جست‌وجوی استاد و پژوهشگر

• Tsetlin Machines
• Reinforcement Learning
• Machine Learning
• +۳ مورد دیگر

Xuan Zhang is an Associate Professor at the Department of Information and Communication Technology, University of Agder. His research focuses on Tsetlin Machines, learning automata, and their applications in machine learning, computer vision, and hyperspectral imaging. Research Trends: Zhang’s recent work includes developing interpretable machine learning models (e.g., Tsetlin Machines), optimizing convolutional architectures for image processing, and applying automata theory to solve multi-armed bandit problems and channel selection in cognitive networks. His field spans theoretical analysis and practical implementations in AI, remote sensing, and health informatics. Scientific Contributions Co-developed advanced Tsetlin Machine variants for XOR/NOT operator convergence, disease forecasting, and image restoration Published in journals like IEEE Transactions on Pattern Analysis and Machine Intelligence , Information Sciences , and Applied Intelligence Explored Bayesian pursuit algorithms, hierarchical learning automata, and particle swarm optimization techniques Contact: xuan.zhang@uia.no

Ida Scheelمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Data Science
• Machine Learning
• +۷ مورد دیگر

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.

Kolbjørn Engelandمشاهده پروفایل

استاد

• Hydrology
• Flood Risk Assessment
• Climate Change Impact on Water Resources
• +۵ مورد دیگر

Kolbjørn Engeland is a Professor at the Section for Geography and Hydrology, University of Oslo. His work focuses on hydrology, flood risk assessment, and climate change impacts on water resources, utilizing Bayesian and stochastic modeling techniques. He collaborates on international projects like 'Advancing frequency analysis of nonstationary hydrological extremes' and 'SnowSub'. Affiliation: University of Oslo, Department of Geosciences Research Groups: Hydrology and Water Resources, LATICE (Land-Atmosphere Interactions in Cold Environments) His research integrates climate science, statistical hydrology, and renewable energy planning. Key projects address flood risk reduction, snow sublimation in hydropower, and long-term hydrological variability. He has published extensively on Bayesian modeling, flood frequency, and climate-hydrology interactions. Recent publications include advancements in geostatistical runoff modeling, climate change adaptation in flood mapping, and stochastic methods in Nordic hydrology. His work spans both theoretical and applied hydrology, with applications to Norwegian and European catchments. Engeland’s advising and project leadership include collaborations with researchers in geosciences and water management, though specific student names are not listed. He contributes to sustainable hydropower analysis and climate change scenarios.

Mads Lund Pedersenمشاهده پروفایل

پژوهشگر

• Cognitive Psychology
• Neuroscience
• Neuropsychology
• +۷ مورد دیگر

Mads Lund Pedersen is a Researcher at the University of Oslo (UiO) and Norment, affiliated with the Department of Cognitive and Clinical Neuroscience. His academic background includes a Dr.philos. (PhD) in Cognitive Neuroscience from UiO (2017) and a Master's in Cognitive Neuroscience (2012). He has held postdoctoral positions at UiO (2017–2020) and was a visiting scholar at Brown University’s Laboratory of Neural Computation and Cognition (2017–2019). His research focuses on computational modeling of decision-making processes in psychiatric and neurological disorders, particularly using reinforcement learning and drift-diffusion models. Key interests include understanding reward sensitivity in addiction, cognitive control mechanisms in adolescence, and the neural basis of psychiatric conditions like depression and psychosis. Collaborations include institutions such as Brown University, Washington University in St. Louis, Harvard Medical School, and the Central Institute of Mental Health in Mannheim. His work integrates neuroimaging, computational models, and genetic data to explore mental health biomarkers and comorbidity mechanisms. Recent projects include longitudinal studies of brain structure in psychosis, normative cognitive trajectories in youth, and the impact of interventions like attention bias modification. Pedersen’s contributions span over 30 peer-reviewed articles in journals like Biological Psychiatry , NeuroImage , and Journal of Cognitive Neuroscience .

Sigrunn Holbek Sørbyeمشاهده پروفایل

استاد

• Bayesian Statistics
• Time Series Analysis
• Spatial Data Modeling
• +۳ مورد دیگر

Sigrunn Holbek Sørbye is a Professor at the Department of Mathematics and Statistics, UiT The Arctic University of Norway. Her research focuses on Bayesian statistics, time series analysis, and spatial data modeling with applications in climatology, ecological statistics, and computational statistics. Institution: UiT The Arctic University of Norway Department: Department of Mathematics and Statistics Research Interests: Dr. Sørbye specializes in Bayesian computation using integrated nested Laplace approximation (INLA), statistical modeling of long-range dependent processes, and applications to climate systems. Her work includes modeling cosmic dust detection rates, analyzing metabolic risk factors, and studying population dynamics through capture-recapture data. Selected Publications Trends: Recent research spans dietary pattern analysis, solar dust modeling, climate sensitivity studies, and ecological monitoring. Key methodologies involve Bayesian hierarchical modeling (2025, 2023), INLA applications (2022, 2020), and long-memory stochastic processes (2020-2019). Collaborative Networks: Dr. Sørbye collaborates on interdisciplinary projects including "Modellering av komplekse systemer" (Complex Systems Modeling) and "Intermittent fluctuations in physical systems." She also contributes to "Transforming ocean surveying by the power of DL and statistical methods." Contact: Tromsø, Norway. sigrunn.sorbye@uit.no | +47 77 64 55 04

Geir Olve Storvikمشاهده پروفایل

استاد

• Data Science
• Computational Statistics
• Bayesian Hierarchical Modelling
• +۵ مورد دیگر

Geir Olve Storvik is a Professor in Statistics and Data Science at the University of Oslo. His research focuses on computational statistics, Bayesian hierarchical modeling, Monte Carlo methods, spatio-temporal modeling, and dynamical processes. He has made significant contributions to Bayesian machine learning and its applications in biological and environmental domains. University: University of Oslo Department: Statistics and Data Science Academic Rank: Professor Storvik's research spans interdisciplinary applications of Bayesian methods in fields such as neural networks, DNA methylation, and infectious disease modeling. His recent work includes sparse Bayesian neural networks and sequential Monte Carlo approaches for epidemiology. Key projects include Bayesian methods in machine learning and BigInsight Statistical and machine learning methods for sensor data . He serves as an advisor to PhD students like Aliaksandr Hubin and Ivar Grytten, with completed theses in graph-based genomics and Bayesian variable selection. His work appears in journals like Transactions on Machine Learning Research , The Journal of Artificial Intelligence Research , and Journal of the Royal Statistical Society , covering topics from model sparsity to ecological forecasting.

Thordis Linda Thorarinsdottirمشاهده پروفایل

استاد

• Statistics
• Data Science
• Environmental Science
• +۵ مورد دیگر

Thordis Linda Thorarinsdottir is a Professor of Statistics and Data Science at the University of Oslo's Department of Mathematics, affiliated with the Faculty of Mathematics and Natural Sciences. She previously worked as a Chief Research Scientist and Research Leader for Climate and Environment at the Norwegian Computing Center (2006–2023). Her research focuses on developing stochastic models for environmental sciences, emphasizing uncertainty quantification and probabilistic prediction in climate, weather, and hydrology. Key areas include spatial and spatio-temporal modeling, Bayesian frameworks, and forecast evaluation. **Education**: PhD in Mathematical Statistics from Aarhus University (2006). **Research Interests**: Environmental applications in climate science, spatial modeling, probabilistic forecasting, and extreme event analysis. She collaborates with experts in meteorology, hydrology, and climate science to address data deficiencies and real-world challenges. **Projects**: Leads initiatives like CONFER (Climate Futures) and is part of the Integreat research group. Her work integrates statistical theory with environmental process understanding to enhance predictive accuracy and decision-making under uncertainty. **Awards**: Not explicitly listed, but her contributions to climate and environmental statistics are recognized internationally. **Grants & Labs**: Active in interdisciplinary collaborations, including the Norwegian Research Council-funded projects. Her team focuses on probabilistic modeling and ensemble forecasting techniques.

Odd Kolbjørnsenمشاهده پروفایل

دانشیار

• Geophysics
• Seismic Inversion
• Bayesian Statistics
• +۳ مورد دیگر

Odd Kolbjørnsen is an Associate Professor at the University of Oslo's Department of Mathematics, affiliated with the Faculty of Mathematics and Natural Sciences. His primary research focuses on geophysics, seismic inversion, Bayesian statistics, and data-driven reservoir modeling. He holds a strong academic background in mathematical geosciences, with contributions to methodologies like Bayesian inversion, Markov mesh modeling, and deep learning applications in geoscience. His work bridges traditional geophysical analysis with modern computational techniques, emphasizing uncertainty quantification and high-resolution imaging in reservoir characterization. Key research areas include seismic data reconstruction, multi-task neural networks for flow metering, and 4D seismic inversion for lithology-fluid prediction. His publications span journals like Geophysics , Mathematical Geosciences , and Neural Networks , reflecting interdisciplinary collaborations in geostatistics and energy engineering. No scientific awards are explicitly listed, though his extensive publication record underscores his research impact. His advisory role and involvement in research groups like 'Statistics and Data Science' at UiO highlight his academic leadership. Ongoing work focuses on integrating machine learning with geophysical data analysis and CO2 sequestration optimization.

• Bayesian Statistics
• Spatial Statistics
• Disease Mapping
• +۳ مورد دیگر

Andrea Ingeborg Riebler is a Professor in the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU). Her research focuses on Bayesian statistics, spatial modeling, disease mapping, and statistical software development. She is a leading contributor to the R-INLA framework for Bayesian hierarchical modeling and has developed packages like SUMMER and makemyprior for spatial-temporal analysis and prior elicitation. Her work bridges methodological advancements with applications in public health, demography, and epidemiology. Key research interests include spatial epidemiology, meta-analysis, and robust statistical modeling. She collaborates widely, particularly with researchers in geostatistics, computational statistics, and health sciences. Notable contributions include methods for small-area estimation in low/middle-income countries, cancer burden projections, and addressing positional uncertainty in geostatistical surveys. Her recent publications (2023–2025) emphasize scalable disease forecasting, mortality-based cancer incidence prediction, and improving DHS data analysis through positional adjustment. Teaching includes advanced computational statistics, survival analysis, and statistical modeling for biologists. Education: Doctoral dissertation work at NTNU (2021) focused on robust hierarchical models. Grants/Projects: Active in international health surveys (e.g., European Social Survey Health Inequalities Module). Labs/Teams: Collaborates with the NTNU Statistics Group and global health data initiatives.

Tore Selland Kleppeمشاهده پروفایل

استاد

• Monte Carlo Methods
• Bayesian Statistics
• Stochastic Volatility Models
• +۵ مورد دیگر

Tore Selland Kleppe is a Professor of Mathematics at the University of Stavanger, affiliated with the Faculty of Science and Technology and the Department of Mathematics and Physics. His research focuses on computational statistics, Bayesian methods, Monte Carlo techniques, and their applications in econometrics and energy economics. Key research interests include Hamiltonian Monte Carlo (HMC) methods, stochastic volatility modeling, commodity price dynamics, and Markov-switching models. He has contributed to advancements in numerical integration for stochastic differential equations, adaptive sampling algorithms, and efficient computation in high-dimensional Bayesian models. Notable work includes developing dynamically rescaled HMC algorithms, incorporating transport maps and importance sampling for hierarchical models, and analyzing commodity futures using state-space frameworks. His publications span top journals like Journal of Computational and Graphical Statistics , Statistics and Computing , and Energy Economics . Collaborations involve experts in econometrics (e.g., Roman Liesenfeld, Atle Oglend) and computational methods. His recent work addresses challenges in restricted domain sampling, storage constraints in energy markets, and adaptive step-size strategies for MCMC efficiency. No awards or grants are explicitly mentioned, but his extensive publication record reflects sustained academic contributions. He actively participates in conferences like the International Conference on Econometrics and Statistics and Norwegian Statistical Association meetings.

Hildegunn Viljugreinمشاهده پروفایل

پژوهشگر

• Ecology
• Evolutionary Biology
• Wildlife Diseases
• +۵ مورد دیگر

Hildegunn Viljugrein is a Researcher at the Center for Ecological and Evolutionary Synthesis (CEES) within the Department of Biosciences, University of Oslo . Her work integrates ecological modeling , wildlife disease epidemiology , and environmental change impact across terrestrial and marine systems. Research Interests : Wildlife disease dynamics with focus on chronic wasting disease in cervids and tick-borne pathogens like Borrelia burgdorferi Mathematical and statistical modeling of salmon lice infestation in aquaculture Environmental drivers of vector competence and parasite load in northern ecosystems Population genetics of wild reindeer under hunting pressure Publication Trends : Analysis of 15 recent articles reveals expertise in Lyme disease ecology , chronic wasting disease modeling , and salmon lice prediction systems . Methodologies emphasize statistical inference , Bayesian hierarchical models , and cross-species disease transmission dynamics . Labs & Collaborations : Active member of CEES's disease ecology team, collaborating with Norwegian Veterinary Institute , University of Bergen , and Nordic wildlife agencies on zoonotic and marine health initiatives.

Morten Jakobsenمشاهده پروفایل

استاد

• Seismic Inversion
• Reservoir Geophysics
• Anisotropic Media
• +۵ مورد دیگر

Morten Jakobsen is a Professor in Reservoir Geophysics at the Department of Geosciences, University of Bergen. His research focuses on advanced geophysical modeling and inversion techniques, particularly in anisotropic media, seismic full waveform inversion (FWI), and electromagnetic logging. He has contributed to computational methods for seismic and medical ultrasound imaging, emphasizing nonlinear inverse scattering and iterative solution strategies. Jakobsen’s work integrates mathematical physics with practical reservoir engineering challenges, addressing topics like fracture parameter estimation, domain decomposition preconditioning, and matrix-free algorithms for large-scale problems. His team collaborates across institutions, including NORCE Norwegian Research Centre, on projects such as 3D geological interpretation for well geosteering (3D GiG) and the DigiWells initiative. Funded by the Research Council of Norway (projects 309589, 336385, 267769), his research spans academic journals like Geophysical Prospecting and Geophysics, with a strong emphasis on computational efficiency and convergence in inverse problems. Education : Not explicitly stated in texts, but his academic role implies PhD in Geophysics or related field. Research interests include reservoir characterization, anisotropic elastic media modeling, and innovative preconditioning techniques to enhance computational workflows. He leads efforts in developing robust algorithms for seismic and electromagnetic data inversion, with applications in both subsurface exploration and medical imaging contexts. Key projects involve collaborative frameworks with industry and academia, focusing on accelerating forward modeling and inversion processes through advanced numerical methods. His publications highlight contributions to the theoretical foundations of FWI and practical implementations in heterogeneous media.

Riccardo De Binمشاهده پروفایل

دانشیار

• Statistical Learning
• High-dimensional Data Analysis
• Survival Analysis
• +۴ مورد دیگر

Riccardo De Bin is an Associate Professor at the University of Oslo, affiliated with the Department of Mathematics and the Statistics and Data Science group. His research focuses on statistical methodology, including high-dimensional data analysis, survival analysis, boosting methods, and resampling techniques. He has contributed to interdisciplinary applications in biomedical research, energy systems, and aviation safety. Research Interests : Asymptotic theory and resampling techniques Variable selection and penalized regression Statistical learning methods for survival analysis Integration of clinical and omics data Applications in energy storage and environmental engineering Recent Work : Recent publications highlight advancements in survival modeling using boosted first-hitting-time approaches, degradation analysis for lithium-ion batteries, and Bayesian methods for nonlinear models. He also addresses methodological challenges in high-dimensional biomedical data and publication bias in statistical practice. Teaching : Teaches advanced courses in statistical learning, including STK2100 (Machine Learning for Prediction), STK4030 (Statistical Learning: Advanced Regression), and STK-IN4300 (Statistical Learning Methods in Data Science).

• Control Systems
• Model Predictive Control
• Wind Farm Control
• +۵ مورد دیگر

Lars Struen Imsland is a Professor at the Department of Technical Cybernetics at the Norwegian University of Science and Technology (NTNU). He has been serving in this position since 2009 and is a member of the Control Systems research group. His work spans multiple domains within control theory and its applications. Dr. Imsland's research interests include: Model Predictive Control (MPC) for various applications Wind farm control and optimization Process control in petroleum engineering Energy systems optimization and control Virtual flow metering and estimation techniques Grey-box modeling approaches His recent publications demonstrate a strong focus on applying advanced control techniques to renewable energy systems, particularly wind farms, as well as petroleum engineering applications. His work often combines traditional control theory with modern machine learning approaches, creating hybrid methodologies that leverage the strengths of both domains. The research shows increasing attention to handling uncertainty in control systems and integrating renewable energy sources with conventional power systems. Dr. Imsland has received recognition for his contributions to control systems through numerous publications in high-impact journals and conferences. As an advisor, Dr. Imsland has supervised multiple master's students working on diverse control applications, including wind turbine control, building energy management, and petroleum engineering systems. His students have gone on to work on cutting-edge research problems at the intersection of control theory and practical engineering applications.

• Paleobiology
• Ecological Statistics
• Evolutionary Biology
• +۷ مورد دیگر

Trond Reitan is a Researcher at the Centre for Planetary Habitability at the University of Oslo . His work bridges paleobiology, statistical ecology, and hydrological modeling, employing advanced Bayesian inference and stochastic differential equations to analyze ecological and evolutionary patterns across diverse systems. Key research areas include: fossil community dynamics, plant-pollinator networks, and climate-biodiversity interactions Active in computational modeling and statistical methodology development Collaborates across disciplines from primatology to genomics Scientific publications (15 most recent) demonstrate expertise in: Stochastic processes and Bayesian inference in ecological modeling Time series analysis of paleobiological datasets Climate change impacts on species interactions Evolutionary implications of genetic repeat variation Hydrological influences on avian breeding patterns Computational frameworks for community stability analysis