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
Basse-O'Connor, Andreas; Heinrich, Claudio Constantin; Podolskij, Mark. On limit theory for functionals of stationary increments Lévy driven moving averages. Electronic Journal of Probability (ISSN 1083-6489). 24(79) pp 1-42. doi: 10.1214/19-EJP336. 2019.
Jang, Youngsoo; Lee, Jongmin; Park, Jaeyoung; Lee, Kyeng-Hun; Lison, Pierre; Kee-Eung, Kim. PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP 2019), System Demonstrations. (ISBN 9781950737925). pp 187-192. doi: 10.18653/v1/D19-3032. 2019.
Fredricson Marquez, Jonatan; Lee, Aline Magdalena; Aanes, Sondre; Engen, Steinar; Herfindal, Ivar; Salthaug, Are; Sæther, Bernt-Erik. Spatial scaling of population synchrony in marine fish depends on their life history. Ecology Letters (ISSN 1461-023X). 22(11) pp 1787-1796. doi: 10.1111/ele.13360. 2019.
Bianchi, Filippo Maria; Livi, Lorenzo; Mikalsen, Karl Øyvind; Kampffmeyer, Michael C.; Jenssen, Robert. Learning representations of multivariate time series with missing data. Pattern Recognition (ISSN 0031-3203). 96:106973 pp 1-11. doi: 10.1016/j.patcog.2019.106973. 2019.
Reksten, Jarle Hamar; Salberg, Arnt Børre; Solberg, Rune. Flood detection in Norway based on Sentinel-1 SAR imagery. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences (ISSN 1682-1750). XLII-3/W8 pp 349-355. doi: 10.5194/isprs-archives-XLII-3-W8-349-2019. 2019.
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