Prof. Dr. Christian L. Müller

Christian L. Müller’s academic trajectory is defined by a sustained effort to bridge computational methods and biological data analysis. He began his training in bioinformatics at the University of Tübingen, complemented by studies in computational mathematics at Uppsala University, establishing an early interdisciplinary foundation. This dual perspective matured during his Ph.D. at ETH Zürich, where he worked on black-box optimization methods and their application to biological systems. 

His postdoctoral years at ETH Zürich and New York University marked a transition toward systems biology and applied mathematics, where he began focusing on statistical learning in biological contexts. This direction crystallized during his tenure at the Flatiron Institute (2014–2019), where he led projects within large collaborative efforts such as CBIOMES. There, he contributed to advancing microbiome data analysis through compositional data modeling, network estimation, and optimal transport methods—tools essential for understanding complex ecological and biological systems. 

Since 2019, Müller has served as Group Leader at Helmholtz Munich and Professor of Statistics at LMU Munich, where he leads research at the intersection of statistics, machine learning, and computational biology. His work centers on extracting structure from high-dimensional biological data, particularly microbial, epigenetic, and single-cell datasets. Key contributions include the development of (microbial) network estimation, Bayesian and variational methods for compositional data, and optimal transport schemes. His recent research extends into biomedical applications, including antimicrobial discovery using learned molecular representations. 

Beyond research, Müller has played an active role in shaping the field through teaching, mentorship, and community building, organizing workshops and training programs in computational biology, statistics,  and optimization. His career reflects a consistent emphasis on methodological innovation driven by real-world biological questions, positioning him as an active contributor within modern data-driven life sciences.