Prof. Dr. Katja Ickstadt
Room: M/733
Phone: 0231 / 755 - 3111
Fax: 0231 / 755 - 5303
E-Mail: ickstadtstatistik.tu-dortmundde
Address:
Fakultät Statistik
Vogelpothsweg 87
D-44227 Dortmund
Office hours: by arrangement
Research
- Regression methods for very large, high-dimensional data
- Bayesian methods and Markov chain Monte Carlo methods
- Classification and clustering methods for genetic data
- Spatial and spatio-temporal point process modeling with applications in biology and epidemiology
- Gaussian process modeling and analysis with various applications
Publications
A complete overview of the publications can be found here.
2023
Falk Hemker, Timo Haselhoff, Susanne Brunner, Bryce T. Lawrence, Katja Ickstadt, Susanne Moebus. The role of traffic volume on sound pressure level reduction before and during COVID-19 lockdown measures in Bochum, Germany, 2023, Environmental Science and Engineering.
2022
S. M. Seyedpour, C. Henning, P. Kirmizakis, S. Herbrandt, K. Ickstadt, R. Doherty and T. Ricken. Uncertainty with Varying Subsurface Permeabilities Reduced Using Coupled Random Field and Extended Theory of Porous Media contaminant transport models, Water 2023, 15(1), 159. doi. org/ 10.3390/w15010159
Weber, F., Ickstadt, K., & Glass, E. (2022). shinybrms: Fitting Bayesian regression models using a graphical user interface for the R package brms, The R Journal, 14/(2), 96-120. https://doi.org/10.32614/RJ-2022-027
Sarah Friedrich, Andreas Groll, Katja Ickstadt, Thomas Kneib, Markus Pauly, Jörg Rahnenführer, Tim Friede, Regularization approaches in clinical biostatistics: A review of methods and their applications, Statistical Methods in Medical Research, SAGE journals, 2022, https://doi.org/10.1177/09622802221133557
Jonas Kupschus, Stefan Janssen, Andreas Hoek, Jan Kuska, Jonathan Rathjens, Carsten Sonntag, Katja Ickstadt et al. Rapid detection and online analysis of microbial changes through flow cytometry, Cytometry: Part A , Volume 101 - Issue 12, 2022, https://doi.org/10.1002/cyto.a.24704
D. Djeudeu, S. Moebus, K. Ickstadt, Multilevel conditional autoregressive models for longitudinal and spatially referenced epidemiological data, Spatial and Spatio-temporal Epidemiology, Volume 41, 2022, 100477, ISSN 1877-5845, https://doi.org/10.1016/j.sste.2022.100477.
Silvia Chines, Christiane Ehrt, Marco Potowski,Katja Ickstadt, Susanne Brunner, et al. Navigating Chemical Reaction Space - Application to DNA-encoded Chemistry. Chemical Science, 2022, DOI: 10.1039/D2SC02474H.
Djeudeu, D. (2022). spatial and spatio-temporal regression modeling with conditional autoregressive random effects for epidemiological and spatially referenced data (Publisher's Version) [Dortmund University Library]. doi. org/10.17877/de290r-22855
Ickstadt, K., Müller, H., & Weinert, H. (2022). Data literacy. In C. Weihs (Ed.), Statistical data analysis in journalism (Publisher's Version, 1st ed., pp. 29-42). Springer VS. doi. org/10.1007/978-3-662-64693-9_3
Ickstadt, K., Pauly, M., Motta, M., Herbrandt, S., Moroff, N., Niemann, F., Henke, M., & Ten Hompel, M. (2022). Artificial intelligence learning methods for data valorization: automated detection and prediction. In M. Ten Hompel, M. Henke, & B. Otto (Eds.), Silicon Economy (Publisher's Version, pp. 229-250). Springer Vieweg. doi. org/10.1007/978-3-662-63956-6_11
Thomas, M., Bornkamp, B., & Ickstadt, K. (2022). Identifying treatment effect heterogeneity in dose-finding trials using Bayesian hierarchical models. Pharmaceutical Statistics, 21(1), 17-37. doi. org/10.1002/pst.2150
Teaching
Summer term 2023
- Introduction to Data Science II(LSF, moodle)
- Bayes Statistics(LSF, moodle)
- Reading Course Linear Models(LSF, moodle)
- Visualisation of Sports Data with Prof. Dr. Andreas Groll, Prof. Dr. Philipp Doebler(LSF, moodle)
Professional Colloquium(LSF)
Summer term 2022
- Introduction to Data Science II with Prof. Dr. Andreas Groll, Prof. Dr. Claus Weihs and Prof. Dr. Jens Teubner(LSF, moodle)
- Bayes statistics(LSF, moodle)
- Visualization of sports data with Prof. Dr. Andreas Groll, Prof. Dr. Philipp Doebler and Prof. Christina Elmer(LSF, moodle)
- Professional colloquium(LSF)
Winter term 2021/2022
- Introduction to Data Science I with Prof. Dr. Andreas Groll, Prof. Dr. Claus Weihs and Prof. Dr. Jens Teubner(LSF, moodle)
- Statistical Theory with Prof. Dr. Carsten Jentsch(LSF, moodle)
- Professional Colloquium(LSF)
Summer term 2021
- Introduction to Data Science II
- Bayes Statistics
- Visualization of sports data
- Practical Colloquium(LSF)
Short CV
- Habilitation: On hierarchical point process models in spatial statistics, 2001, supervised by Prof. J. Lehn.
- Dr. rer. nat. / Ph.D. , Technical University of Darmstadt, 1994 , Germany (Dissertation): Multivariate parameter estimation for classes of generalized unimodal a priori distributions, supervised by Prof. J. Lehn Technical University of Darmstadt.
- Diploma, Department of Mathematics, Technical University of Darmstadt, 1989, Germany.
Scientific Positions
- 2012 - present: Dean, Department of Statistics, TU Dortmund University, Germany
- 2004 - present: Professor (C4), Chair Mathematical Statistics with Applications in Biometrics, Department of Statistics, TU Dortmund University, Germany
- 2001 - 2004: Professor (C3), For Biostatistics, Department of Statistics, TU Dortmund University, Germany
- 1999: Temporary Professor (6 months) Department of Mathematical Stochastics, University of Freiburg, Germany
- 1998 - 2001:Habilitation Grand (Land Hessen), Department of Mathematics
- 1997 - 1998: Assistant Professor, Department of Statistics, University of North Carolina, Chapel Hill, USA
- 1994 - 1995: Research Associate, Institute of Statistics and Econometrics, University of Basel, Switzerland
- 1995 - 1997: Visiting Assistant Professor, Institute of Statistics and Decision Sciences, Duke University, USA (DFG Postdoctoral Scholarship)