Mathematical Statistics

The group conducts research developing stochastic models and statistical methods. The topics of interest are

For life science statistics see the webpage of the research group in Biomathematics and biostatistics.
Our research in various ways intersects with other research groups at the Department, in particular with the groups in Computational Mathematics, Probability theory, and Optimization.

Twitter account of the Statistics group: follow it for news on statistics seminars, job openings in Statistics and more.


Please visit the Statistics seminar which is also announced in the Calendar of Mathematical Sciences.


Patrik Albin Extreme values
Marina Axelson-Fisk Bioinformatics
Ottmar Cronie ​Spatial and spatio-temporal statistics, statistical learning
Olle Häggström ​Bayesian statistics and risk analysis
​Rebecka Jörnsten ​Biostatistics
Erik Kristiansson ​Biostatistics
​Petter Mostad ​Bayesian statistics and forensics
​Umberto Picchini ​Bayesian inference and likelihood-free methods for stochastic modelling
Holger Rootzén ​Extreme values and risk analysis
​Serik Sagitov ​Branching and coalescent processes
Moritz Schauer ​Inference for stochastic differential equations, non-parametric Bayes
Aila Särkkä ​Spatial statistics
Sergei Zuyev ​Spatial statistics
​Peter Jagers ​Population dynamics, point processes
​Olle Nerman​ ​​Biostatistics
Mats Rudemo ​Spatial statistics and image analysis
Igor Rychlik Ocean engineering modelling
​Nanny Wermuth ​Multivariate statistical models and their properties
PhD Students
Oskar Allerbo ​Biostatistics
Oskar Eklund ​Stochastic continuous-depth neural networks
​Felix Held ​Statistical methods for description and estimation of undirected graphs
​Henrik Imberg ​Sampling theory
​Juan Inda ​Multi-omics data integration
​Julia Jansson ​Spatial statistics, point process learning
​Petar Jovanovski ​Simulation-based Bayesian inference and deep learning for stochastic modelling
​Konstantinos Konstantinou ​Spatial statistics
​David Lund Large-scale analysis of DNA sequencing data​
Vincent Molin​ ​Machine learning, Monte Carlo methods for Bayesian inverse problems
​Helga Kristín Ólafsdóttir ​Extreme value theory, non-Gaussian models within spatial statistics
Vincent Szolnoky ​Neural networks and the implications of infinitely wide ones
​Selma Tabakovic ​Generalization bounds of neural networks

Page manager Published: Thu 03 Nov 2022.