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.

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