My name is Stefan Peidli. I’m a Postdoc at the Huber Group at EMBL, Heidelberg.

My Current Research

  • Perturbation biology (drugs, crispr, …)
  • Cancer (specifically colon and lung)
  • SARS-CoV2

Research Interests

  • All things single cell
  • Manifold Learning
  • Deep Learning
  • Immunology
  • Fast Evolution (e.g. of Cancer or Viruses)

My story

  • Bioinformatics Postdoc at EMBL working with Open Targets
  • PhD in Computational Biology in Berlin
  • Started working at Theislab, found out I like computational biology
  • Master in Mathematics at TU Munich, found out I like machine learning
  • Bachelor in Mathematics with Minor in Physics at TU Munich

Favorite Papers

  • Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F., & Theis, F. J. (2016). Diffusion pseudotime robustly reconstructs lineage branching. Nature methods, 13(10), 845-848.
  • Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology, 15(12), 1-21.
  • Lipton, Z. C. (2018). The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue, 16(3), 31-57.
  • Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural computation, 14(8), 1771-1800.
  • Luecken, M. D., & Theis, F. J. (2019). Current best practices in single‐cell RNA‐seq analysis: a tutorial. Molecular systems biology, 15(6), e8746.
  • Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018). Deep generative modeling for single-cell transcriptomics. Nature methods, 15(12), 1053-1058.
  • Schiebinger, G., Shu, J., Tabaka, M., Cleary, B., Subramanian, V., Solomon, A., … & Lander, E. S. (2019). Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell, 176(4), 928-943.