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
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.
Dieu Tien Bui is a Full Professor in the Department of Business and IT at the University of South-Eastern Norway (USN) School of Business. His research focuses on Geospatial Artificial Intelligence Machine Learning GIS and Remote Sensing Natural Hazard Modeling Environmental Problems (landslides, floods, soil salinity, biomass) . He has contributed to over 15 recent publications in journals like Science of the Total Environment , Remote Sensing , and Geomorphology , emphasizing hybrid AI models for landslide and flood susceptibility. His work spans Vietnam, India, China, and Iran with applications in climate change adaptation and disaster management. Scientific Awards: Global Highly Cited Researcher PhD Supervision: He has supervised 8 PhD students at institutions including USN, NTNU, and Vietnamese universities.
Nicola Paltrinieri is a Professor of Risk Assessment at the Department of Mechanical and Industrial Engineering, NTNU (Norway), and an Adjunct Professor at the University of Bologna (Italy). His expertise spans risk assessment, hydrogen technologies, process safety, and data-driven safety management. He holds Chartered Engineer and Chartered Scientist certifications and has served on editorial boards for journals like Safety Science and Journal of Risk Research . Education: PhD in Environmental, Safety and Chemical Engineering (University of Bologna, 2012) Master’s in Chemical and Process Engineering (University of Bologna, 2008) Research Interests: Focuses on hydrogen infrastructure safety, Natech accident analysis, risk-based inspection strategies, and AI integration in safety systems. His work emphasizes sustainable energy transitions and mitigating risks in emerging technologies like hydrogen. Key Projects (2022-2026): H2Glass : Decarbonizing glass and aluminum sectors via hydrogen HyInHeat : Hydrogen technologies for industrial heating HYDROGENi : Norwegian research center for hydrogen/ammonia Awards: Onsager Fellowship (2016–2021) Frank Lees Medal (2012) for safety-related publications Grants & Leadership: Head of NTNU Energy Team Hydrogen, coordinator for EU-funded projects like SUSHy , and active in international risk committees (e.g., EFCE, ESRA). His work bridges academia and industry, with over 8 PhD examinations supervised. Labs/Teams: Leads the NTNU Energy Team Hydrogen and collaborates on initiatives like SH2IFT-2 for safe hydrogen fuel handling. His research group focuses on AI-driven risk analysis and hydrogen infrastructure resilience.
Sebastien Nicolas Gros is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). His research focuses on safe reinforcement learning (RL) and data-driven model predictive control (MPC), with applications in energy systems, biomedical engineering, and autonomous vehicles. Institution: Norwegian University of Science and Technology Department: Engineering Cybernetics His work emphasizes AI-driven optimization for domestic energy storage, battery integration, and smart building management. Collaborations include Equinor, DNV, Kongsberg, Volvo, and CorPower Ocean. Key themes in his publications include: Control theory for renewable energy systems (wave energy converters, buildings) Biomedical applications (artificial pancreas, glucose monitoring) Transportation systems (electric vehicles, autonomous ships) Machine learning integration with physical models He supervises 6 PhD students and co-supervises projects on multi-rotor wind turbines and industrial PhD collaborations. The articles demonstrate a convergence of RL, MPC, and uncertainty quantification across energy, biomedical, and transportation domains.
Alvaro Köhn-Luque is an Associate Professor at the Oslo Center for Biostatistics and Epidemiology, University of Oslo, and Group Leader at the Department of Medical Genetics, Oslo University Hospital. His work bridges mathematical modeling with clinical applications, particularly in cancer research. His academic background includes a PhD in Mathematical and Computational Biology from Complutense University of Madrid (2012), preceded by multiple Master's degrees in Mathematics and Physics from Spanish universities. Dr. Köhn-Luque's research focuses on mathematical oncology , developing computational models to understand cancer dynamics and improve treatment strategies. His work spans multiscale modeling of tumor growth, personalized cancer medicine through computer simulations, and biomarker discovery using machine learning approaches. He has made significant contributions to modeling breast cancer progression and treatment response, particularly in the context of endocrine therapy and CDK4/6 inhibition. His recent publications demonstrate a strong trend toward integrating mechanistic learning approaches that combine mathematical models with machine learning techniques. This hybrid methodology allows for more accurate prediction of treatment outcomes while maintaining biological interpretability. His work frequently involves collaboration with clinical researchers to ensure models are grounded in real patient data and have direct translational potential. Computational modeling of tumor heterogeneity and drug response Development of methods for phenotypic deconvolution in cancer cell populations Integration of multi-omics data for personalized treatment prediction Application of birth-death processes to model tumor evolution Creation of user-friendly computational tools for biomedical researchers Dr. Köhn-Luque has supervised multiple PhD students including Even M Myklebust, Salim Ghannoum, and Xiaoran Lai, and has secured funding for projects including RESCUE, BigInsight, and Integreat. His research demonstrates a consistent trajectory from theoretical mathematical biology toward increasingly clinically relevant applications in personalized cancer medicine.
Professor Ole-Christoffer Granmo is a distinguished academic at the University of Agder, Norway, where he serves as Professor in the Department of Information and Communication Technology. He is the Founding Director of the Centre for Artificial Intelligence Research (CAIR) at the University of Agder, leading cutting-edge research in artificial intelligence and machine learning. Dr. Granmo obtained his master's degree in 1999 and his PhD in 2004, both from the University of Oslo. His academic journey has been marked by significant contributions to the field of AI, most notably the creation of the Tsetlin machine in 2018, for which he received the AI research paper of the decade award from the Norwegian Artificial Intelligence Consortium (NORA) in 2022. Professor Granmo's research primarily focuses on logical and causal world modeling across multiple modalities including images, sound, and natural language. His work spans logical auto-encoding, convolution, regression, transformer architectures, and reinforcement learning, all with the overarching goal of creating ultra-low-power artificial general intelligence through transparent logical learning and reasoning. His publications reveal a strong emphasis on interpretable AI systems, hardware implementations, and applications across diverse domains including cybersecurity, healthcare, social media analysis, and bioinformatics. AI Research Paper of the Decade (2022) - Norwegian Artificial Intelligence Consortium (NORA) Eight paper awards in machine learning Professor Granmo has coordinated over seven research projects and mentored 55+ master's students and nine PhD students. His leadership extends to co-founding the Norwegian Artificial Intelligence Consortium (NORA) and establishing two companies: Anzyz Technologies AS and Tsense Intelligent Healthcare AS. As an advisor at Literal Labs, he actively bridges academic research with practical industry applications, demonstrating his commitment to translating theoretical innovations into real-world solutions that address complex challenges across multiple sectors.
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.
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.
Dr. Hongbin Liu is a Senior Lecturer at the Department of Informatics, King’s College London, UK, affiliated with the Centre for Robotics Research. His work focuses on robotic tactile sensing, soft robot design, and haptic exploration. Research Interests: Robotic tactile sensing Learning objects by touch Soft and flexible robot design Modelling of soft interactions Dr. Liu’s recent publications highlight advancements in robotics and sensor technology , particularly in tactile perception, medical device design, and continuum robot navigation. His work integrates Bayesian classifiers for autonomous object recognition, polymer-based optical waveguides for triaxial tactile sensors, and deep learning for medical diagnostics. Collaborations span institutions like Imperial College London, The University of Hong Kong, and Medical University of Vienna. Professional Affiliation: King’s College London Department of Informatics Centre for Robotics Research (research group)
Abbas Roozbahani is an Associate Professor in the Department of Building and Environmental Technology at the Faculty of Science and Technology, Norwegian University of Life Sciences (NMBU). His academic expertise lies in Water Infrastructure Engineering, where he contributes to research, teaching, and project leadership in sustainable urban water systems. His research interests include: Sustainable water management Urban water transport systems (drinking water, wastewater, stormwater) Risk assessment of water infrastructure Simulation and optimization of water systems Hydroinformatics and artificial intelligence Asset management for urban water infrastructure The analysis of his recent publications (2022–2025) reveals a strong focus on integrating advanced computational methods—such as Bayesian Networks, Fault Tree Analysis, machine learning (e.g., LSTM), and multi-criteria decision-making (MCDM)—into water resources management. His work frequently addresses urban stormwater optimization, drought and climate change risk assessment, groundwater forecasting, and the water-food-energy nexus, demonstrating a consistent trend toward data-driven, risk-informed, and sustainable solutions for complex water systems. Dr. Roozbahani teaches graduate-level courses including: THT301 - Asset Management for Urban Water Infrastructure THT302 - Analysis and Design of Water Distribution Networks THT261 - Introduction to Water and Wastewater Systems (co-instructor) THT313 - Water Management in Changing Conditions (co-instructor) THT390 - Preparations for the Master's Thesis (co-instructor) He has supervised multiple MSc and PhD students and led projects funded by academic and private institutions. His collaborative research spans international institutions, with frequent co-authorship on topics related to risk modeling, AI in hydrology, and sustainable infrastructure planning.
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.
Carlos José Díaz Baso is a Research Fellow at the Rosseland Centre for Solar Physics (RoCS), part of the Institute of Theoretical Astrophysics at the University of Oslo. His research focuses on solar chromospheric phenomena, Bayesian statistics, and deep learning applications in solar physics. Education: Ph.D. in Astrophysics (2014-2018, Universidad de La Laguna, Spain), followed by postdoctoral positions at Stockholm's Institute for Solar Physics (2018–2022) and currently at RoCS (2022–present). Research emphasizes analyzing solar spectra and magnetic field dynamics using advanced statistical and machine learning techniques. Key projects include the ISSRESS initiative studying small-scale solar reconnection events. Recent publications explore spectral resolution impacts, coronal oscillations, and sunspot light bridges. Active in international collaborations using instruments like SST/CRISP and SolO/EUI. Engaged in developing observational strategies for solar telescopes and improving data analysis methodologies.
Salman Nazir is Professor at the University of South-Eastern Norway (USN) , Faculty of Technology, Natural Sciences and Maritime Sciences, Department of Maritime Operations. He heads the Training and Assessment Research Group (TARG) and is Scientific Leader of the national Centre of Excellence in Maritime Simulator Training and Assessment (COAST). Since 2019 he has held the rank of Professor, after serving as Associate Professor from 2015 and earlier post-doctoral and lecturer roles in Norway, Italy, South Korea and Pakistan. Education PhD in Industrial Chemistry and Chemical Engineering ( cum laude ), Politecnico di Milano, Italy, 2011–2013 MSc in Chemical Engineering (Process System Engineering), Hanyang University, South Korea, 2007–2009 BSc in Chemical Engineering, Bahauddin Zakariya University, Pakistan, 2002–2006 Research Interests Prof. Nazir’s work sits at the intersection of Human Factors, Safety and Simulation Technology . He investigates how immersive virtual- and augmented-reality simulators, novel training syllabi and evidence-based performance indices can enhance operator competence and safety in complex maritime and process-industry systems. Concepts such as Distributed Situation Awareness , multi-criteria decision making , accident analysis and learning process optimisation are central to his multidisciplinary agenda, which actively involves cognitive scientists, computer scientists, maritime practitioners and industrial stakeholders. Research Trends & Article Overview Across more than 80 peer-reviewed outputs, a clear trajectory emerges: early focus on process-industry training simulators and KPI development evolved into maritime-centric studies on simulator fidelity, VR-based education, and human-automation interaction in autonomous shipping. Recent work (2019–2021) emphasises systematic reviews and comparative European studies, validating VR-headset efficacy, performance-assessment frameworks, and sociotechnical implications of increased automation. Honours & Awards COAST designated one of 12 national Centres of Excellence in Education (SFU) by DIKU, Norway Coordinator/Leader, EU Horizon 2020 project ENHANCE (multi-million NOK) 400 000 NOK MARKOM 2020 Workshop grant PhD cum laude , Politecnico di Milano Young-researcher grants, Politecnico di Milano (2011 & 2013) Merit scholarships, Hanyang University & Korean Government Advising & Grant Portfolio Prof. Nazir currently supervises 6 master students and 1 PhD candidate in “Automated Performance Assessment in Maritime Operations”, while co-supervising additional PhD students at Liverpool John Moores University. He has successfully graduated 3 master and 6 bachelor students . External funding includes EU Horizon 2020, Norwegian SFU scheme, MARKOM 2020, Maritime Technology and Innovation (MTDI) and multiple Italian national grants. Labs & Collaborative Networks He leads TARG at USN, acts as Scientific Leader of COAST , and collaborates with leading international scholars such as Prof. Zaili Yang (Liverpool John Moores), Prof. Annette Kluge (University of Duisburg-Essen), Prof. Davide Manca (Politecnico di Milano) and Prof. Paulo Carvalho (UFRJ, Brazil). These partnerships span computer science, cognitive psychology, maritime logistics and safety engineering, ensuring a truly interdisciplinary research ecosystem.
Ingrid Bouwer Utne is a Professor in the Department of Marine Technology at the Norwegian University of Science and Technology (NTNU). Her research focuses on risk assessment, autonomy, safety, and maintenance management of marine and maritime systems. She leads the Risk Group at NTNU and has been a main supervisor for numerous PhD students in areas like autonomous systems safety and risk modeling. She is actively involved in research projects such as the ERC AdG BREACH (Risk-Based Rationality in Autonomous Systems), SFI Autoship (Center for Autonomous Ships), and SAFEGUARD (Intelligent autonomous systems for safeguarding ocean infrastructure). Her work emphasizes risk-based control systems, online risk monitoring, and human-autonomy collaboration in maritime operations. Key publications include 'Risk and Interdependencies in Critical Infrastructures' (Springer) and 'Online Probabilistic Risk Assessment of Complex Marine Systems' (Springer). She has advised over 20 PhD students and contributed to industry-funded projects like UNLOCK and ORCAS, focusing on safer autonomous systems design and verification.
