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

- Bayesian statistics.
- Spatial and spatio-temporal statistics, stochastic geometry and imaging, with applications towards demography, forestry, offshore operations and wave modelling for sea transports, materials science, neurology, telecommunication.
- Theory for extremes in random processes and fields, and statistical analysis of extreme values such as extreme climatic episodes.
- Reliability improvement: in particular development of load models and fatigue accumulation for trucks.
- Financial risk management: research aimed at new methods for optimization of financial portfolios, measurement of catastrophic risks and handling of credit risks.
- Statistical estimation and algorithms for non-stationary and non-Gaussian processes.
- Computer intensive statistical methods and Big data statistics.
- Environmental statistics.
- Forensic statistics.

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 | |

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 | |

| | |

Emeriti |
| |

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 | |