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
Zhao, Hao; Waldeland, Anders U.; Serrano, Dany Rueda; Tygel, Martin; Iversen, Einar. Time-migration velocity estimation using Fréchet derivatives based on nonlinear kinematic migration/demigration solvers. Studia Geophysica et Geodaetica (ISSN 0039-3169). 64 doi: 10.1007/s11200-019-1172-0. 2020.
Gilbert, Andrew David; Holden, Marit; Eikvil, Line; Aase, Svein Arne; Samset, Eigil; Mcleod, Kristin. Automated Left Ventricle Dimension Measurement in 2D Cardiac Ultrasound via an Anatomically Meaningful CNN Approach. In: Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis. (ISBN 978-3-030-32874-0). pp 29-37. 2019.
Trier, Øivind Due; Cowley, David C.; Waldeland, Anders Ueland. Using deep neural networks on airborne laser scanning data: results from a case study of semi-automatic mapping of archaeological topography on Arran, Scotland. Archaeological Prospection (ISSN 1075-2196). 26(2) pp 165-175. doi: 10.1002/arp.1731. 2019.
Wickstrøm, Kristoffer Knutsen; Kampffmeyer, Michael C.; Jenssen, Robert. Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps. Medical Image Analysis (ISSN 1361-8415). doi: 10.1016/j.media.2019.101619. 2019.
Hubin, Aliaksandr; Storvik, Geir Olve; Grini, Paul Eivind; Butenko, Melinka Alonso. Bayesian binomial regression model with a latent Gaussian field for analysis of epigenetic data. In: Proceedings of Computer Data Analysis and Modeling: Stochastics and Data Science 2019. Belarusian State University Press. (ISBN 978-985-566-811-5). pp 167-171. doi: 123456789/233357. 2019.
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