Dr. Björn Kriesche

Short curriculum vitae

  • since Oct 2012: PhD student and scientific assistant at the Institute of Stochastics, Ulm University
  • Dec 2010 - Sep 2012: Student assistant at the Institute of Stochastics, Ulm University
  • Oct 2010 - Sep 2012: Master of Science in business mathematics, Ulm University
  • Aug 2009 - Sep 2009: Internship at HDI Gerling Leben in Cologne
  • Oct 2007 - Sep 2010: Bachelor of Science in business mathematics, Ulm University

Research activities

In the context of my PhD thesis with the working title Stochastic risk analysis based on geographically mapped data I work on the following projects

Model-based prediction of area weather events

Statistical estimation of population density maps for North America from radiocarbon data

Publications and talks

Publications and Working Papers

  • B. Kriesche, H. Weindl, A. Smolka, V. Schmidt: Stochastic simulation model for tropical cyclone tracks, with special emphasize on landfall behavior (2014). Natural Hazards 73(2), pp 335-353.
  • B. Kriesche, R. Hess, B. Reichert, V. Schmidt: A probabilistic approach to the prediction of area weather events, applied to precipitation (2015). Spatial Statistics 12, pp 15-30.
  • M. Chaput, B. Kriesche, M. Betts, A. Martindale, R. Kulik, V. Schmidt, K. Gajewski: Spatio-temporal distribution of Holocene populations in North America (2015).  Proceedings of the National Academy of Sciences 112 (39), pp 12127–12132.
  • B. Kriesche, A. Koubek, Z. Pawlas, V. Benes, R. Hess, V. Schmidt: A model-based approach to the computation of area probabilities for precipitation exceeding a certain threshold (2015). Proceedings of the 21st International Congress on Modelling and Simulation, Gold Coast, Australia, 2103-2109.
  • A. Koubek, Z. Pawlas, T. Brereton, B. Kriesche and V. Schmidt: Testing the random field model hypothesis for random marked closed sets (2016). Spatial Statistics 16, pp 118-136.
  • B. Kriesche, A. Koubek, Z. Pawlas, V. Benes, R. Hess, V. Schmidt: On the computation of area probabilities based on a spatial stochastic model for precipitation cells and precipitation amounts (2016). Stochastic Environmental Research and Risk Assessment (accepted).
  • B. Kriesche, R. Hess, V. Schmidt: A point process approach for the spatial stochastic modeling of thunderstorm cells (in preparation).
  • B. Kriesche, M. Chaput, R. Kulik, K. Gajewski, V. Schmidt: Estimation of spatio-temporal correlations of prehistoric population and vegetation in North America (in preparation). 


Talks

  • A model-based approach to the computation of area probabilities for precipitation exceeding a certain threshold. Workshop on Uncertainty Modeling in the Analysis of Weather, Climate and Hydrological Extremes, 16.06.2016, Banff.
  • A model-based approach to the computation of area probabilities for precipitation exceeding a certain threshold. 13th International Meeting on statistical Climatology, 08.06.2016, Canmore.
  • A model-based approach to the computation of area probabilities for precipitation exceeding a certain threshold. 21st International Congress on Modelling and Simulation, 01.12.2015, Gold Coast.
  • On the computation of area precipitation exceedance probabilities by modeling precipitation amounts. STOCHASTIKA 2015, Feb 10, 2015; Kohutka.
  • Stochastic modeling of spatially resolved data, with applications to the prediction of area weather events. Charles University, Oct 21, 2014; Prague.
  • Stochastic modeling of spatially resolved data, with applications to the prediction of area weather events. University of Ottawa, Sep 18, 2014; Ottawa.
  • A probabilistic approach to the prediction of area-related weather events. 11th German Probability and Statistics Days, Mar 4 - Mar 7, 2014; Ulm.
  • Stochastic simulation model for tropical cyclone tracks: Improvements of landfall behavior. 4th International Summit on Hurricanes and Climate Change, Jun 13 - Jun 18, 2013; Kos, Greece.


Theses