Dominik Schrempf

Dominik Schrempf

postdoctoral fellow

Supervisor: Gergely Szöllősi

PhD (Vetmeduni Vienna, 2018)


Room(s): Lágymányos Campus, Northern Building 3.136
Phone(s): +36-1-372-2785
Extension(s): +36-1-372-2500 / 6325
Email: uh.etle.raseac@fpmerhcsd



Dominik is interested in evolutionary biology with a special focus on phylogenetics. He studies phenomena leading to discord between evolutionary relationships of different genomic regions. These processes include incomplete lineage sorting, gene duplication and loss, and horizontal gene transfer. To model these phenomena, Dominik uses Markov processes, birth and death processes, and various graph algorithms. In recent years, Dominik has also developed an increasing interest in functional programming. For example, he uses the Haskell programming language for building simulation and analysis software.


2018 – Present: Postdoc, Eötvös Loránd University
2013 – 2017: PhD student, Vienna Graduate School of Population Genetics, Vetmeduni Vienna
2006 – 2013: Student in Technical Physics, Vienna University of Technology

2018: Promotio sub auspiciis praesidentis rei publicae (Austria’s highest possible honor for school and study success) accompanied by the Austrian Award of Excellence

Links to associated scientific database profiles:

Selected publications of recent years:

  1. Schrempf, D., Lartillot, N., & Szöllősi, G. (2019), Scalable empirical mixture models that account for across-site compositional heterogeneity, Submitted to Molecular Biology and Evolution. Available on bioRxiv, link
  2. Schrempf, D., Minh, B. Q., von Haeseler, A., & Kosiol, C. (2019). Polymorphism-aware species trees with advanced mutation models, bootstrap, and rate heterogeneity, Molecular Biology and Evolution, 36(6), 1294–1301.
  3. Schrempf, D., & Hobolth, A. (2017). An alternative derivation of the stationary distribution of the multivariate neutral Wright-Fisher model for low mutation rates with a view towards mutation rate estimation from site frequency data. Journal of Theoretical Population Biology, 114, 88–94.
  4. Schrempf, D., Minh, B. Q., De Maio, N., von Haeseler, A., & Kosiol, C. (2016). Reversible polymorphism-aware phylogenetic models and their application to tree inference. Journal of Theoretical Biology, 407, 362–370.
  5. De Maio, N., Schrempf, D., & Kosiol, C. (2015). PoMo: An Allele Frequency-Based Approach for Species Tree Estimation. Systematic Biology, 64(6), 1018–1031. Shared first authorship with De Maio, N.