New employees


David Cohen, professor at the Division of Applied Mathematics and Statistics

Start date: April 1, 2020

My main research interest is numerical analysis of differential equations, with a particular focus on geometric numerical integrators. This basically consists in the design and analysis of structure-preserving numerical methods for the approximation, in time, of solutions to deterministic and random dynamical systems. Such dynamical systems come, for instance, from applications in molecular dynamics, in astrophysics, in the field of modern communication systems, or in nonlinear wave phenomena. Concrete examples are highly oscillatory problems, multiscale problems, models for shallow water waves, or stochastic wave and Schrödinger equations.

I defended my Phd thesis in 2004 (University of Geneva) and then hold various positions (University of Tübingen, NTNU, University of Basel, Karlsruhe Institute of Technology). Since 2012, I worked as an associate professor at Umeå University.


Sebastian Persson, PhD student at the Division of Applied Mathematics and Statistics

Start date: February 1, 2020

Sebastian PerssonMy research as a PhD student in Cvijovic lab will be focused on using mathematical modelling for understanding different cellular processes that are connected to ageing in yeast. For example, in one project I will use reaction-diffusion models to study cell polarization and in another project, I will use ODE-modelling and nonlinear mixed-effects modelling to study signalling pathways that are connected to glucose sensing.

I started as a student at Chalmers in 2015 and have a Bachelor's degree in Biotechnology and a Master's degree in Engineering mathematics and computational science.


Mike Pereira, postdoctor at the Division of Applied Mathematics and Statistics

Start date: February 1, 2020

Mike PereiraI am a postdoctoral researcher within the project STONE, which is carried out jointly by the Division of Applied Mathematics and Statistics in the Department of Mathematical Sciences and the Automatic Control Group within the Department of Electrical Engineering. The goal of the project is to use stochastic partial differential equations to model traffic flows and to estimate parameters based on data from real measurements.

My research areas are primarily spatial and computational statistics. But I also have strong interests in Machine Learning, graph theory and stochastic partial differential equations, and how these domains can foster new approaches to deal with spatial data.

In my previous research, I worked on a matrix-free approach to the simulation, the prediction and the inference of (generalized) Gaussian fields defined on Riemannian manifolds. I also worked on probabilistic models for road crash data using network-constrained point patterns within a Bayesian framework.


Kirsti Biggs, postdoctor at the Division of Algebra and Geometry

Start date: January 15, 2020

Kirsti BiggsI am a postdoctoral researcher working with Julia Brandes on the project "Diophantine Problems with Restricted Sets of Variables".

My research lies in analytic number theory, with a particular focus on using the Hardy--Littlewood circle method to tackle additive problems involving sums of squares, cubes or higher powers, such as variants of Waring’s problem and Vinogradov’s mean value theorem. I am also interested in the interactions of additive combinatorics with such problems.

I recently completed my PhD at the University of Bristol, UK, under the supervision of Trevor Wooley. My latest work involves small subsets of the natural numbers defined by digital restrictions in a given base. These subsets are known as ellipsephic sets, and their digital properties cause them to have a fractal-like structure, which can be seen as a p-adic analogue of certain real fractal sets studied by harmonic analysts.


Marcos Parras Moltó, postdoctor at the Division of Applied Mathematics and Statistics

Start date: January 13, 2020

Marcos Parras MoltoMy research interest is related to the metagenomic study of microbial communities from complex samples with the objective of knowing better their genomic and taxonomic composition.

In previous studies, I have analysed the human oral viral communities for the detection of possible biomarkers that could be useful to establish differences between patients with caries or aphthous ulcers and healthy people, through comparative models based on the genetic distance of the assembled contigs. On the other hand, the determination of the possible hosts of viral contig possessing lithic enzymes may be useful to stablish the base of potential phage therapies.

In addition, I have carried out studies from 16S bacterial amplicons for the determination of the phylogenetic core of different complex samples, as well as establishing the relation between the taxonomic range and the different functions of the bacterial genes that are contained in the 16S references trees.

Now I am interested in learning new techniques and analytical model for the study of antibiotic resistances in bacteria, which in addition to the development of phage therapies could be an improvement in the treatment of so-called “super bacteria”.


Irina Pettersson, lecturer at the Division of Analysis and Probability

Irina PetterssonStart date: January 1, 2020

My research concerns asymptotic analysis and homogenization of partial differential operators. The problems originate often in mathematical physics and describe phenomena like electromagnetic wave scattering on small particles and heat transfer.

In the classical homogenization theory, one studies mixtures of materials with different properties. Such mixtures might have two scales: a microscopic scale describing the microstructure of the heterogeneous material and a macroscopic scale representing the size of the material. The homogenization theory provides tools to rigorously substitute the heterogeneous material with microstructure by a new homogeneous material. The properties of the new homogeneous material are called effective (homogenized) and the resulting equations are often easier to solve than the original equations. One well-known example is a derivation of the Darcy law for transport in porous media  from Navier-Stokes equations.

Now I am interested in signal propagation in neurons, and I am working on the derivation of a 1D nonlinear cable equation from a 3D-model based on the Hodgkin-Huxley model of current flow through ionic channels in neural membrane.

Published: Fri 27 Mar 2020.