Björn Kriesche

E-Mail-Adresse

bjoern.kriesche(at)uni-ulm.de

Telefon

+49 (0)731/50-23528

Telefax

+49 (0)731/50-23649

Adresse

 
  • Raum-Nr. 142
    Helmholtzstr. 18
    89069 Ulm
 

Sprechzeiten

nach Vereinbarung

E-Mail-Adresse

bjoern.kriesche(at)uni-ulm.de

Telefon

+49 (0)731/50-23528

Telefax

+49 (0)731/50-23649

Adresse

 
  • Raum-Nr. 142
    Helmholtzstr. 18
    89069 Ulm
 

Sprechzeiten

nach Vereinbarung

Kurz-Lebenslauf

  • seit Okt 2012: Wissenschaftlicher Mitarbeiter und Doktorand am Institut für Stochastik, Universität Ulm
  • Dez 2010 - Sep 2012: Studentischer Mitarbeiter am Institut für Stochastik, Universität Ulm
  • Okt 2010 - Sep 2012: Master of Science in Wirtschaftsmathematik, Universität Ulm
  • Aug 2009 - Sep 2009: Praktikum bei der HDI Gerling Leben in Köln
  • Okt 2007 - Sep 2010: Bachelor of Science in Wirtschaftsmathematik, Universität Ulm

Forschungstätigkeit

Im Rahmen meiner Doktorarbeit mit dem Arbeitstitel Stochastic risk analysis based on geographically mapped data bearbeite ich folgende Projekte:

Modellbasierte Prognose gebietsbezogener Wahrscheinlichkeiten für das Auftreten von Wetterereignissen

Statistische Schätzung von Populationsdichtekarten aus archäologischen Radiokarbondaten für Nordamerika

Publikationen und Vorträge

Publikationen und 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).


Vorträge

  • 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, 10.02.2015, Kohutka.
  • Stochastic modeling of spatially resolved data, with applications to the prediction of area weather events. Charles University, 21.10.2014, Prag.
  • Stochastic modeling of spatially resolved data, with applications to the prediction of area weather events. University of Ottawa, 18.9.2014, Ottawa.
  • A probabilistic approach to the prediction of area-related weather events. 11th German Probability and Statistics Days, 4.3.-7.3.2014, Ulm.
  • Stochastic simulation model for tropical cyclone tracks: Improvements of landfall behavior. 4th International Summit on Hurricanes and Climate Change, 13.6.-18.6.2013, Kos, Greece.


Abschlussarbeiten