Find Academic Supervisors

Daniel M. RoyView profile

Professor

• Machine Learning
• Statistics
• Probabilistic Programming
• +7 more

Daniel M. Roy is a Full Professor at the University of Toronto, with cross-appointments in the Department of Computer Science, Department of Statistical Sciences, and Department of Electrical and Computer Engineering. He serves as Research Director at the Vector Institute and holds the CIFAR Canada AI Chair. Research Focus: Foundational principles of prediction, inference, and decision-making under uncertainty across machine learning, statistics, mathematical logic, applied probability, and computer science. Scientific Contributions: Key work in learning theory, statistical network analysis, probabilistic programming, and information-theoretic frameworks for generalization. Awards: ICML 2024 Best Paper Award for "Information Complexity of Stochastic Convex Optimization" and promotion to Full Professor in 2024. Student Advising: Actively mentors Ph.D. candidates and postdoctoral researchers with strong quantitative backgrounds, particularly at the intersection of machine learning, statistics, and computer science. Email: daniel.roy@utoronto.ca

Yuriy RogovchenkoView profile

Professor

• Qualitative theory of differential equations
• Mathematical modeling in biology
• Perturbation methods
• +2 more

Yuriy Rogovchenko is a Professor in the Department of Mathematical Sciences at the University of Agder. His research spans differential equations, mathematical modeling, and education innovation, with applications in biology, social sciences, and engineering. Rogovchenko has contributed extensively to mathematics education through projects like PLATINUM (Erasmus+ Strategic Partnership) and CPEA-ST-2019/10067 (Eurasia project). PhD in differential equations (Institute of Mathematics, Kyiv, 1987) Regular Associate at Abdus Salam ICTP, Trieste (2004-2011) Editor for 11 international journals Referee for over 70 journals Research Interests: Qualitative theory of differential equations, perturbation methods, mathematical modeling in interdisciplinary contexts. He focuses on enhancing conceptual understanding through inquiry-based learning and nonstandard problems. Publications: Recent works include advancements in linear system observability, parameter identification methods, and educational studies on exact differential equations. His collaborations with Svitlana Rogovchenko and Matthias Pätzold highlight applications in engineering and biology. Awards: Sørlandet kompetansefonds research award (2016).

• Artificial Intelligence
• Machine Learning
• Probabilistic Graphical Models
• +7 more

Helge Langseth is a Professor at the Department of Computer Technology and Informatics , within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). His research focuses on Artificial Intelligence , Machine Learning , and Probabilistic Graphical Models , particularly Bayesian Networks and their applications in Decision Support Systems . Langseth's work addresses Explainable AI (XAI) , Reinforcement Learning , and Recommender Systems . He has contributed to Bayesian Optimization , Probabilistic Modeling , and Robotic Control in oceanic environments. His recent publications emphasize transparency , fairness , and scalability in AI systems, with applications spanning maritime trade, migraine diagnosis, and power grid management. He is affiliated with the Intelligent Systems Research Group at NTNU and actively mentors doctoral and master's students. Co-authored works with Yanzhe Bekkemoen , Sverre Herland , and Jørgen Hanssen reflect his role in advising the next generation of AI researchers.

• Numerical Analysis
• Differential Equations
• Geometric Numerical Integration
• +5 more

Elena Celledoni is a Professor in the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU). She has been employed at NTNU since 2004 and has held the position of professor since 2009. She is a member of the Differential Equations and Numerical Analysis Group at the Department of Mathematical Sciences and serves as its leader. Her educational background includes: Master's degree in Mathematics from the University of Trieste (1993) Ph.D. in Computational Mathematics from the University of Padua, Italy (1997) Elena Celledoni's research focuses on numerical analysis, particularly structure preserving algorithms for differential equations and geometric numerical integration. Her work bridges theoretical mathematics with practical computational methods, developing algorithms that maintain the geometric properties of the systems they approximate. She has made significant contributions to Lie group integrators, energy-preserving methods, and the application of these techniques to mechanical systems and shape analysis. In recent years, her research has expanded to include the intersection of numerical methods with machine learning, exploring how structure-preserving approaches can enhance neural networks and data-driven modeling. Her publications demonstrate a clear trend toward integrating traditional numerical analysis with modern machine learning techniques while maintaining a strong foundation in geometric integration and structure preservation. This interdisciplinary approach has led to innovations in neural ODEs, structure-preserving neural networks, and physics-informed machine learning models that respect the underlying mathematical structures of the systems they model. Elena Celledoni has received recognition for her work through the following honors: Member of the Royal Norwegian Society of Sciences and Letters Member of the European Consortium of Mathematics in Industry Council Member of the board of the International Council of Mathematics in Industry and Applications Editorial board member for SIAM Review, Journal of Computational Dynamics, Journal of Geometric Mechanics, Calcolo, and Networks and Heterogeneous Media As an advisor, she has mentored several students including Torbjørn Ringholm who completed his doctoral dissertation on 'Discrete gradient methods in image processing and partial differential equations on moving meshes.' Her research has been supported by various grants enabling her to lead projects on geometric numerical integration, collaborate internationally, and organize significant academic events such as the special semester at Isaac Newton Institute of MS in 2019 on 'Geometry, compatibility and structure preservation.' She leads the Differential Equations and Numerical Analysis Group at NTNU, which focuses on developing and analyzing numerical methods that preserve the geometric structure of differential equations. The group maintains active collaborations with researchers worldwide and has made substantial contributions to advancing the field of geometric numerical integration and its applications to real-world problems.

• Machine Learning
• Information Theory
• Deep Learning
• +6 more

Zhiyuan Wu is a Doctoral Research Fellow at the University of Oslo , affiliated with the Digital Signal Processing and Image Analysis research group under the Faculty of Mathematics and Natural Sciences . Education: Bachelor’s degree in Communication Systems and Information Technology from Lanzhou University, China Master’s degree from the Technical University of Munich, School of CIT Research Focus: Zhiyuan Wu specializes in machine learning, with particular emphasis on probabilistic graphical models, information theory, and tackling real-world challenges such as distributional shifts and privacy concerns. His work explores entropy regularization techniques to address label shift in distributed learning systems, aiming to improve model robustness and data privacy across diverse domains like medical applications. Publications & Research Trends: His recent publication at the International Conference on Learning Representations highlights a novel approach to mitigating label shift through entropy regularization. This aligns with his broader research goals of enhancing the adaptability and interpretability of machine learning models in dynamic, privacy-sensitive environments. Labs & Teams: He is actively involved with the Digital Signal Processing and Image Analysis (DSB) research group, contributing to collaborative projects that bridge theoretical advancements with practical implementations in machine learning.

Haakon SundbøView profile

Senior Lecturer

• Multimedia Technology
• E-Learning
• Photography
• +16 more

Haakon Sundbø is a Senior Lecturer at the Department of Information and Communication Technology, University of Agder. He teaches courses such as Computer Science (DAT100), Multimedia Design (DAT104), Web Publishing (DAT110), Visual Design (DAT222), Sound Engineering (DAT228), ICT and E-learning in Vocational Subjects (PED978), and Didactic Use of Digital Tools (MM903). His academic responsibilities also include guest lecturing and supervision in ICT Project (DAT215), Photography (DAT227), Video (DAT229), Bachelor's Project (DAT303), Web and Online Knowledge (MM901), Remote Operated Vehicles (MM202), Interaction Design (MM403), and Technology for Teachers (TEKPED60). Materials, Energy and Nanotechnology (BSc, University of Oslo) Multimedia Technology and Design (BSc, University of Agder) Multimedia and Educational Technology (MSc, University of Agder) Haakon's research focuses on lifelong learning, e-learning platforms, interaction design, user experience, artificial intelligence in education, augmented reality, virtual reality, and digital competence development. He has contributed to projects like Decentralized Competence Development (DEKOMP), UiA Consense, and drone technology research at University of Agder. His recent publications analyze AI integration in teaching, digital frameworks for schools, screen usage guidelines, and collaborative learning networks. He also leads a research group on Multimedia and E-learning, emphasizing flexible digital resources and practical technology adoption in educational contexts.

Ida ScheelView profile

Associate Professor

• Bayesian Statistics
• Data Science
• Machine Learning
• +7 more

Ida Scheel is an Associate Professor in Statistics and Data Science at the University of Oslo , Department of Mathematics. She specializes in Bayesian hierarchical modeling, recommendation systems, and stochastic processes on networks. Her research interests include: Bayesian statistics and model diagnostics Data science applications in environmental and health domains Network-based machine learning Uncertainty quantification in predictive modeling Recent publication trends show a focus on Bayesian model validation, machine learning for product adoption prediction, and real-estate analytics. She contributes to interdisciplinary projects like BigInsight and CELS . Scientific awards : Sverdrup Prize for Young Researchers (2011) Advising : Supervised 8 PhD students (main/co-supervisor) in areas spanning Bayesian causal effects, neural network survival analysis, and model conflict detection. Key grants include participation in the Data Science@UiO and Integreat projects. Labs/teams : Active member of the Center for Computational Inference in Evolutionary Life Science (CELS) and the BigInsight center.

Sergej StoppelView profile

Researcher

• Visual Data Science
• Data Visualization
• Human-Computer Interaction
• +3 more

Sergej Stoppel is currently a researcher (PostDoc) in the visualization group at the University of Bergen. His academic background includes a Mathematics master's degree (2014) and a PhD in 2018, both from the University of Bergen. His research focuses on visual data science, with an emphasis on interactive visualization techniques, human-computer interaction, and physical visualization systems. He has authored/co-authored numerous papers on topics such as hexagonal map enhancements, spatio-temporal data interaction, and low-cost physical art generation. Education: M.Sc. Mathematics (2014), University of Bergen PhD in Visualization (2018), University of Bergen Research Interests: His work spans data visualization, scientific visualization, and interactive techniques. Notable contributions include Vol²velle (printable interactive volume visualization widgets), Firefly (virtual illumination drones), and LinesLab (low-cost art generation systems). He explores methods to bridge the gap between virtual and physical visual representations, emphasizing user-centric design. Publications: His recent work includes advancements in hexagonal map visualization (2022), spatio-temporal selection techniques (2020), and illumination automation (2019). These contributions highlight his focus on enhancing data interpretation through novel interaction paradigms. Labs/Teams: Active member of the VisGroup at the University of Bergen, collaborating on projects like MetaVis and VIDI. His work is supported by grants involving visualization and medical imaging applications.

Eric MörthView profile

Researcher

• Medical Visualization
• Medical Imaging
• Biomedical Engineering
• +6 more

Eric Mörth is a PhD Scientist in Multimodal Medical Visualization at the University of Bergen's Department of Informatics, collaborating with the Mohm Medical Imaging and Visualization Center (MMIV). His research focuses on innovative medical data visualization techniques, such as MuSIC and ICEVis, which enhance clinical decision-making. Currently on a research stay at Harvard University's VCG Group, he holds a Master's in Medical Informatics from the Medical University of Vienna and a degree in Biomedical Engineering from the Technical University of Vienna. His awards include the Best Paper Honorable Mention (VCBM2022) and Best Short Paper (VINCI2022). Mörth's work spans cancer imaging, scrollytelling narratives, and interactive visualization tools, supported by grants from Trond Mohn Stiftelse. He advises through Team Smit and has contributed to projects like RadEx and ParaGlyder, advancing medical data exploration and communication.

• Serious Games
• Healthcare Technology
• Gamification
• +4 more

Simon McCallum is an Associate Professor at NTNU’s Faculty of Information Technology and Electrical Engineering. Born in New Zealand, he transitioned from commercial game development in Norway to academia in 2009. His research focuses on Serious Games, particularly in healthcare contexts, gamification, and virtual reality applications. He holds a PhD from the University of Otago (2007), where he studied Computer Science, Mathematics, Psychology, and Philosophy. McCallum has contributed to over 30 publications, including works on cognitive health gaming, software engineering education, and AR applications. He teaches courses such as IMT4307 (Serious Games Research) and IMT3603 (Game Programming). His key projects include the Smartkuber AR game for cognitive screening and the Traction Trebuchet historical engineering study. McCallum has collaborated with institutions like the University of Otago and has engaged in outreach through conferences and media appearances, promoting game technology in healthcare and education.

Jaakko Timo Henrik JärviView profile

Professor

• Generic Programming
• Programming Language Design
• Graphical User Interfaces (GUI)
• +7 more

Jaakko Timo Henrik Järvi is a Professor in the Department of Informatics at the University of Bergen, Norway, with additional affiliations at the University of Turku, Finland. His research focuses on programming language design, generic programming, and human-computer interaction, particularly in GUI frameworks and software reuse. His research interests include generic programming, programming language design (especially the Magnolia language), high-performance computing, array programming, and GUI engineering. He emphasizes formal methods and algebraic specifications to build reusable and efficient software systems. His work bridges theoretical foundations with practical applications in software development and education. The recent publications highlight a strong trend in declarative GUI frameworks, multi-selection models, and generic programming. His work explores domain-specific languages for GUI structure manipulation, reusable selection semantics across platforms, and optimizing array computations using the Mathematics of Arrays. These efforts reflect a consistent focus on software abstraction, correctness, and reusability. Jaakko Järvi has supervised doctoral students, including Tetiana Yarygina, whose dissertation explored microservice security. While no specific grants are detailed, his work on VisAST was supported by the Research Council of Norway (Project 250683), indicating active external funding. He frequently collaborates with researchers like Magne Haveraaen, Knut Anders Stokke, and Sean Parent. He contributes to tools and frameworks such as the MultiselectJS library and the VisAST educational tool. These are outcomes of collaborative research teams focused on improving software development practices and computer science education.

Jan G. BjaalieView profile

Professor

• Neuroinformatics
• Digital brain atlasing
• Brain architecture and connectivity
• +4 more

Jan G. Bjaalie is Professor of Neuroinformatics and Dean of Research and Innovation at the University of Oslo Faculty of Medicine . Since 2023 he heads the faculty’s research and innovation strategy, while directing the Neural Systems and Graphics Computing Laboratory at the Institute of Basic Medical Sciences. Education: 1990 Ph.D. in Neuroanatomy, University of Oslo 1986 M.D., University of Oslo Research interests revolve around collaborative and open neuroscience, digital brain atlasing, and the cyber-infrastructures that enable data sharing. He leads efforts to build next-generation atlases that integrate multi-scale brain architecture and connectivity data, and to develop ontologies and FAIR-compliant platforms for global neuroscience. Recent work emphasizes in silico integration of rodent and human imaging datasets, leveraging machine-learning registration tools and cloud-based services such as EBRAINS. The goal is to transform how brain data are stored, visualised and reused across laboratories worldwide. Scientific output & impact: A scan of publications from 2023-2025 reveals a strong focus on digital atlas frameworks, automated image registration (DeepSlice, DeMBA), open data standards (AtOM ontology), and large-scale analyses of genetic influences on brain structure in Alzheimer’s models. These works collectively advance reproducible, high-throughput neuroanatomy and cross-species translation. Grants & leadership roles: Coordinator/Partner in EU Flagships EBRAINS 2.0, BRAIN Health, Human Brain Project (2013-2026) Infrastructure Director, Human Brain Project (2018-2023) Leader of Neuroinformatics Platform & EBRAINS Data Services (2017-2023) Head of Institute of Basic Medical Sciences (2009-2016) Executive Director, International Neuroinformatics Coordinating Facility (INCF) (2006-2008) Chair, International Brain Initiative (2021-) Editorial & governance service: Founding Chief Editor Frontiers in Neuroinformatics (2007-), Section Editor Brain Structure and Function (2002-2020), member of the INCF Governing Board and EBRAINS AISBL Management Board, and numerous international advisory panels on data governance and ethics. His laboratory hosts the Norwegian Neuroinformatics Node and collaborates closely with global consortia to deliver open-access atlases, software pipelines and FAIR data standards that underpin modern neuroscience.

Solveig BruvollView profile

Associate Professor

• Autonomous Systems
• Cybersecurity
• Military Technology
• +4 more

Solveig Bruvoll is an Associate Professor at the University of Oslo (15% position) and holds a 100% research position at the Norwegian Defence Research Establishment (FFI). She specializes in Autonomous Systems and Cybersecurity , focusing on security in military operations and software resilience. Her work bridges engineering, mathematics, and defense applications. Research interests include autonomous vehicle security, defense simulation, and mathematical modeling. She teaches the course TEK5510 and has published extensively in conferences like IEEE CNS and journals such as Journal of Defense Modeling and Simulation . Her FFI affiliation emphasizes practical military technology and operational analysis. Publications highlight contributions to path planning algorithms, command agent modeling, and cybersecurity frameworks for autonomous systems. She collaborates on NATO projects, as seen in her work on M&S support for operational tasks. No awards are explicitly listed, but her research impacts critical defense and civilian infrastructure security domains.

Ole GoetheView profile

Associate Professor

• Artificial Intelligence
• User Experience (UX)
• Human-Computer Interaction (HCI)
• +5 more

Ole Goethe is an Associate Professor at Kristiania University of Applied Sciences within the School of Arts, Design and Media's Westerdals Department of Film and Media. He holds concurrent roles at NTNU and Nord University. His work bridges academia and industry through research in human-technology interaction, gamification, and inclusive design. Education: Executive Program in Project Management, BI Norwegian Business School (2010-2011) Master's in Motion Picture, Academy of Art University (2004-2008) Bachelor of Arts in Computer Graphics & Minor in Computer Science, Academy of Art University (1997-2002) Research Interests: Focuses on synergized human-technology interactions, inclusive design principles, and gamification applications. Specializes in creating engaging systems that promote diversity, longevity of user engagement, and accessibility. Active in HCI, service design, and AI-driven systems. His work emphasizes ethical UX practices, adaptive game mechanics, and accessibility frameworks to address societal challenges through technology. Article Trends: Recent publications explore 'Attainable Game Experiences' - a framework ensuring inclusive gaming accessibility. Explores universal design principles in XR, exergames, and gamified civic systems. Earlier work covers e-learning gamification challenges and VR embodiment theories. Awards & Recognition: Not explicitly stated in provided texts, but recognized as a Toptal verified expert in design with industry collaborations at Harvard and LucasFilm. Grants & Advising: No specific grants listed. Advised over 15 years across academia and industry, mentoring teams in SaaS, edtech, and game development. Involved in design sprints, user research, and agile methodologies. Labs & Teams: Core contributor to Kristiania's Game Development program and Excited Centre for Excellence in IT Education. Collaborates with industry on projects like Correlate (productivity app) and Picturemarks (digital humanities platform).

• Robotic Vision
• Autonomous Systems
• Computer Vision
• +6 more

Annette Stahl is a Professor at the Department of Engineering Cybernetics, NTNU, and an Onsager Fellow. She leads the Robot Vision Group and oversees the AILARON project, funded by the Research Council of Norway. Her expertise spans robotic vision, control theory, and autonomous systems. She holds a PhD in Applied Mathematics (Computer Vision) from Heidelberg University. Her research focuses on underwater robotics, autonomous vehicles, and aquaculture monitoring, with applications in fish health analysis and marine environmental sensing. Stahl has supervised numerous PhD and master’s students, including projects on autonomous ships (SFI AutoShip), underwater SLAM (AROS), and plankton identification. She collaborates with industry partners like Zebop AS and SINTEF Ocean. Notable projects include the robotic microplankton sniffer-dog and AI-driven fish welfare monitoring. Her work is published in top journals/conferences like IEEE Access and MIC Journal. She actively contributes to datasets like the VAROS Synthetic Underwater Data Set.