Source: HMGU

Prof. Dr. Dr. Fabian Theis
Head of Institute and Research Group Leader

Phone: +49 89 3187-43260
Building/Room: 58a / 112

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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 working group head of CMB at the Institute of Bioinformatics at the Helmholtz Center Munich. In spring 2009, he became associate Professor for Mathematics in Systems Biology at the Math Department of the TU Munich. 2009-2014 he was member of the ‘Young Academy’ (founded by the Berlin-Brandenburg Academy of Sciences and Humanities and the German Academy of Natural Scientists Leopoldina). In 2010 he was awarded an ERC starting grant. 

Since May 2013 Fabian Theis is Director of the Institute of Computational Biology at the Helmholtz Zentrum München and holds the Chair "Mathematical Modeling of Biological Systems" at the Department of Mathematics of the TU Munich. Since 2019 he is Associate Faculty at the Wellcome Trust Sanger Institute in Hinxton, UK. His research interests include development of computational methods for analyzing and modelling single cell heterogeneities as well as machine and  deep learning for prediction in biology and biomedicine. In 2017 he was awarded the Erwin Schrödinger prize together within an interdisciplinary team at the ETH Zürich. Fabian Theis is part of and also coordinates various consortia (i.e. sparse2big involving 8 Helmholtz Centers) and founded the network SingleCellOmics Germany (SCOG). Fabian Theis coordinates the 2019 launched Munich School for Data Science (MUDS) and is scientific director of theHelmholtz Artificial Intelligence Cooperation Unit (Helmholtz AI) 


Selected Publications

1. Bergen, V., Lange, M., Peidli, S., Wolf F.A., Theis,F.J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nature Biotechnology. doi:10.1038/s41587-020-0591-3 (2020).

2. Böttcher, A, Büttner, M, Tritschler, S, [...], Theis, FJ°, Lickert, H°. Wnt/PCP-primed intestinal stem cells directly differentiate into enteroendocrine or Paneth cells. accepted at Nature Cell Biology (2020)

3. Sachs S, Bastidas-Ponce, A, Tritschler, S, [...]., Tschöp, MH, Theis, FJ°, Hofmann, SM°, Müller, TD°, Lickert, H°. Targeted pharmacological therapy restores β-cell function for diabetes remission. Nature Metabolism 2, 192–209 (2020)

4. Lotfollahi, M, Wolf, FA and Theis, FJ. scGen predicts single-cell perturbation responses. Nature Methods 16, 715–721 (2019)

5. Fischer, DS, Fiedler, AK, Kernfeld, E, Genga, RM, Hasenauer, J, Maehr, R., Theis, FJ. Inferring population dynamics from single-cell RNA-sequencing time series data. Nature Biotechnology 37, 461–468 (2019)

6. Buettner, M, Miao, Z, Wolf, FA, Teichmann, SA°, Theis, FJ°. A test metric for assessing single-cell RNA-seq batch correction. Nature Methods 19, 43–49 (2019)

7. Eraslan, G, Simon, L, Mircea, M, Mueller, NS, Theis, FJ. Single cell RNA-seq denoising using a deep count autoencoder. Nature Communications 10, 390 (2019)

8. Wolf, F, Angerer, P, Theis, FJ. SCANPY: Large-scale single-cell gene expression data analysis. Genome Biology 19, 15 (2019). (ranked most cited paper that year from Gen Biol)

9. Buggenthin, F, Buettner, F, Hoppe, PS, Endele, M, Kroiss, M, Strasser, M, Schwarzfischer, M, Loeffler, D, Kokkaliaris, KD, Hilsenbeck, O, Schroeder, T°, Theis, FJ°, Marr, C°. Prospective identification of hematopoietic lineage choice by deep learning. Nature Methods 14 403–406 (2017)

10. Haghverdi, L, Buettner, M,  Wolf FA , Buettner F, Theis FJ. Diffusion pseudotime robustly reconstructs lineage branching. Nature Methods 13, 845–848 (2016)

 ° joint corresponding authors