Find Academic Supervisors

• Artificial Intelligence
• Machine Learning
• AI Alignment
• +2 more

Michael Riegler is a Researcher at the AI Department, Simula Research Laboratory , focusing on interdisciplinary applications of Artificial Intelligence in healthcare, sports analytics, and multimedia systems. His work bridges Machine Learning , AI Alignment , and Applied AI across clinical and real-world domains. Key Affiliations: Simula Research Laboratory (AI Department Head) Research Themes: Explainable AI in medicine, multimodal data analysis, and AI-driven health monitoring Research Interests include: Developing AI/ML algorithms for medical imaging (e.g., polyp detection, embryo analysis) Addressing missing data challenges in healthcare through novel imputation techniques Creating multimodal virtual avatars for investigative interview training Designing edge AI systems for sports analytics and sustainable fishing Recent Publications highlight collaborations with institutions in Norway and globally, with a focus on: Medical Applications: Polyp segmentation, ECG analysis, and explainable models for disease detection Sports Analytics: Athlete performance prediction and soccer video processing Data Infrastructure: Lifelogging datasets (ScopeSense), semantic representation frameworks Labs & Teams include leadership in Simula’s AI Department and participation in projects like Medico Multimedia Task , ImageCLEF , and MediaEval workshops. His work emphasizes responsible AI innovation in public sectors and privacy-preserving systems for edge environments.

Vasileios MavroeidisView profile

Associate Professor

• Cybersecurity
• Security Automation and Orchestration (SOAR)
• Cyber Threat Intelligence (CTI)
• +6 more

Vasileios Mavroeidis is an Associate Professor in Digital Security at the Department of Informatics, University of Oslo (UiO). He specializes in security automation and orchestration (SOAR) and cyber threat intelligence (CTI) representation, reasoning, and sharing. He actively contributes to European cybersecurity initiatives, including Horizon Europe, Connecting Europe Facility, and the European Defense Fund, and serves as the primary representative of UiO at the OASIS standards development organization since 2017. Role : Associate Professor Department : Digital Security (SEC), University of Oslo Standardization Involvement : Chairman of OASIS Threat Actor Context (TAC), Leading Contributor to CACAO and OpenC2 Projects : Concordia, CyberHunt, JCOP (Joint Cyber Security Operations Platform), Oslo Analytics, P4C (Partnership for Cybersecurity) His research focuses on cyber threat intelligence (CTI), exploring its taxonomies, sharing standards (STIX, CACAO), and ontologies, with contributions to the European Union Agency for Cybersecurity (ENISA) Cybersecurity Playbooks task force. He analyzes quantum computing's impact on cryptography, develops automated threat detection systems using machine learning (e.g., recurrent neural networks for malware-generated domains), and investigates privacy issues under GDPR. Recent publications highlight his work on LLMs for code stylometry , neurosymbolic AI for cyber defense , and knowledge management systems for CACAO playbooks . His articles span 2017–2025, emphasizing formal verification, biometric data protection, and incident response automation. He collaborates with organizations like OASIS (Threat Actor Context, CACAO, OpenC2) and FIRST (Traffic Light Protocol), and participates in European research projects. His work includes standardization efforts in cybersecurity playbooks , MITRE ATT&CK representation, and quantum-resistant cryptography .

Svitlana RogovchenkoView profile

Professor

• Differential Equations
• Impulsive Differential Equations
• Asymptotic Methods in Nonlinear Mechanics
• +3 more

Svitlana Rogovchenko is a Professor at the Department of Engineering Sciences, University of Agder, Norway. Her academic work bridges pure mathematical research in differential equations with innovative pedagogical approaches in engineering and interdisciplinary education. Educational Background: Doctor of Philosophy in Differential Equations (1988), Institute of Mathematics, National Academy of Sciences, Kyiv, Ukraine Master of Science in Mathematics (1983), Kyiv State University, Ukraine Research Interests: Qualitative theory of ordinary and impulsive differential equations, asymptotic methods in nonlinear mechanics, mathematical modeling, and the intersection of mathematics education with engineering and biology curricula. She focuses on conceptual understanding, pedagogical challenges, and commognitive conflicts in learning differential equations. Recent Publications Trends: Her work spans mathematical modeling of vehicle crashworthiness, interdisciplinary education for biologists and engineers, and pedagogical innovations in ordinary differential equations. Articles emphasize assessment design, group work tensions, and bridging mathematical theory with applied contexts.

• Machine Learning
• Graph Neural Networks
• Time Series Analysis
• +7 more

Filippo Maria Bianchi is an Associate Professor in the Department of Mathematics and Statistics at UiT The Arctic University of Norway, where he conducts research at the intersection of machine learning, dynamical systems, and complex networks. He is also a Senior Researcher at NORCE Norwegian Research Centre and actively contributes to the IEEE Task Force on Learning for Structured Data and the ELLIS Society. Department: Department of Mathematics and Statistics School: Faculty of Science and Technology University: UiT The Arctic University of Norway Adjunct Position: Senior Researcher, NORCE Education: Bachelor’s in Computer Engineering, Sapienza University of Rome Master’s in Artificial Intelligence & Robotics, Sapienza University of Rome (cum laude, 2012) PhD in Machine Learning, Sapienza University of Rome His research focuses on graph machine learning, time series analysis, reservoir computing, and probabilistic forecasting , with applications in energy analytics and remote sensing. He has led and contributed to numerous projects involving Arctic power grids, satellite-based environmental monitoring, and deep learning for sustainability. The recent publications reflect a strong trend in graph neural networks —particularly pooling mechanisms, spatiotemporal modeling, and explainability—alongside applications in energy forecasting, avalanche detection, and remote sensing . His work combines theoretical innovation with real-world impact, especially in Arctic and remote environments. Scientific Affiliations and Leadership: Vice-Chair, IEEE Task Force on Learning for Structured Data Member, ELLIS Society Co-founder, Northernmost Graph Machine Learning group Member, IEEE Task Force on Reservoir Computing Visiting Professor, Politecnico di Milano (2024–2025) He actively mentors students and collaborates on interdisciplinary research. He has led projects in power grid reliability, solar fault detection, and unsupervised change detection in satellite imagery . His work is supported by open-source implementations and reproducible research practices. Laboratories and Research Groups: Northernmost Graph Machine Learning group (co-founder) ARC Research Group, UiT Graph Machine Learning Group, Lugano

• Machine Learning
• Artificial Intelligence
• Medical Imaging
• +4 more

Elisabeth Wetzer is an Associate Professor in Machine Learning at the Department of Physics and Technology, UiT The Arctic University of Norway. Her research bridges artificial intelligence with healthcare applications, focusing on multimodal image registration, bias mitigation in AI, and physics-informed learning models. Current Role: Associate Professor, Machine Learning Group Research Themes: AI ethics, medical imaging, cross-modal representations, algorithmic fairness Her recent work explores technical challenges in PET imaging analysis and societal implications of AI bias. Collaborative projects span medicine, mathematics, and computer science disciplines. Key scientific contributions include: Physics-informed deep learning for PET image data Studies on multi-task learning efficacy in medical classification Research on gender bias in algorithmic systems She actively participates in diversity initiatives and public outreach, including presentations at Nobel laureate conferences and media engagements on AI ethics.

Hans Jonas Fossum MoenView profile

Associate Professor

• Autonomous Systems
• Sensor Technologies
• Robotics
• +3 more

Hans Jonas Fossum Moen is an Associate Professor with a 20% appointment at the Department of Technology Systems, University of Oslo (UiO), and holds a 100% position as a researcher at the Norwegian Defence Research Establishment (FFI). His primary affiliation is with the Section for Autonomous Systems and Sensor Technologies. He is based at the Kjeller campus, with a visiting address at Gunnar Randers Road 19 and a postal address at Postboks 70. His research focuses on advancing autonomous systems and sensor technologies, particularly in the domains of swarm robotics, multi-agent coordination, and optimization algorithms. Key areas include UAV navigation, distributed localization in IoT networks, radar detection enhancement, and adaptive control systems for multi-functional swarms. He emphasizes the integration of biological principles into robotic systems, as evidenced by his participation in the ICRA 2018 Workshop on Swarms. His publications consistently highlight contributions to swarm intelligence, with a focus on improving data quality and efficiency in robotics applications. He has collaborated extensively with colleagues such as Kyrre Glette, Oleg Yakimenko, and Jan Dyre Bjerknes, exploring topics ranging from task allocation in multi-agent systems to evolutionary algorithms for filter optimization. His work bridges theoretical computer science with practical engineering challenges in autonomous systems. No scientific awards have been explicitly mentioned in the provided texts. Moen’s advising and grants narrative indicates no listed advisees or active grant projects, though his 20% UiO position suggests potential involvement in academic supervision. His primary research activities are embedded within FFI and the Autonomous Systems section at UiO, contributing to interdisciplinary efforts in sensor technologies and robotic systems.

• Control Systems
• Robotics
• Marine Robotics
• +3 more

Kristin Y. Pettersen is a Professor at the Department of Technical Cybernetics, Norwegian University of Science and Technology (NTNU), and a Professor II at the Norwegian Defence Research Institute (FFI). She is a co-founder of Eelume AS, a company specializing in underwater robotics solutions. Education: Civil Engineering and PhD in Technical Cybernetics from NTNU Her research focuses on advanced control systems for marine and underwater vehicles, particularly snake robots and autonomous underwater vehicles (AUVs). Key areas include formation control, path following, adaptive guidance algorithms, and safety-critical control in dynamic environments. Recent work explores machine learning integration and energy-shaping techniques for robust locomotion. Publications highlight trends in Model Predictive Control (MPC) , Collision Avoidance , and Task-Priority Operational Space Control for redundant and underactuated systems. Her work bridges theoretical control theory with practical applications in marine robotics, including autonomous inspections and cooperative transport. Labs/Teams: Collaborates with NTNU's Faculty of Information Technology and Electrical Engineering and co-founded Eelume AS, advancing subsea robotic manipulation technologies.

Jim TørresenView profile

Professor

• Artificial Intelligence
• Robotics
• Biology-Inspired Computing
• +6 more

Jim Tørresen is a Professor of Computer Science at the Department of Informatics, University of Oslo, where he has been employed since 1999 (Associate Professor 1999-2005, Professor since 2006). He serves as group leader for the Robotics and Intelligent Systems (ROBIN) research group and is also a Principal Investigator at the Centre for Interdisciplinary Studies in Rhythm, Time and Motion (RITMO). His academic career includes visiting positions at Cornell University's Creative Machines Lab (2010-2011) and Kyoto University in Japan (1993-1994). His educational background includes a Dr.ing. (Ph.D.) in Computer Architecture from the Norwegian University of Science and Technology (1996) and an M.Sc. in Computer Architecture from the same institution (1991). Before his academic career, he worked in industry at Navia Aviation (1998-1999) and NERA Telecommunications (1996-1998). Tørresen's research spans artificial intelligence, robotics, and bio-inspired computing. His work focuses on biology-inspired algorithms, programmable logic (FPGA), robotics (simulation, prototyping, control), and human-robot interaction. He has made significant contributions to areas including evolutionary computing, reconfigurable hardware, and adaptive systems. His research often bridges theoretical computer science with practical applications in healthcare, music, and industrial settings. His recent publications demonstrate a strong focus on human-robot interaction, particularly in healthcare contexts for elderly care, as well as applications in sports science, musical robotics, and geological engineering. His work shows a consistent pattern of interdisciplinary research that combines machine learning techniques with domain-specific challenges. Tørresen has also authored a popular science book on artificial intelligence in the "what is" series by Universitetsforlaget, which discusses fundamental concepts, methods, future perspectives, and ethical aspects of AI. He has been active in academic leadership, serving as General Chair for the 22nd International Conference on Field Programmable Logic and Applications (FPL) in 2012 and the 9th Joint IEEE International Conference of Developmental Learning and Epigenetic Robotics in 2019. As group leader of ROBIN, he oversees research on intelligent systems that operate in dynamic environments requiring runtime adaptation. The group works at both fundamental and applied levels, using evolutionary algorithms for robot learning and machine learning techniques for classification and recognition tasks in various application domains.

• Mathematics Education
• Metascience
• Classroom Research
• +4 more

Roar Bakken Stovner is an Associate Professor at the Department of Primary and Secondary Teacher Education within the Faculty of Education and International Studies at Oslo Metropolitan University (OsloMet), where he also serves as Head of Studies - Area of Responsibility 2. His academic career at OsloMet began as an Assistant Professor (2021-2023) before his promotion to Associate Professor in 2023. Prior to his university position, he worked as a teacher in lower and upper secondary schools in Oslo (2012-2018) and completed his PhD at the University of Oslo (2016-2020). He holds a Master of Science and Technology from NTNU (2007-2012) with a thesis on applied computational topology. Stovner's research spans several interconnected domains within educational science: Developing methodologies for describing and measuring teaching quality across multiple classrooms, with special emphasis on mathematics education Metascience research examining how citation practices distort findings and confer unwarranted authority to claims in educational research Creating programming tools for educational research, including the gptworkr package for text analysis in R using ChatGPT His scholarly work focuses on classroom research, mathematics education, mathematical competencies, metascience, and citation analysis. He actively participates in research groups focused on Classroom Research and Task Design in Mathematics Education, and leads significant involvement in the Teacher Education Panel Study (TEPS), a comprehensive longitudinal study examining teacher education implementation in Norway. Stovner's recent publications demonstrate substantial engagement with contemporary issues in educational research methodology, AI applications in education, and critical examination of scientific practices. His work on the EDUCATE project has produced influential reports on algorithmic thinking in mathematics education across different grade levels and exploratory teaching approaches in upper secondary education. His contributions to educational discourse include conference presentations, book chapters, and practical resources for teacher education, such as "Bedre masterskriving – en bok for lærerstudenter" (Better Master's Writing - A Book for Teacher Students), which supports academic writing development among future educators.

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.

• Mathematics Education
• Teacher Education
• Cognitive Psychology
• +5 more

Danyal Farsani is an Associate Professor at the Department of Teacher Education , Norwegian University of Science and Technology (NTNU), specializing in mathematics education through the lens of embodied cognition and cultural responsiveness . His work bridges teacher training , gesture-based learning , and cross-linguistic analysis in educational contexts. Key Research Themes: Embodied mathematical concepts, cognitive load in arithmetic, cultural/ethnomathematical practices, and multimodal communication in classrooms. Recent Collaborations: Focus on global North-South educational dynamics, robotics integration in teacher training, and metaphorical processing in language learning. Methodological Focus: Empirical studies using mental arithmetic tasks, gesture analysis, and cross-cultural curriculum evaluations. Publication Trends (2022–2025): Analysis of 15 recent articles reveals recurring emphasis on embodied cognition , mathematical metaphors , visual attention mechanisms , and culturally adaptive teaching tools like the Ethiopian Gebeta game. Subfields span gesture studies , cross-linguistic pedagogy , and innovative teacher education frameworks . No scientific awards or student advisories are explicitly mentioned in the provided data.

Esther UlitzschView profile

Associate Professor

• Psychometric Model Development
• Bayesian Latent Variable Models
• Small-Sample Estimation Techniques
• +3 more

Esther Ulitzsch is an Associate Professor at the University of Oslo 's Centre for Educational Measurement (CEMO) . Her research focuses on advancing psychometric models, particularly Bayesian latent variable techniques for small-sample conditions, and analyzing digital interaction data from simulated learning environments. She holds a PhD from Freie Universität Berlin and previously worked as a Research Associate at the IPN – Leibniz Institute for Science and Mathematics Education in Kiel, Germany. Education: PhD in Educational Measurement (Freie Universität Berlin) Research Associate at IPN Kiel Research Interests: IRT model development for aberrant response detection Efficient estimation in small samples Clickstream analysis for student behavior Test-taking engagement dynamics Publications: Over 20 peer-reviewed articles since 2017, including work on mixture models for careless responding, neural networks for IRT estimation, and Bayesian factor modeling. Recent contributions address response time analysis, cross-country measurement invariance, and sequential process mining in interactive tasks. Affiliations: Active in the CREATE (Research on Equality in Education) and FREMO (Frontier Research in Educational Measurement) groups at CEMO.

Andreas PettersenView profile

Researcher

• Mathematical Competence Assessment
• Mathematical Task Analysis
• Educational Equity
• +3 more

Andreas Pettersen is a Researcher at the Department of Teacher Education within the University of Oslo . He holds a PhD in Mathematics Education from the University of Oslo (2014–2019) and previously worked as an Adviser developing national mapping tests in mathematics for grades 1–3. His research focuses on mathematical competence assessment, task analysis, educational equity, and large-scale data analysis. Key research interests include understanding mathematical competence assessment methods, analyzing task demands in education, and exploring students' attitudes and equity in education. His work is methodologically quantitative, leveraging large-scale datasets like PISA to investigate educational outcomes. Recent publications (2024) examine resilient student profiles, Nordic mathematics education practices, and comparisons between PISA results and Norwegian curriculum standards. He co-edits a book series on PISA 2022 outcomes and contributes to the Large-scale Educational Assessment (LEA) research group. Pettersen collaborates on projects like Kartleggingsprøver i regning and contributes to national educational policy discussions. His interdisciplinary approach bridges mathematics education, psychology, and policy analysis.

• Mathematical Logic
• Nonstandard Analysis
• Fixed Point Theory
• +7 more

Eyvind Martol Briseid is an Associate Professor at the Department of Primary and Vocational Teacher Education, Faculty of Teacher Education and International Studies, Oslo Metropolitan University (OsloMet). His work bridges mathematical logic and mathematics education, with a focus on task design, proof, and argumentation in teaching. He is actively involved in research groups on task design in mathematics teaching and mathematics didactics. His research interests span Mathematical Logic , Nonstandard Analysis , Fixed Point Theory , and Mathematics Education . He investigates foundational aspects of mathematics, such as the limits of provability and rates of convergence, while also applying logical insights to pedagogical contexts. His work on task design emphasizes cognitive engagement, reasoning, and the integration of reading in mathematics. The recent publications reflect a dual trajectory: one in pure mathematics, particularly in logic and analysis, and another in educational research, especially on teacher training, proof, and curriculum. Trends include extracting computational content from proofs, functional interpretations in nonstandard arithmetic, and situational interest in word problems among prospective teachers. His interdisciplinary approach connects deep theoretical work with practical classroom applications. Scientific Awards: No awards listed in the provided text. Briseid advises on teacher education and curriculum development, particularly in mathematics. He has contributed to projects on reading in mathematics and guided schools in cross-curricular teaching. He has not listed formal PhD or master’s students in the provided materials. His research has been supported through institutional affiliations and collaborative projects, such as those under "Ungdomstrinn i utvikling." He is a member of research groups focused on Task design in mathematics teaching and Mathematics didactics: proof and argumentation . These groups engage in seminars, dissemination, and collaborative research with institutions like NTNU and the University of Agder. His work involves both theoretical development and practical implementation in teacher training and school development.

Nani TeigView profile

Associate Professor

• Science Education
• Large-Scale Assessment
• Inquiry-Based Teaching
• +5 more

Nani Teig is an Associate Professor at the University of Oslo 's Department of Teacher Education and School Research , specializing in science education and large-scale assessments. Her work bridges empirical research with educational policy, focusing on instructional quality, academic resilience, and equity in student outcomes. Education : PhD in Science Education (2019, University of Oslo) Master's in Science Education (2011, University of Texas at Austin) Diploma in Applied Statistical Modeling (2011, University of Texas at Austin) Bachelor's in Physics (2005, State University of Malang) Research Interests : Nani investigates inquiry-based teaching , scientific reasoning , and teacher competence through large-scale data from PISA, TIMSS, and PIRLS. Recent studies focus on academic resilience , assessment practices , and equity in Nordic education systems . Scientific Awards : Bruce H. Choppin Dissertation Award (IEA) Young CAS Grant (2022-2024) Global Education Monitoring Fellowship (UNESCO, 2022) Early Career Travel Award (EERA, 2022) International Ford Fellowship (2009-2011) Projects : Principal investigator for Academic Resilience in Science and Mathematics (ARISE) and Teachers’ Effect on Student Outcome (TESO) , both funded by Norwegian research bodies. Leads the Large-scale Educational Assessment (LEA) research group.