Research Areas

  • Program logic, including mathematical properties of computer programs and interactive theorem proving.
  • Computational biology on the molecular level, including biological sequence analysis, protein structure modeling, inference of evolution.
  • Numerical analysis of the partial differential equations describing ice and ocean dynamics. In particular focused on finite element methods for non-linear free surface problems.
  • Computational statistics, including data science, machine learning , information theory, modeling and analysis of stochastic dynamical systems.


An illustration of a mesh for FEM-analysis of ice-sheet movement on Greenland.

Josefin Ahlkrona

Josefin studies finite element methods for geophysical flows. She received her PhD in 2016 from the Division of Scientific Computing, Department of Information Technology, at Uppsala University, where she developed finite element and radial basis function methods for computer simulations of ice sheets. She then studied stabilized finite element methods at the Christian-Albrechts-Universität zu Kiel in Germany before she joined the division of Computational Mathematics in January 2019.

Chun-Biu Li

My research interest is to develop and apply statistical and computational methods to understand how biophysical systems work. Two specific questions I am asking are what are the constructive roles of stochasticity and heterogeneity in biological functions, and what are the appropriate multi-scale theoretical methods and models to study them. In particular, my focuses are on the interplays between information theory, data science, machine learning and statistical physics. I also teach and supervise in Mathematical Statistics.

Lars Arvestad

My background is in Computer Science and I study problems in evolution and genomics. The fundamental questions relate to making use of the information in biological data: how do we best extract knowledge out of the given data and how do we do it efficiently?

A comparison of several protein-coding genes, with colors indicating the class of amino acids that codons (DNA triples) code for.

Anders Mörtberg

My main research interests are currently in homotopy type theory and univalent foundations, in particular computational justifications to univalence and higher inductive types using cubical type theory. I'm also interested in constructive mathematics, logic and type theory, category theoretical foundations, functional programming, and computer formalization of mathematics and computer science.

A view of code and workspace for computer-assisted theorem proving with Cubical.