Persönlicher Status und Werkzeuge

Prof. Dr. Fabian J. Theis

Department

Mathematics

Contact Details

Business card at TUMonline

Academic Career and Research Areas

Fabian Theis (b. 1976) conducts research in the field of computational biology. The main focus of his work is the application of machine learning methods to biological questions, in particular as a means of modeling cell heterogeneities on the basis of single cell analyses and also of integrating “omics” data into systems medicine approaches.

Professor Theis received PhD degrees in physics and computer science in 2002 and 2003 respectively. After working as a postdoc in Regensburg, Tokyo and Tallahassee, he took up a position as Bernstein Fellow at the Max Planck Institute for Dynamics and Self-Organization in Göttingen. He then joined the German Research Center for Environmental Health, Helmholtz Zentrum München, where he was a group leader at the Institute for Bioinformatics and Systems Biology for six years. In 2009 he was became an Associate Professor at the Chair of Applied Mathematics, TUM. Since 2013 he has been a Full Professor of biomathematics at TUM, where he holds the Chair of Mathematical Modeling of Biological Systems, and director of the Institute of Computational Biology at the Helmholtz Zentrum München.

Awards

  • Erwin Schrödinger Prize (2017)
  • m4 Award, Bavarian Ministry of Economic Affairs (2015)
  • ERC Starting Grant "Latent Causes" (2010)
  • Member of the Junge Akademie at the Berlin-Brandenburg Academy of Sciences and Humanities and the German National Academy of Sciences Leopoldina (2009)
  • Heinz Maier-Leibnitz Prize, German Research Foundation (DFG) (2006)

Key Publications (all publications)

Wolf FA, Angerer P, Theis FJ: "Scanpy for analysis of large-scale single-cell gene expression data". Genome Biology. 2018; 19 (15).
Abstract

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. 2017;14 (4): 403–406.
Abstract

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

Hilsenbeck O, Schwarzfischer M, Skylaki S, Schauberger B, Hoppe PS, Loeffler D, Kokkaliaris KD, Hastreiter S, Skylaki E, Filipczyk A, Strasser M, Buggenthin F, Feigelman JS, Krumsiek J, van den Berg AJJ, Endele M, Etzrodt M, Marr C, Theis FJ, Schroeder T: "Software tools for single-cell tracking and quantification of cellular and molecular properties". Nature Biotechnology. 2016; 34(7): 703–706.
Abstract

Buettner F, Natarajan KN, Casale FP, Proserpio V, Scialdone A, Theis FJ, Teichmann SA, Marioni JC, Stegle O: "Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells". Nature Biotechnology. 2015; 33(2): 155–160.
Abstract