Professor. Vice Dean of researchDepartment of Mathematical Sciences Faculty of Information Technology and Electrical Engineering
Background and activities
Ingelin Steinsland (1974) is professor at the Department of Mathematical Sciences. She is a member of the the Statistics group. Professor Steinsland has a MSc in Industrial Mathematics and a PhD in Statistics from NTNU. She is the Vice Dean of research at the Faculty of Information Technology and Electrical Engineering.
Aim of research: Ingelin Steinsland aims to introduce new statistical methods and knowledge to applied scientists, and develop statistical methods and knowledge based on problems from applied scientists. She has a special focus on models and methods for large datasets, dependecies and uncertainty . Steinsland has some key interests; forecasting for physical systems, especially hydrology, quantitative genetics and citizen science. Recently she has been involved in NTNU's initativ on Covid 19.
- 2017- Vice Dean of research, Faculty of Information Technology and Electrical Engineering, NTNU
- 2014- Professor of Statistics, Dept. of Mathematical Sciences, NTNU
- 2008-2014 Associate Professor, Dept. of Mathematical Sciences, NTNU
- 2008- Advisor, SINTEF Energy Research (part time)
- 2006-2007: Researcher SINTEF Energy Research
- 2004-2006 Post Doctor, Dept.of Mathematical Sciences, NTNU. Project: Markov chain Monte Carlo methods in quantitative genetics
- 2003-2004 Associate Professor (temporary), Dept. of Mathematical Sciences, NTNU
- 1998-1999 Software developer in a start-up (VoxelVision AS)
- 2009-2010 Visiting scholar at Department of Statistics, Stanford University, USA
- 2009-2010 Visiting professor at SAMSI/Duke University, North Carolina, USA
- 2001-2002 Visiting post graduate at Department of Statistics, Trinity College, Ireland
Scientific, academic and artistic work
A selection of recent journal publications, artistic productions, books, including book and report excerpts. See all publications in the database
- (2022) A spatial modeling framework for monitoring surveys with different sampling protocols with a case study for bird abundance in mid-Scandinavia. Journal of Agricultural Biological and Environmental Statistics.
- (2022) Quantile based modeling of diurnal temperature range with the five-parameter lambda distribution. Environmetrics.
- (2021) Journal of the Royal Statistical Society: Series C (Applied Statistics) Journal of the Royal Statistical Society: Series C (Applied Statistics) ORIGINAL ARTICLE Open Access A two-field geostatistical model combining point and areal observations—A case study of annual runoff predictions in the Voss area. The Journal of the Royal Statistical Society, Series C (Applied Statistics).
- (2021) A spatial modeling framework for monitoring surveys with different sampling protocols with a case study for bird populations in mid-Scandinavia. arXiv.org.
- (2021) Repeatability in a multiphase pipe flow case study. International Journal of Multiphase Flow. vol. 147.
- (2021) Closure Law Model Uncertainty Quantification. International Journal for Uncertainty Quantification.
- (2020) Accounting for spatial varying sampling effort due to accessibility in Citizen Science data: A case study of moose in Norway. Spatial Statistics.
- (2020) Estimation of annual runoff by exploiting long-term spatial patterns and short records within a geostatistical framework. Hydrology and Earth System Sciences. vol. 24.
- (2020) Hierarchical Modelling of Haplotype Effects on a Phylogeny. Frontiers in Genetics. vol. 11.
- (2020) Spatial modelling improves genetic evaluation in smallholder breeding programs. Genetics Selection Evolution. vol. 52:69.
- (2020) Uncertainty propagation through a point model for steady-state two-phase pipe flow. Algorithms. vol. 13 (3).
- (2019) Twenty-three unsolved problems in hydrology (UPH) – a community perspective. Hydrological Sciences Journal. vol. 64 (10).
- (2019) Streamflow forecast sensitivity to air temperature forecast calibration for 139 Norwegian catchments. Hydrology and Earth System Sciences. vol. 23 (2).
- (2019) Flexible modelling of spatial variation in agricultural field trials with the R package INLA. Theoretical and Applied Genetics. vol. 132 (12).
- (2018) Benefits of spatiotemporal modeling for short-term wind power forecasting at both individual and aggregated levels. Environmetrics. vol. 29 (3).
- (2017) Bayesian Model Averaging for Wind Speed Ensemble Forecasts Using Wind Speed and Direction. Weather and forecasting. vol. 32 (6).
- (2016) Effects of uncertainties in hydrological modelling. A case study of a mountainous catchment in Southern Norway. Journal of Hydrology. vol. 536.
- (2016) Is my study system good enough? A case study for identifying maternal effects. Ecology and Evolution. vol. 6 (11).
- (2016) Spatial modeling with system of stochastic partial differential equations. Wiley Interdisciplinary Reviews: Computational Statistics. vol. 8 (2).
- (2015) Weather SDM: estimating snow density with high precision using snow depth and local climate. Hydrology Research. vol. 46 (4).