Scientific Computing (SCO)

The Scientific Computing Group (SCO) focuses on developing algorithms, computer architectures, and high-performance computing solutions for bioinformatics.

We mainly focus on

  • computational molecular phylogenetics
  • large-scale evolutionary biology data analyses
  • supercomputing
  • quantifying biodiversity
  • next-generation sequence-data analysis


Secondary research interests include, but are not limited to,

  • emerging parallel architectures (FPGAs, GPUs, Xeon PHI)
  • discrete algorithms on trees
  • population genetics


Here we outline SCO’s current research activities. Our research is situated at the interface(s) between computer science, electrical engineering, biology, and bioinformatics. The overall goal is to devise new methods, algorithms, computer architectures, and freely available/accessible tools for molecular data analysis and make them available to evolutionary biologists. In other words, our overarching goal is to support research. One aim of evolutionary biology is to infer evolutionary relationships between species and the properties of individuals within populations of the same species. In modern biology, evolution is a widely accepted fact and can nowadays be analyzed, observed, and tracked at the DNA level. A famous dictum widely quoted in this context comes from evolutionary biologist Theodosius Dobzhansky: “Nothing in biology makes sense except in the light of evolution.”