research focuses on software language engineering, domain-specific languages (DSLs), and graphical user interface (GUI) frameworks. He explores innovative approaches to DSL design, including spreadsheet-based workbenches and reusable GUI structures. His work emphasizes user involvement in language standardization, such as in ECMAScript/JavaScript evolution. He also investigates formal methods for GUI abstraction and constraint-based systems. Barash collaborates with industry and academia, notably through the Magnolia programming language ecosystem and teaching experiences with JetBrains' MPS tooling. His contributions span over 20 peer-reviewed publications in top conferences like ACM SIGPLAN and IEEE. Key research areas include declarative GUI manipulation, language workbench democratization, and legacy grammar modernization. He has presented at venues such as the International Symposium on Formal Methods and contributes to frameworks like Event-B IDE development. His work bridges theoretical advances with practical tooling, aiming to make language engineering accessible to broader audiences.

Johan PensarView profile

Associate Professor

• Statistical Machine Learning
• Probabilistic Graphical Models
• Causal Learning
• +3 more

Johan Pensar is an Associate Professor of Statistics and Data Science at the University of Oslo's Department of Mathematics. He holds a PhD from Åbo Akademi University (2016) and was a postdoc at the University of Helsinki (2016–2020). His research focuses on statistical machine learning, probabilistic graphical models, causal inference, and applications in genomics. He has supervised multiple PhD students and co-supervised others in interdisciplinary projects, including causal modeling in healthcare and machine learning for microbiology. Education: PhD in Statistics, Åbo Akademi University, 2016 Postdoctoral Researcher, University of Helsinki, 2016–2020 Research Interests: Pensar's work integrates statistical theory with practical applications. Key areas include developing methods for causal discovery, probabilistic graphical models (e.g., Bayesian networks), and their use in genomics and healthcare. He emphasizes interpretable machine learning and robust statistical frameworks for complex data. Publications: Recent work spans causal inference, microbial genome analysis, and housing market prediction. His methods address challenges like confounding bias, generalization in ML, and uncertainty quantification in valuation models. Awards: Finnish Statistical Society Doctoral Thesis Award (2013–2016) Teaching & Advising: Pensar teaches advanced courses in statistical learning and probabilistic graphical models. He advises PhD students on causal modeling, ML in healthcare, and data science applications. He collaborates with industry partners like Integreat and Eiendomsverdi AS. Lab/Teams: He is affiliated with the Norwegian Centre for Knowledge-driven Machine Learning (Integreat) and leads research on Bayesian methods in ML.

Pekka ParviainenView profile

Associate Professor

• Machine Learning
• Bayesian Networks
• Graphical Models
• +7 more

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.

Noeska SmitView profile

Professor

• Medical Visualization
• Computational Medicine
• Multimodal Imaging
• +3 more

Noeska Smit is a Professor in Medical Visualization at the Department of Informatics, University of Bergen , where she has held a tenure-track position funded by the Trond Mohn Foundation since 2017. She is also a senior researcher and member of the leadership team at the Mohn Medical Imaging and Visualization (MMIV) Centre . Her research focuses on novel interactive visualization techniques for exploring and communicating multimodal medical imaging data , particularly in multi-parametric MR acquisitions . She leads projects in gynecologic cancer imaging , Multiple Sclerosis neuroimaging , and human anatomy education through collaborations with institutions like UGent (Belgium) and HVL. 2019: Dirk Bartz Prize for Visual Computing in Medicine 2016: PhD at Delft University of Technology , Netherlands 2012: MSc in Computer Science (Computer Graphics & Visualization) , Delft University Recent publications highlight her work in MRI radiomics , narrative visualization , open-source anatomy platforms , and interactive clustering tools for tumor analysis. Her methods are applied in oncological pelvic surgery planning , neurological disease monitoring , and 3D learning environments . She supervises PhD candidates Eric Mörth and Sherin Sugathan , and has contributed to open-source medical visualization tools like RegistrationShop and Online Anatomical Human (OAH) . Her work bridges clinical practice and computer science through collaborations with radiologists, surgeons, and ML researchers.

Lei JiaoView profile

Professor

• Artificial Intelligence
• Machine Learning
• Tsetlin Machines
• +7 more

Lei Jiao is a Professor in the Department of Information and Communication Technology at the University of Agder's Faculty of Engineering and Science. Previously serving as an Associate Professor from May 2014 to October 2022, Dr. Jiao has established himself as a leading researcher in artificial intelligence, with particular expertise in Tsetlin Machines and their applications across diverse domains. PhD in Information and Communication Technology, University of Agder (2008-2012) Master of Engineering in Communication and Information System, Shandong University (2005-2008) Bachelor of Engineering in Telecommunication Engineering, Hunan University (2001-2005) Dr. Jiao's research spans multiple cutting-edge areas including interpretable artificial intelligence, wireless communication protocols, network resource allocation, and signal processing. His work on Tsetlin Machines has pioneered new approaches to machine learning that emphasize interpretability while maintaining high performance. The research group he contributes to at the University of Agder focuses on Autonomous and Cyber-Physical Systems (ACPS), Battery recycling, and the Centre for Artificial Intelligence Research (CAIR). Analysis of Dr. Jiao's recent publications reveals a strong emphasis on interpretable AI systems, particularly through Tsetlin Machines. His work spans applications in GNSS jammer detection, crowd anomaly detection, DNA sequence classification, and hardware acceleration of machine learning models. The research consistently demonstrates how logical, rule-based approaches can provide transparent alternatives to traditional neural networks while maintaining competitive performance. Supervised numerous PhD students including Vojtech Halenka, Ahmed K. Kadhim, and Sindhusha Jeeru Mentored over 30 Master's thesis projects covering topics from Tsetlin Machines to signal processing and computer vision Collaborates extensively with Ole-Christoffer Granmo and other leading researchers in the AI field Dr. Jiao actively contributes to advancing the field through supervision of doctoral candidates, collaboration on major research projects, and development of novel machine learning approaches that balance performance with interpretability. His work bridges theoretical foundations with practical applications across telecommunications, computer vision, and natural language processing domains.

Anna PlaksienkoView profile

Research Fellow

• High-dimensional statistics
• Omics data integration
• Multi-omics analysis
• +4 more

Dr. Anna Plaksienko is a Research Fellow in High-dimensional Statistics at the University of Oslo's Institute of Basic Medical Sciences, Faculty of Medicine. Her work focuses on statistical methods for omics data integration, particularly in multi-source high-dimensional regression and graphical models. She develops computational tools like the methyLImp2 and jewel R packages for missing value imputation and joint network estimation. Education: PhD in Mathematics (2021) - Gran Sasso Science Institute, Italy BSc in Mathematics (2016) - Novosibirsk State University, Russia Research Interests: She explores differential network estimation, stability selection, and error control in omics data (RNA-Seq, methylation, lipidomics). Her methods aim to uncover biological insights from multi-omics integration and high-dimensional datasets. Labs/Teams: She is part of the High-dimensional Statistics research group at the Institute of Basic Medical Sciences, collaborating on projects involving statistical methodology and computational biology.

Stefan BrucknerView profile

Professor

• Data Visualization
• Interactive Visualization Techniques
• Biomedical Data Visualization
• +4 more

Stefan Bruckner is a Professor of Visualization, currently heading the Chair of Visual Analytics at the University of Rostock, Germany, after previously serving as a full Professor at the University of Bergen, Norway. He holds a master's (2004) and PhD (2008) in Computer Science from TU Wien, Austria, and an habilitation (2012). His research focuses on interactive visualization techniques, particularly in biomedical and data-driven contexts, with contributions spanning illustrative visualization, volume rendering, and narrative medical storytelling. He has led industry collaborations with GE Healthcare and Agfa HealthCare, resulting in 7 patents. His academic roles include program co-chair for EuroVis, PacificVis, and the Eurographics Medical Prize, and editorial board membership in IEEE Transactions on Visualization and Computer Graphics and Computers & Graphics. Awards include the Eurographics Young Researcher Award and Karl-Heinz-Höhne Award for Medical Visualization, alongside 11 best paper awards. Research emphasizes narrative visualization for disease communication, user-centric medical data exploration, and innovative visual metaphors like Honeycomb Plots and ScrollyVis. His work bridges academic research with practical applications, fostering interdisciplinary collaborations in healthcare and computational biology.

Ivan ViolaView profile

Professor

• Medical Visualization
• Molecular Visualization
• Data Visualization
• +3 more

Ivan Viola is a Professor at the University of Bergen's Department of Informatics. His research focuses on medical and molecular visualization, with a particular emphasis on biomedical applications such as fetal ultrasound analysis, protein interactions, and biomolecular dynamics. He leads the VisGroup, developing tools like COZOID for protein-protein interaction analysis and The Vitruvian Baby for fetal ultrasound standardization. His work spans interdisciplinary collaborations, integrating computer science with biology and medicine. Key contributions include advancements in visualization techniques for physiological data, molecular dynamics simulations, and real-time data processing. He has published extensively in top journals and conferences, addressing challenges in data exploration, abstraction, and interactive visualization. Viola's research impacts fields such as prenatal diagnostics, drug discovery, and medical imaging, with tools like Watergate for water trajectory analysis and cellVIEW for multi-scale biomolecular rendering. His projects emphasize user-driven design and the development of intuitive visualization solutions for complex scientific data.