Norwegian University of Science and TechnologyNorway
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.
Norwegian University of Science and TechnologyNorway
Pierluigi Salvo Rossi is a Professor at the Department of Electronic Systems , Norwegian University of Science and Technology ( NTNU ), with additional roles as Deputy Head of Department (since 2021) and Deputy Manager at the Center for Green Shift in the Built Environment (since 2022). He also serves as a part-time Research Scientist at SINTEF Energy's Gas Technology department. Education: Ph.D. in Computer Engineering, University of Naples “Federico II”, Italy (2005) Dr.Eng. (cum laude) in Telecommunications Engineering, University of Naples “Federico II”, Italy (2002) Research Interests span Wireless Communications , Digital Twins , Machine Learning , and Statistical Signal Processing , focusing on applications like Industrial IoT , Fault Detection , and Energy Systems . His recent Publications highlight trends in Federated Learning , Graph Signal Processing , and Multi-Sensor Anomaly Detection across domains from Natural Gas Pipelines to Subsea Leakages . Scientific Awards include: Exemplary Senior Editor, IEEE Communications Letters (2018) Department Ambassador, NTNU (2016) IEEE Senior Member (since 2011) Professional Roles encompass editorial leadership (e.g., IEEE Sensors Journal) and conference organization (e.g., General Chair for IEEE Sensor Array and Multichannel Signal Processing Workshop, 2022). He leads major funded research projects like PREFERENCE (RCN, 2023-2027) and AUTOSHIP (RCN, 2020-2028).
Jon Olav Vik is a Professor at the Norwegian University of Life Sciences (NMBU), affiliated with the Department of Mathematical Sciences and Technology within the Faculty of Science and Technology. He leads the DigiSal project—"Towards the Digital Salmon: From a reactive to a pre-emptive research strategy in aquaculture"—funded under the Research Council of Norway’s Digital Life initiative. He is also a lead modeller in the GenoSysFat project, which aims to enhance omega-3 content in farmed salmon through integrated genomics and systems biology approaches. His research spans systems biology , computational physiology , genotype-phenotype modeling , and ecological dynamics . He works at the intersection of biology, mathematics, and computer programming, developing models to understand how genetics, nutrition, and environment interact in fish and ecological systems. His pedagogical focus includes biostatistics and programming in R, and he teaches courses such as STIN100, STIN300, and STAT100. The 15 most recent publications reflect a consistent focus on systems-level understanding in biology, particularly in salmon aquaculture, metabolic regulation, and genotype-phenotype relationships. These works appear in high-impact journals like Nature , Science , PLOS Computational Biology , and Journal of The Royal Society Interface , demonstrating interdisciplinary reach across computational biology, genomics, ecology, and biostatistics. Key themes include metabolic modeling, microbiome stability, lipidome remodeling, and sensitivity analysis in dynamic models. Jon Olav Vik has contributed to major collaborative efforts including the Infrastructure for Systems Biology Europe (ISBE) , where he helped develop frameworks for "modelling as a service." He has also authored book chapters and technical deliverables on systems biology and modeling practices. He actively supervises students and invites master’s thesis candidates with interests in quantitative biology. While no specific awards are listed, his leadership in national and international research projects underscores his scientific impact. His work supports both fundamental science and sustainable aquaculture innovation.
Bjørge Hermann Hansen is a postdoctoral researcher at the Department of Sport Medicine, Norwegian School of Sport Sciences (NIH), actively engaged in national physical activity surveillance and the International Children's Accelerometer Database (ICAD). His work bridges epidemiology, statistics, and device-based measurement to investigate activity patterns across diverse populations including children, cancer survivors, and clinical cohorts like Duchenne Muscular Dystrophy patients. Previously referenced in 2020-2023 materials as a University of Agder professor, NIH's December 2023 employee page confirms his current research role at Norway's premier sport science institution. Hansen's research focuses on physical activity epidemiology and measurement validity, specializing in accelerometer data analysis. He investigates intensity distribution, sedentary behavior, and their health associations across lifespan stages. His methodological expertise includes harmonizing multi-cohort data, validating self-report tools against objective measures, and developing protocols for reliable activity monitoring. Current projects address environmental determinants like family car ownership and school policies on screen time. Recent publications reveal a dominant trend: large-scale meta-analyses using pooled international data (ICAD) to establish dose-response relationships between activity patterns and mortality. He also examines secular trends in Norwegian youth activity, methodological challenges in measurement, and the impact of clinical conditions on movement behaviors. Key journals include The Lancet, British Journal of Sports Medicine, and PLOS ONE. Scientific awards: None explicitly mentioned in source materials. Hansen teaches Statistics, Epidemiology, and Scientific Methods at NIH, translating complex concepts through podcasts like Fysioformidlingen. While no specific grants or student advising records are provided, his leadership in national surveillance projects (e.g., Kan3 reports for Folkehelseinstituttet) indicates active grant-funded research. He advocates for evidence-based policies such as school mobile phone bans to increase recess activity. He collaborates with the ICAD consortium and Norwegian Institute of Public Health on population monitoring. His work with the Kan3 project involves nationwide data collection on adult physical activity and fitness. Recent Folkehelseinstituttet reports (2023-2024) demonstrate his role in national health surveillance systems.
Inland Norway University of Applied SciencesNorway
Daumantas Bloznelis is an Associate Professor of Business Analytics at the Norwegian University of Life Sciences (Ås, Norway) and an Adjunct Associate Professor at the University of Inland Norway (Rena, Norway). He holds a PhD in Economics from the Norwegian University of Life Sciences, with visiting scholar experience at Cornell University (USA). His research focuses on financial econometrics, commodity markets, and statistical price modeling, with particular emphasis on risk management and forecasting in aquaculture sectors. Bloznelis has extensive experience in academia, including teaching courses on machine learning, econometrics, and quantitative methods across multiple universities. He has supervised numerous PhD and Master’s theses, contributing to the development of future scholars in finance and management. His work also extends to applied research, such as cross-hedging carbon risk and portfolio optimization in electric vehicle sectors. Bloznelis has received several accolades, including scholarships from the Norwegian Research Council and Vilnius University, and awards for academic excellence in Lithuania. Education: PhD in Economics/Finance (2011–2016), Norwegian University of Life Sciences MSc in Statistics/Econometrics (2009–2011), Vilnius University BSc in Statistics/Econometrics (2005–2009), Vilnius University Research Interests: Bloznelis specializes in statistical price modeling, forecasting methodologies, and risk management in financial and commodity markets. His work integrates machine learning and econometric techniques to address practical challenges in sectors like salmon farming and electric vehicles. He also explores the application of copula models and factor analysis to portfolio optimization and market dynamics. Key Awards: 3rd prize in International Econometric Team Competition (2010) PRESIDENT OF LITHUANIA AWARD for dictation contest (2007) Prime Minister of Lithuania Award for matriculation excellence (2005) Professional Contributions: Bloznelis has presented at over 30 international conferences, including NCCC commodity price analysis meetings and CEMA annual conferences. He serves on the Board of Advisors for Vilnius University’s Faculty of Mathematics and Informatics. His research outputs include influential papers on futures market biases, hedging strategies, and factor models in commodity pricing.
Tobias Kaufmann is a Full Professor of Neurotechnology and Computational Psychiatry at the University of Tübingen, Germany, and a Senior Researcher at the Norwegian Centre for Mental Disorders Research (NORMENT) at the University of Oslo, Norway. His research focuses on investigating the pathophysiological changes in brain structure and function, particularly exploring their genetic underpinnings through computational analysis of large-scale neuroimaging and genetic datasets. He has contributed to understanding the genetic architecture of brain regions like the thalamus and brainstem, their roles in psychiatric and neurological disorders, and the development of neuroimaging tools such as ARTiiFACT for artifact processing. His work bridges neurotechnology, computational methods, and clinical psychiatry to advance precision medicine approaches in mental health. His research interests include neuroimaging genetics, brain aging, and the application of machine learning to neuroimaging data. He has developed software tools for analyzing brain connectivity and functional networks, with a focus on schizophrenia, Alzheimer’s disease, and other psychiatric disorders. Kaufmann is also involved in collaborative initiatives like the ECNP NeuroImaging Network to promote open science and data-sharing in mental health research. His lab at NORMENT focuses on integrating multimodal data (e.g., MRI, genetics) to study brain disorders, while his role at the University of Tübingen emphasizes advancing neurotechnological methods. He has no listed awards but has published extensively in top journals like Nature Neuroscience and NeuroImage, with a strong emphasis on computational psychiatry and neuroimaging methodologies.
Ingrid Hobæk Haff is an Associate Professor in insurance mathematics and statistics at the Department of Mathematics, University of Oslo since 2015. She holds a master's degree in Industrial Mathematics from NTNU (2002) and a PhD from the Statistics for Innovation center (2008–2012), with a 20% position as a research scientist at the Norwegian Computing Centre. Previously, she worked there as a research scientist and senior scientist. Her research interests focus on multivariate statistics, copulae, skew and heavy-tailed distributions, and applications in insurance and finance. She has contributed to advancements in statistical modeling, particularly in copula constructions and their applications to risk assessment and extreme value analysis. Key awards include the Sverdrup award for young scientists and the mathematical award of Hanna og John Olav Stubban . She is affiliated with the Statistics and Data Science research group and the completed Stochastics of Renewable Energy Markets (STORE) project. Her work spans interdisciplinary collaborations, including applications in immunology and bioinformatics, leveraging machine learning for antibody-antigen interaction studies and synthetic data generation.
Jelena Veletic is a Postdoctoral Fellow at the Institute for Educational Research within the Faculty of Educational Sciences at the University of Oslo, specializing in large-scale educational assessment and school leadership analysis using international datasets. Her educational qualifications include: PhD in Educational Measurement from the Centre for Educational Measurement (CEMO), University of Oslo (2023) with thesis "Challenges and Opportunities in Measuring School Leadership. An analysis of data from the Teaching and Learning International Survey (TALIS)" Master of Science in Psychology from the Department of Psychology, University of Banja Luka, Bosnia and Herzegovina (2012) Veletic's research focuses on the intersection of school leadership practices, organizational climate, and teacher well-being, employing advanced statistical methodologies including multilevel modeling and structural equation modeling. Her work examines how leadership styles influence school environments across diverse cultural contexts, particularly through analysis of TIMSS and TALIS datasets. She contributes significantly to measurement model development in educational leadership research. Her publication record reveals a concentrated trajectory in international comparative leadership studies, with increasing emphasis on Nordic educational systems and methodological innovation in cluster analysis for leadership profiling. Recent work demonstrates sophisticated applications of multilevel SEM to unpack complex relationships between distributed leadership and teacher satisfaction. Scientific recognition includes: Marie Skłodowska-Curie Fellowship under EU Horizon 2020 Veletic actively participates in EU-funded research initiatives including the OCCAM project, collaborating with international scholars on secondary analysis of TALIS data. Her work involves substantial grant-funded research in educational measurement and cross-national leadership studies. She maintains active membership in the EKVA and Large-scale Educational Assessment (LEA) research groups at the Institute for Educational Research, contributing to methodological advancements in international educational surveys and leadership assessment frameworks.
Giulia Di Nunno is a Professor in the Department of Mathematics at the University of Oslo, specializing in stochastic analysis and its applications to finance and risk management. She also holds an adjunct professorship at the Norwegian School of Economics (NHH). Her research focuses on stochastic calculus, control theory, financial modeling, and energy finance, with a particular interest in dynamic risk measures. She has led major projects like the STORM initiative on time-space risk models and is involved in interdisciplinary research on sustainability and energy markets. Di Nunno has served as President of the Scientific Council of CIMPA and is an associate editor for several prestigious journals, including Finance and Stochastics and Stochastics . Her work bridges theoretical advancements with practical applications in finance and energy sectors. Education: PhD in Mathematical Statistics (University of Pavia, 2003), Degree in Mathematics (University of Milan, 1998). Research Groups: Risk and Stochastics, STORE (completed). Key Projects: SURE-AI (AI-driven risk modeling), Unruly Sustainability (interdisciplinary research), STORM (ToppForsk project). Editorial Roles: Associate Editor for Finance and Stochastics , DEAF , FMF , and others. Her publications emphasize stochastic processes, volatility modeling, and risk measurement, with recent contributions on time-changed dynamics and applications to energy finance. She actively contributes to the international academic community through research networks like AMaMeF and ModSimFIE.
Johan Braeken is a Professor at the University of Oslo's Centre for Educational Measurement (CEMO). He holds expertise in psychometric modeling, particularly in modern test design, including computerized adaptive testing (CAT) and item response theory (IRT). His research focuses on improving assessment methodologies in education and large-scale evaluations. Education: PhD in Psychology (Psychometrics) from K.U.Leuven, Belgium (2008). Previous roles include Associate Professor (2014–2017) and Assistant Professor positions at Wageningen University and Tilburg University in the Netherlands. He has also worked as a psychometrician at CITO (2008–2009). Research Interests: Development and application of latent variable models, CAT optimization, and evaluation of international educational assessments. He explores statistical methods for improving measurement precision and addressing model violations. Software Contributions: Creator of the 'Empirical Kaiser Criterion' app for factor analysis, 'Fixed-precision MCAT selection rules' for R's mirtCAT package, and an Item Characteristic Curve visualization tool. Labs/Groups: Active in the CREATE and FREMO research groups, focusing on educational measurement and frontier research in psychometrics.
Manuela Zucknick is Professor and Director of the Oslo Centre for Biostatistics and Epidemiology at the University of Oslo's Faculty of Medicine, Department of Biostatistics. Her research integrates statistical learning with translational cancer research to advance personalized medicine through multi-omics data integration. PhD Biostatistics, Imperial College London (2008) MSc Bioinformatics, Imperial College London (2004) Diplom Statistik, University of Dortmund (2003) Her research focuses on Bayesian methods for integrating heterogeneous data sources in cancer research, particularly for drug response prediction in pharmacogenomic screens and patient prognosis. She develops structured high-dimensional regression models for 'large p, small n' problems in molecular medicine, with emphasis on incorporating prior biological knowledge into risk prediction frameworks. Her work bridges statistical methodology with clinical applications in personalized cancer therapies. Her recent publications demonstrate consistent contributions to multi-omics integration and survival modeling across diverse clinical contexts including cancer, pregnancy complications, and rheumatoid arthritis. The research shows strong methodological innovation in handling high-dimensional biological data while maintaining clinical relevance. Through the Oslo Centre for Biostatistics and Epidemiology, she leads collaborative projects spanning oncology, obstetrics, and rheumatology. Her work frequently involves designing statistical frameworks for pharmacogenomic screens and developing tools for biomarker discovery in complex disease settings.
Zhi Zhao is a Researcher at the University of Oslo's Faculty of Medicine, affiliated with the department of Statistical learning in molecular medicine. His work focuses on statistical and computational methods applied to molecular medicine, including omics data analysis, pharmacogenomics, and cancer genomics. Research Interests Statistical learning for multi-omics data integration Bayesian variable selection in drug sensitivity studies Biomarker discovery for immune-related diseases Survival analysis modeling in clinical applications Key Contributions Developed R packages like BayesSUR for multivariate Bayesian regression and EnrichIntersect for set enrichment analysis. His recent work includes studies on immune checkpoint inhibitor toxicity biomarkers and pan-cancer drug response prediction. Awards No scientific awards explicitly mentioned in the provided text. Labs/Teams Core member of the Statistical learning in molecular medicine research group.
Maria Dung Cao is an Associate Professor at the Department of Nursing, Health and laboratory science at Oslo Metropolitan University (HioF). Her research focuses on medical biochemistry, molecular diagnostics, and cancer research, particularly breast cancer metabolism and biomarker discovery. She completed her PhD in Molecular Medicine (2012) at NTNU, Norway, with a thesis on MR metabolic characterization of breast cancer treatment effects. Her educational background includes a Master’s in Molecular Medicine (NTNU, 2008) and a Bachelor’s in Biomedical Science (HiØ, 2004). She has conducted research stays at Johns Hopkins University (USA) and Huddersfield University (UK). Key research interests include: Breast cancer metabolomics and metabolic profiling Choline phospholipid metabolism pathways Magnetic Resonance Spectroscopy (MRS) applications Epigenetic biomarkers in oncology Therapeutic target identification in cancer Recent work emphasizes metabolic response prediction to neoadjuvant chemotherapy and biomarker validation for treatment outcomes. Her publications highlight collaborations in international oncology research and translational metabolomics. No scientific awards are listed, but her extensive publication record demonstrates impactful contributions to cancer metabolomics and diagnostic biomarker research.
Norwegian University of Science And TechnologyNorway
Robert Brian O'Hara is a Professor in the Department of Mathematical Sciences at NTNU. His research focuses on the intersection of ecology and statistics, particularly developing models to analyze species distributions and dynamics. He leads a research group addressing challenges in biodiversity monitoring, including citizen science data integration and statistical tool development. Current projects include the GreenPlan initiative for land-use impact modeling and the Transforming Citizen Science for Biodiversity project. His work emphasizes integrating diverse data sources (e.g., observational, experimental, citizen science) to improve model accuracy. Notable contributions include the PointedSDMs R package for species distribution modeling and collaborations on projects like the gllvm package for model-based ordination. He supervises PhD students Kwaku Peprah Adjei, Philip Stanley Mostert, and Ron Tuganov, whose research spans data integration, statistical tools, and ecological modeling. Key themes in his publications include niche overlap prediction, climate-driven ecosystem shifts, and methodological advancements in ecological statistics. His research aims to bridge gaps between statistical rigor and ecological complexity to inform conservation and policy decisions.
Norwegian University of Science and TechnologyNorway
Salvo Rossi is a Full Professor of Statistical Machine Learning for Signal Processing at the Department of Electronic Systems, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU). He holds leadership roles as Deputy Director for Research and Deputy Manager of the Centre for Green Shift in the Built Environment. Additionally, he serves as a part-time Senior Research Scientist at SINTEF Energy Research. His work bridges academia and industry, with significant contributions to signal processing, machine learning, and IoT systems. Research Interests: Statistical Machine Learning Data Fusion in Wireless Sensor Networks Digital Twins and Industrial IoT Anomaly Detection and Fault Diagnosis Federated and Distributed Learning Graph Signal Processing His recent publications reflect a strong trend toward intelligent, distributed systems for industrial monitoring, particularly in energy and safety-critical environments. The integration of machine learning with signal processing for decision fusion in sensor networks is a central theme. Scientific Awards and Recognition: Exemplary Senior Editor, IEEE Communications Letters (2018) Department Ambassador, NTNU (2016) IEEE Senior Member (since 2011) Editorial and Professional Service: Senior Area Editor, IEEE Transactions on Signal and Information Processing over Networks (since 2025) Topical Editor, IEEE Sensors Journal (since 2022) Area Editor, IEEE Open Journal of the Communications Society (2019–2023) Executive Editor, IEEE Communications Letters (2019–2021) General Chair, IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), Trondheim, 2022 He has advised numerous PhD and master’s students (not explicitly listed), led major research projects in IoT and green energy, and contributed to national and international research initiatives. His lab, SPIN (Signal Processing Group), focuses on intelligent signal processing for real-world applications in smart environments and Industry 4.0.
