Katja Ickstadt

Prof. Dr. Katja Ickstadt

Mathematical Statistics with Applications in Biometrics

Phone: 0231 / 755 - 3111

Vogelpothsweg 87 | Room 733

CV

Katja Ickstadt studied mathematics with focus on technology at the Technical University of Darmstadt, where she completed her PhD in 1994 and her habilitation in 2001. She conducted research and taught at universities in the USA and Switzerland, and holds a professorship for statistics at TU Dortmund University since 2001. Her research focusses on Bayesian methods, regression methods for very large, high-dimensional data, spatial and spatio-temporal point process modeling with applications in biology and epidemiology, Gaussian process modeling and analysis with various applications, and classification and clustering methods for genetic data. Together with her research group, she maintains an excellent international network of scientists and practitioners in the field of biostatistics.

Research Interests

Bayesian statistics
Regression methods for large high-dimensional data
Spatio-temporal point process modeling in biostatistics
Gaussian process modeling with various applications

Publications

Selected publications

Geppert, L., Ickstadt, K., Munteanu, A., & Sohler, C. (2020). Streaming statistical models via Merge & Reduce. International journal of data science and analytics, 10(4), 331–347. https://doi.org/10.1007/s41060-020-00226-0

Madjar, K., Zucknick, M., Ickstadt, K., & Rahnenführer (2020). Combining heterogeneous subgroups with graph-structured variable selection priors for Cox regression. arXiv:2004.07542v1

Ickstadt, K., Schäfer, M., & Zucknick, M. (2018). Toward integrative Bayesian analysis in molecular biology. Annual review of statistics and its application, 5, 141–167. https://doi.org/10.1146/annurev-statistics-031017-100438

Geppert, L., Ickstadt, K., Munteanu, A., Quedenfeld, J., & Sohler, C. (2017). Random projections for Bayesian regression. Statistics and computing, 27(1), 79–101. https://doi.org/10.1007/s11222-015-9608-z

Treppmann, T., Ickstadt, K., & Zucknick, M. (2017). Integration of multiple genomic data sources in a Bayesian cox model for variable selection and prediction. Computational and mathematical methods in medicine, 2017. https://doi.org/10.1155/2017/7340565

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