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.

Seminars

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

Members​

Faculty ​
Patrik Albin Extreme values
Marina Axelson-Fisk Bioinformatics
​David Bolin ​Spatial statistics
​Olle Häggström ​Bayesian statistics and risk analysis
​Rebecka Jörnsten ​Biostatistics
​Erik Kristiansson ​Biostatistics
​Petter Mostad ​Bayesian statistics and forensics
​Staffan Nilsson ​Statistical epidemiology
​Mike Pereira ​Spatial statistics, SPDEs
​Umberto Picchini ​Bayesian inference and likelihood-free methods for stochastic modelling
​Krzysztof Podgorski ​Random fields and non-Gaussian models
Holger Rootzén ​Extreme values and risk analysis
Mats Rudemo ​Spatial statistics and image analysis
​Serik Sagitov ​Statistical consulting
Moritz Schauer ​Inference for stochastic differential equations, non-parametric Bayes
Aila Särkkä ​Spatial statistics
Sergei Zuyev ​Spatial statistics
​ ​
​Emeriti ​
​Peter Jagers ​Population dynamics, point processes
​Olle Nerman​ ​​​Biostatistics
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
​Anton Johansson ​Deep learning, model selection for neural networks
​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​
​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: Mon 18 Jan 2021.