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.
Johannes Skaar is a Professor at the Department of Physics, University of Oslo (UiO). He holds a 100% position there since 2017, previously at NTNU. His research focuses on quantum field theory, quantum optics, electromagnetics, metamaterials, photonics, and quantum information. He teaches advanced courses such as FYS4170 Relativistic Quantum Field Theory and FYS1005 Classical Mechanics. His work spans theoretical physics with notable contributions to single-photon states, metamaterial properties, and quantum cryptography security. Skaar’s research integrates foundational physics with applied technologies like metamaterials and quantum communication systems. His studies on Fresnel equations and magnetic permeability have advanced electromagnetic theory. He frequently publishes in top journals like Physical Review A and Physical Review Letters . Research groups: Theoretical Physics at UiO.
Arnoldo Frigessi is Professor of Statistics at the University of Oslo, where he leads the Oslo Center for Biostatistics and Epidemiology and serves as director of BigInsight—a Centre of Excellence for Research-Based Innovation. This consortium unites industry, business, public actors, and academia to develop model-based machine learning methodologies for big data, with strong emphasis on health applications. His research centers on statistical methodology driven by real-world scientific challenges, specializing in stochastic models for complex dependence structures and computationally intensive inference algorithms. Core application domains include: Genomics and personalized cancer therapy (particularly breast and lung cancer) Infectious disease modeling (including pandemic response) eHealth, sensor data analysis, and recommender systems Personalized marketing and viral diffusion dynamics Analysis of his 15 most recent publications (2024-2025) reveals dominant themes in cancer systems biology , where he integrates multi-omics, single-cell transcriptomics, and computational modeling to decode tumor evolution under therapy. Parallel work advances infectious disease epidemiology through time-varying reproduction number estimation and mobility-based transmission modeling, while methodological innovations span synthetic data generation (TVineSynth), causal inference via target trial emulation, and Bayesian ranking models for recommender systems. Scientific Awards: No specific awards mentioned in source materials Frigessi actively supervises graduate students, including a Department of Informatics project on "Utilizing covariate information in recommender systems." His leadership of BigInsight—funded as a Research-Based Innovation Centre by the Research Council of Norway—secures major grants supporting interdisciplinary collaborations with industrial partners (e.g., Telenor, DNB) and public health institutions. Current projects integrate real-world clinical data with mechanistic models for treatment optimization. He directs BigInsight's multidisciplinary team of statisticians, computer scientists, and domain experts, while leading the Oslo Center for Biostatistics and Epidemiology's efforts in developing statistical frameworks for complex health data. These initiatives drive Norway's national strategy for data-driven health innovation.
Yulia Rodina is a Professor of Linguistics and Language Acquisition at UiT The Arctic University of Norway, affiliated with the Department of Language and Culture. She leads the AcqVA-Nor research group and directs the Flere språk til flere initiative. As Deputy Chair of the HSL Faculty Research Ethics Committee, she oversees ethical standards in research. Her research focuses on multilingualism, second/third language acquisition, and language attrition using experimental psycholinguistic methods. Key themes include grammatical gender, morphosyntax, and cross-linguistic influence in languages like Russian, Norwegian, Bosnian, and Serbian. She investigates heritage language development in children across Norway, Germany, and the UK, and explores language variation and change in Norwegian dialects. Rodina teaches courses in Second Language Acquisition, Multilingualism, and Varieties of English. She supervises MA theses on topics such as gaming's role in vocabulary acquisition and syntactic development in bilingual children. Her work is supported by projects funded through the Arctic MSCA, RCN, and EU initiatives like AcqVA Aurora and MultiGender. She collaborates with international teams on studies like the MAP project (Heritage Russian in Spain) and CLIMAD (cross-linguistic influence in multilingualism). Her labs include AcqVA, PoLaR, and C-LaBL, focusing on acquisition, variation, and attrition processes.
Anders Skrondal is a Professor II at the University of Oslo's Faculty of Educational Sciences, affiliated with the Centre for Educational Measurement (CEMO). He also serves as a Senior Scientist at CEFH (Research Council of Norway Centre of Excellence) at the Norwegian Institute of Public Health and Co-Principal Investigator at CREATE, another Norwegian Centre of Excellence. His academic journey includes roles as Head of the Biostatistics Group at the Norwegian Institute of Public Health and Professor of Statistics at the London School of Economics (LSE), where he directed the Methodology Institute. Skrondal's research focuses on psychometrics, statistics, biostatistics, and econometrics, with a major contribution being the development of the GLLAMM framework. He has authored 14 books and over 200 peer-reviewed papers, achieving an h-index of 63 and 30,000+ citations. His awards include the 1997 Psychometric Society Dissertation Prize and leadership roles in prestigious organizations like the Psychometric Society and Royal Statistical Society. Research Interests: Skrondal specializes in statistical methodologies including latent variable modeling, multilevel modeling, and missing data analysis. His work bridges theoretical advancements and practical applications in medicine, psychology, and social sciences. He is renowned for integrating latent variable and mixed model frameworks to address complex data structures. Recent trends in his publications emphasize methodological solutions for missing data, non-ignorable mechanisms, and psychometric model validation. His articles span statistical theory, medical applications, and educational measurement. Awards: President, Psychometric Society (2016–2017) Elected Member, International Statistical Institute Outstanding Academic Title for 'The Cambridge Dictionary of Statistics' (2011) Fulbright Professor at UC Berkeley (2013–2014) Advising & Grants: Skrondal has led major research initiatives such as CEFH and CREATE, funded by the Research Council of Norway. While no specific advisee list is provided, his collaborations span international institutions. His work on GLLAMM software is used in over 750 journals, reflecting widespread academic impact. Labs/Teams: Active in CEMO and CEFH, he contributes to interdisciplinary teams advancing educational measurement and public health research. His involvement in CREATE focuses on equality in education through statistical innovations.
Marianne Fyhn is a Professor at the University of Oslo's Section of Physiology and Cell Biology within the Faculty of Mathematics and Natural Sciences. She leads the Centre for Integrative Neuroplasticity (CINPLA), a strategic research initiative integrating experimental biology with computational physics/mathematics to study brain information processing and plasticity. PhD in Neuroscience (NTNU, 2000-2005) MSc in Physiology (University of Tromsø, 1997-1999) Bachelor in Arctic Biology (University Courses in Svalbard, 1995-1996) BSc in Biology (University of Bergen/Oslo, 1992-1995) Her research focuses on neural plasticity mechanisms in cortical structures, using large-scale neuronal recordings and transcranial two-photon microscopy to study synaptic and population code changes during sensory learning. Key findings include discovering grid cells in mice and demonstrating how experience modifies cortical circuits for long-term memory. Recent publications analyze perineuronal nets' role in memory stabilization, grid cell conformal mapping, and topological population coding in visual cortex. These works reveal intersections between neural circuit dynamics , computational modeling , and neurodegenerative processes . 2008: European Brain and Behaviour Society Award 2007: Eppendorf-Science Prize in Neurobiology 2006: Donald B. Lindsley Prize & I.K. Lykke Award Fyhn serves as course manager for advanced courses including MBV1020 - Physiology and MBV4340 - Advanced Neurobiology . Her lab develops educational tools like Neuronify for neural circuit simulation and open-source platforms for electrophysiological data analysis.
Prof. Dr. Pia Pinger is a Full Professor of Economics at the University of Cologne, specifically within the Faculty of Management, Economics and Social Sciences (WiSo-Fakultät) and the Department of Economics. She has held this position since 2019 and serves as a Cluster Faculty Member and Speaker of the excellence cluster ECONtribute - Markets & Public Policy. Dr. Pinger is also a Principal Investigator in the Center for Social and Economic Behavior (C-SEB) and the Collaborative Research Center Economic Perspectives on Societal Challenges. Her institutional affiliations include IZA Research Fellow and CESifo Affiliate status. Dr. Pinger earned her PhD from the University of Mannheim in 2013, following research positions at the University of Mannheim and the Centre for European Economic Research (ZEW). Prior to her current role, she served as an Assistant Professor at the University of Bonn (2013-2019) and completed visiting scholar appointments at the University of Chicago. Her research program focuses on human capital and socioeconomic inequalities, with particular emphasis on educational decision-making, early childhood health, applied microeconometrics, and behavioral economics. Dr. Pinger investigates how socioeconomic background influences educational and labor market outcomes through rigorous empirical analysis using natural experiments and causal inference methods. Her work examines gender wage gaps, decision-making at critical life junctures, and the formation of economic preferences from childhood, providing insights into inequality mechanisms and potential policy interventions. Dr. Pinger's publication record reveals a consistent focus on socioeconomic inequality across the life course, with recent work examining gender differences in wage expectations (2024), socioeconomic status effects on children's cognitive development (2021), and causal evidence on prosocial behavior formation (2020). Her research spans labor economics, health economics, and behavioral economics, often connecting micro-level decision-making to broader societal outcomes through sophisticated econometric techniques. ERC Starting Grant (2023) - €1.5 million for OPPORTUNITY project Prize for best dissertation in education economics (2015) Karin Islinger dissertation award (2014) Dissertation prize 'The Future of Labor' (2014) HCEO Emerging Scholar designation (2014) Elected to 'Ausschuss für Bildungsökonomie' (2017) As a Principal Investigator in multiple research centers, Dr. Pinger leads collaborative projects examining economic behavior and policy. Her ERC-funded OPPORTUNITY project represents a major research initiative analyzing how ability signals like grades influence educational trajectories based on socioeconomic background. She actively contributes to academic discourse through media appearances in Süddeutsche Zeitung, DER SPIEGEL, and multiple podcasts addressing educational inequality and economic decision-making. Recent press releases indicate ECONtribute's funding extension through 2025, highlighting the ongoing significance of her research environment.
Daniel Løke is a PhD candidate and doctoral research fellow at Sunnaas Rehabilitation Hospital, collaborating with the University of Oslo. His research focuses on fatigue and rehabilitation psychology following traumatic brain injury (TBI), utilizing biopsychosocial approaches. He holds a Cand.psychol. from the University of Oslo and has clinical experience at Sunnaas since 2015. His doctoral project investigates fatigue mechanisms post-TBI, emphasizing biological, psychological, and psychosocial factors. Collaborations include Oslo University Hospital, Monash University, and the Norwegian user organization Personskadeforbundet LTN, funded by Stiftelsen Dam. Research interests span neuropsychology, health psychology, and rehabilitation, with a focus on subjective health complaints like fatigue and pain. Key studies explore personality traits’ impact on post-TBI outcomes and longitudinal brain maturation in youth exposed to adversity. Publications highlight fatigue epidemiology, brain imaging correlates of psychopathology, and socioeconomic influences on neurodevelopment. Supervised by Marianne Løvstad and others, his work bridges clinical practice and evidence-based interventions for chronic symptom management.
Andreas Fagereng is a Professor of Finance at BI Norwegian Business School and a Senior Researcher at Statistics Norway. He serves as Co-director of the Centre for Household Finance and Macroeconomic Research (HOFIMAR) and is a member of the Research Policy Network on Household Finance at the Center for Economic Policy Research (CEPR). His academic career spans prestigious institutions including Statistics Norway, Norges Bank, and the European University Institute. Dr. Fagereng earned his PhD in Economics from the European University Institute in 2012 and his MSc in Economics from the University of Oslo in 2007. His research focuses on household finance and macroeconomics, particularly examining wealth inequality, consumption behavior, and the relationship between household financial positions and economic outcomes. His work frequently utilizes detailed Norwegian administrative data to investigate asset allocation patterns, investor behavior, and household responses to economic shocks. His extensive publication record reveals consistent themes in analyzing how households respond to income fluctuations, the heterogeneity in returns to wealth across different population segments, and the intergenerational transmission of economic advantage. Dr. Fagereng's research employs sophisticated methodologies including natural experiments, panel data analysis, and structural modeling to address fundamental questions in household finance. Among his notable recognitions is the prestigious ERC Starting Grant for his project 'Inequality in 3D – Measurement and Implications for Macroeconomic Theory (3D-In-Macro)' (2020-2025), which supports his innovative work at the intersection of micro-level household data and macroeconomic theory. Dr. Fagereng's research has significant policy implications for understanding wealth distribution dynamics, designing effective economic stabilization policies, and improving macroeconomic models that incorporate household heterogeneity. His collaborations with leading researchers worldwide have positioned him at the forefront of the growing field examining the connections between household financial decisions and broader economic outcomes.
Tom Luk R Michoel is a Professor at the Computational Biology Unit within the Department of Informatics at the University of Bergen. His research focuses on bioinformatics, computational biology, and machine learning applied to understanding gene regulation and causal relationships in biological systems. He teaches in both the Bachelor and Master programs in Informatics, including courses like BINF301 and MNF130. Research Interests: Michoel’s work explores how genetic variation influences gene expression and disease mechanisms. He develops machine learning algorithms to infer causal gene regulatory networks from large-scale genomic data, emphasizing causal inference over mere correlations. His recent projects include analyzing plasma protein networks linked to cardiovascular disease and applying Bayesian networks to understand gene-disease associations. Publications: His most recent work (2025) focuses on causal protein networks in myocardial infarction risk and network-driven frameworks for coronary artery disease studies. He also contributes to methodological advancements like integrating graph neural networks with metabolic models. Current Activities: Michoel leads the development of tools like Findr.jl for network inference and teaches short courses on causal inference in drug discovery. His lab collaborates on projects involving single-cell analysis, multi-tissue genomics, and systems pharmacology.
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.
Trude Nilsen is a Research Professor at the University of Oslo's Department of Teacher Education and School Research and a leader in the CREATE Centre for Research on Equality in Education. She specializes in educational inequalities, quantitative methods, and international large-scale assessments like TIMSS, PISA, and TALIS. Her work focuses on analyzing teaching quality, teacher competence, and school climate to improve educational equity and outcomes. Education: PhD in Science Education and International Large-Scale Assessment, MSc in Astrophysics, and studies in pedagogy and computer programming. Research Highlights: Leads projects such as TESO (Teachers' Effect on Student Outcomes) and contributes to global expert groups for TIMSS, TALIS, and TALIS Starting Strong. Her studies have explored factors influencing mathematics performance, sleep/nutrition impacts on academic achievement, and systemic approaches to educational equity. She combines structural equation modeling and multilevel analyses to address causal relationships in education. Grants & Projects: Norwegian Research Council-funded CREATE (2023–), TESO (2018–2024), and multiple international collaborations. Supervises PhD/master students and teaches advanced quantitative methods courses. Key Contributions: Over 50 peer-reviewed articles and book chapters, including analyses of Nordic education systems' equity trends and validation of teaching quality instruments. Her work bridges policy implications with rigorous data-driven insights.
Esten Høyland Leonardsen is a Postdoctoral Fellow in the Department of Psychology at the University of Oslo, specializing in cognitive and clinical neuroscience. His academic background includes a BSc and MSc in Informatics (Programming and Networks) from UiO (2014 and 2016, respectively). His research focuses on applying artificial intelligence, machine learning, and neuroimaging techniques to study brain aging, Alzheimer’s disease, and psychiatric disorders. He collaborates with the Center for Lifespan Changes in Brain and Cognition. Education: MSc in Informatics (Programming and Networks), University of Oslo (2016) BSc in Informatics (Programming and Networks), University of Oslo (2014) Research Interests: Esten’s work bridges AI, neuroscience, and clinical applications. Key areas include machine learning for neuroimaging analysis, brain age prediction, Alzheimer’s genetics, and the intersection of computational methods with mental health. His recent studies emphasize explainable AI in dementia diagnosis and the role of immune dysfunction in psychiatric disorders. Publications: Recent work highlights trends in AI-driven neuroimaging, genetic influences on brain aging, and translational research linking computational models to clinical outcomes. For example, he has explored vaccine hesitancy via mixed methods and applied small CNNs for Alzheimer’s classification. Awards: No scientific awards explicitly mentioned in the text. Advising/Grants: Collaborates on projects involving brain imaging, genetics, and computational modeling. No specific grants or advisees listed. Labs/Teams: Active in the Center for Lifespan Changes in Brain and Cognition, leveraging interdisciplinary approaches to study neurodevelopment and aging.
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.
Åshild Marit Bjørnrem is an Associate Professor in the Department of Clinical Medicine at UiT The Arctic University of Norway. She is actively engaged in research focusing on osteoporosis, bone structure, and fracture risk, with particular emphasis on hormonal influences and population-specific risk factors. Her work spans clinical studies, epidemiological research, and translational applications. Dr. Bjørnrem's research interests include: Sex hormones, osteoporosis, and fracture risk Effects of reproductive factors such as parity and breastfeeding on fracture risk Changes in bone structure and cortical porosity during breastfeeding and menopause Relationship between cortical porosity and fracture risk Significance of growth for the development of osteoporosis Her extensive publication record demonstrates a consistent focus on bone health, fracture prevention, and osteoporosis management. Recent work has explored fracture liaison services, ethnic differences in fracture risk, bone microarchitecture, and the validation of fracture prediction models. Her research often employs innovative methodologies including twin studies to distinguish genetic from environmental influences on bone health. Dr. Bjørnrem is actively involved in the Norwegian Capture the Fracture initiative (NoFRACT), contributing to secondary fracture prevention programs in Norway. Her collaborative work spans multiple institutions and involves interdisciplinary teams focusing on improving outcomes for patients with osteoporosis and fracture risk.
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