Prof. Fabian Theis

Technische Universität München
Department of Mathematics
Chair of Mathematical Modeling of Biological Systems

Boltzmannstr. 3
85748 Garching

Phone:   +49 (89) 289 - 18386
Room:     02.06.039

theis (at)

Institute of Computational Biology

Curriculum Vitae

Fabian Theis obtained MSc degrees in Mathematics and Physics at the University of Regensburg in 2000. He received a PhD degree in Physics from the same university in 2002 and a PhD in Computer Science from the University of Granada in 2003. He worked as visiting researcher at the department of Architecture and Computer Technology (University of Granada, Spain), at the RIKEN Brain Science Institute (Wako, Japan), at FAMU-FSU (Florida State University, USA) and at TUAT's Laboratory for Signal and Image Processing (Tokyo, Japan), and headed the 'signal processing & information theory' group at the Institute of Biophysics (Regensburg, Germany). In 2006, he started working as Bernstein fellow leading a junior research group at the Bernstein Center for Computational Neuroscience, located at the Max Planck Institute for Dynamics and Self-Organisation at Göttingen. In summer 2007, Fabian Theis became junior group leader of „Computational Modeling in Biology” at the Institute of Bioinformatics and Systems Biology at the Helmholtz Zentrum Munich. In 2010 he was awarded an ERC starting grant. Since 2013 he is director of the Institute of Computational Biology at the Helmholtz Zentrum Munich and holds the chair „Mathematical modeling of biological systems“ at the Technical University of Munich. His research focus is on machine learning methods applied to biological questions, in particular for modeling single cell heterogeneities, as well as multi-omics data integtration in the context of systems medicine.

Research Interests

  • Computational systems biology
  • Machine learning
  • Single-cell analysis
  • Cell-mapping
  • Multi-omics integration
  • Dynamical systems
  • Biostatistics
  • Statistical learning theory
  • Stochastic modeling
  • Quantitative imaging