SAMBA
SAMBA
Statistical Analysis, Machine Learning and Image Analysis - SAMBA
The SAMBA department has comprehensive theoretical and practical knowledge in the fields of statistics, machine learning and image analysis. We are one of Europe's largest and most competent groups within applied statistics and statistical-matematical modelling. We cover a broad spectrum of methods and are a world leader in some of these areas. The appropriate choice of method for the various problems is thus one of our strengths. Many calculations involve uncertainty and the accurate calculation of this quantity is an important speciality.
Research areas
Our research within statistics for climate and environment is a mixture of applied, problem-driven projects and fundamental research eligible for publication in scientific journals.
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We use statistical methods to quantify all kinds of risk in the banking, insurance and commodity markets. We work a lot with models related to Basel II and Solvency II and have developed a large number of systems related to trading and risk management for various power companies.
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We cover a wide range of applications within the areas marine resources and health.
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We cover a wide range of applications including forecasting, time series analysis, network modelling, optimal delivery, logistics, optimal steering of industry processes, reliability analysis, cost surveys and analysis of very large data sets.
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We do research and development of methodologies for analysis of digital remote sensing data from satellites and aircrafts. Application areas include environmental monitoring, climate change monitoring, environmental emergency detection, natural resource mapping and map revision.
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We have extensive knowledge in the fields of image analysis and machine learning. Object extraction, characterization and recognition from images are central for many of these applications. Another central theme is the analysis of sequences of data, including motion detection and characterization.
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This center for research-based-innovation is funded by the Research Council of Norway. Big Insight produces innovative solutions for key challenges facing our consortium of private, public and research partners, by developing original statistical and machine learning methodologies.
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Last 5 scientific articles
Worsnop, Rochelle P.; Scheuerer, Michael; Hamill, Thomas M.; Smith, Timothy A.; Schlör, Jakob. RUFCO: a deep-learning framework to post-process subseasonal precipitation accumulation forecasts. Artificial Intelligence for the Earth Systems (ISSN 2769-7525). doi: 10.1175/AIES-D-24-0020.1. 2024.
Moen, Per August Jarval; Glad, Ingrid Kristine; Tveten, Martin. Efficient sparsity adaptive changepoint estimation. Electronic Journal of Statistics (ISSN 1935-7524). 18(2) pp 3975-4038. doi: 10.1214/24-EJS2294. 2024.
Manzanares-Salor, Benet; Sánchez, David; Lison, Pierre. Evaluating the disclosure risk of anonymized documents via a machine learning-based re-identification attack. Data mining and knowledge discovery (ISSN 1384-5810). pp 1-36. doi: 10.1007/s10618-024-01066-3. 2024.
Heinrich-Mertsching, Claudio Constantin; Thorarinsdottir, Thordis Linda; Guttorp, Peter; Schneider, Max. Validation of point process predictions with proper scoring rules. Scandinavian Journal of Statistics (ISSN 0303-6898). doi: 10.1111/sjos.12736. 2024.
Engebretsen, Solveig; Aldrin, Magne Tommy; Staven, Fredrik Ribsskog; Bendiksen, Eskil; Stige, Leif Christian; Jansen, Peder Andreas. Heterogeneous Weight Development of Lumpfish (Cyclopterus lumpus) Used as Cleaner Fish in Atlantic Salmon (Salmo salar) Farming. Fishes (ISSN 2410-3888). 9(9) doi: 10.3390/fishes9090336. 2024.
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