I currently work as a research associate and statistical consultant for Freie Universität Berlin. After the defense of my doctoral thesis, I am interested to face new challenges. My focus on statistical and algorithmic methods enables me to address a broad range of data-specific problems from numerous scientific disciplines. My previous education and research followed my general interest in predictive analytics for topics of digital transformation and international (economic) development. Professionally, I gained experience in statistical consulting for researchers and companies. Overall, I regard my interdisciplinary horizon, combined with my passion for data science, as a unique quality that complements any team.
Please do not hesitate to contact me for research ideas or potential projects.
Dr.rer.pol. in Applied Statistics, (2019 - 2022)
Freie Universität Berlin
MSc in Economics, (2016 - 2018)
Universität Wien
MA in Development Studies, (2016 - 2019)
Universität Wien
BSc in Statistics, (2015 - 2018)
Universität Wien
BSc in Economics, (2013 - 2016)
Universität Wien
On 19.12.2022
I am happy and thankful to share that I successfully defended my PhD-thesis on the topic of “A Framework for the Estimation of Disaggregated Statistical Indicators Using Tree-Based Machine Learning Methods” on 19.12.2022 in Berlin.
I want to explicitly thank my first supervisor Prof. Timo Schmid, my second supervisor Prof. Nikos Tzavidis and the third expert reader Prof. Ulrich Rendtel.
Additionally, I want to thank everybody who celebrated this special moment with me!
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Now on CRAN
I am delighted to share the information that my first package SAEforest was accepted to CRAN.
You can find further information here:
You can directly download the package from CRAN
Read the manual page for SAEforest.
Read the Vignette for SAEforest
This paper promotes the use of random forests as versatile tools for estimating spatially disaggregated indicators in the presence of small area-specific sample sizes.