My academic research focuses on statistical machine learning for data problems from various domains, especially, in the field of bio-medicine, genetics, and natural sciences more generally. In particular, I study problems for tree ensemble methods, such as random forest, blind source separation with certain finite alphabet constraints, change-point detection employing multiscale methods, and statistical methodology related to tree structures.

I have currently an opening for phd and postdoc positions.  

Scientific Career

Since October 2022Professor for Machine Learning, Faculty of Informatics and Data Science, University of Regensburg, Germany
Dec 2020 - Sep 2022Scientific Expert at Bayer AG,
Research & Development, Pharmaceuticals, Leverkusen, Germany
July 2019 - Nov 2020Postdoc,
Research fellowship German Research Foundation (DFG),
Department of Statistics,
University of California, Berkeley, USA
Aug 2018 - June 2019Neyman Visiting Assistant Professor,
Department of Statistics,
University of California, Berkeley, USA
Jan - Aug
DFG - Research Training Group 2088, Discovering structure in complex data: Statistics meets Optimization and Inverse Problems, University of Goettingen, Germany
2014 - 2017PhD student,
Institute for Mathematical Stochastics,
University of Goettingen, Germany
(PhD Supervisor: Professor Axel Munk)
2009 - 2014Studies of mathematics,
University Goettingen (Germany) and
University of Edinburgh (UK)

In this YouTube video I give an overview of my PhD thesis for non-experts at the 3-Minute Thesis Competition, University of Goettingen:


e-mail: mail@merlebehr.org or merle.behr@ur.de