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

Statistics for finance, insurance and commodity markets

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.

Statistics for climate, environment, marine resources and health

We cover a wide range of applications within the fields of climate, environment, marine resources and health. To mention a few, we work with climate sensitivity estimation, reconstructing past global climate, pollution in air and water, abundance estimation of wild fish and whales, and we also do research in genomics and bioinformatics.

Statistics for technology, industry and administration

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.

Earth observation

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.

Image Analysis and Machine Learning

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.

Big Insight

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.

Last 5 scientific articles

Aqrawi, Lara Adnan; Jensen, Janicke Liaaen; Øijordsbakken, Gunnvor; Ruus, Ann-Kristin; Nygård, Ståle; Holden, Marit; Jonsson, Roland; Galtung, Hilde; Skarstein, Kathrine. Signalling pathways identified in salivary glands from primary Sjögren's syndrome patients reveal enhanced adipose tissue development. Autoimmunity (ISSN 0891-6934). 51(3) pp 135-146. doi: 10.1080/08916934.2018.1446525. 2018.

Kvamme, Håvard; Sellereite, Nikolai; Aas, Kjersti; Sjursen, Steffen A. Søreide. Predicting mortgage default using convolutional neural networks. Expert systems with applications (ISSN 0957-4174). 102 pp 207-217. doi: 10.1016/j.eswa.2018.02.029. 2018.

Trier, Øivind Due; Salberg, Arnt Børre; Haarpaintner, Jörg; Aarsten, Dagrun; Gobakken, Terje; Næsset, Erik. Multi-sensor forest vegetation height mapping methods for Tanzania. European Journal of Remote Sensing (ISSN 2279-7254). 51(1) pp 587-606. doi: 10.1080/22797254.2018.1461533. 2018.

Jensen, Helge Leander B.; Lillenes, Meryl Sønderby; Rabano, Alberto; Günther, Clara-Cecilie; Riaz, Tahira; Kalayou, Shewit; Ulstein, Ingun; Bøhmer, Thomas; Tonjum, Tone. Expression of nucleotide excision repair in Alzheimer's disease is higher in brain tissue than in blood. Neuroscience Letters (ISSN 0304-3940). 672 pp 53-58. 2018.

Trier, Øivind Due; Salberg, Arnt Børre; Kermit, Martin Andreas; Rudjord, Øystein; Gobakken, Terje; Næsset, Erik; Aarsten, Dagrun. Tree species classification in Norway from airborne hyperspectral and airborne laser scanning data. European Journal of Remote Sensing (ISSN 2279-7254). 51(1) pp 336-351. doi: 10.1080/22797254.2018.1434424. 2018. Full-text