The stochastics group at the department comprises researchers active in the fields of

- probability, statistics, data science, optimization, and operations research.

The group is responsible for the study programs in

- statistics, data science and mathematics-economics

and was recently extended with the Applied Statistics Laboratory, which provides statistical support and consultancy to other departments at Aarhus University.

There is a strong tradition in the group for interdisciplinary research as well as for national and international collaborations. To see the breadth of research interests consult the business cards below.

Randolf Altmeyer
(University of Cambridge)

Stochastics Seminar
(Stochastics Group)

Aleksandar Mijatovic
(University of Warwick)

Stochastics Seminar
(Stochastics Group)

Jakob Dalsgaard Thøstesen

PhD defence

Matteo Giordano
(University of Turin)

Thursday 2 November 2023
13:15–14:00
TBA

Stochastics Seminar
(Stochastics Group)

Chen, H., Pelizzola, M. & Futschik, A. (2023). Haplotype based testing for a better understanding of the selective architecture. *BMC Bioinformatics*, *24*(1), Article 322. https://doi.org/10.1186/s12859-023-05437-3

Bonnet, G., Hirsch, C., Rosen, D. & Willhalm, D. (2023). Limit theory of sparse random geometric graphs in high dimensions. *Stochastic Processes and Their Applications*, *163*, 203-236. https://doi.org/10.1016/j.spa.2023.06.002

Christensen, K., Nielsen, M. S. & Podolskij, M. (2023). High-dimensional estimation of quadratic variation based on penalized realized variance. *Statistical Inference for Stochastic Processes*, *26*(2), 331-359. https://doi.org/10.1007/s11203-022-09282-8

Pelizzola, M., Laursen, R. & Hobolth, A. (2023). Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization. *BMC Bioinformatics*, *24*(1), Article 187. https://doi.org/10.1186/s12859-023-05304-1

Hirsch, C., Holmes, M. & Kleptsyn, V. (2023). Infinite WARM graphs III: Strong reinforcement regime. *Nonlinearity*, *36*(6), 3013-3042. https://doi.org/10.1088/1361-6544/acc9a0

The links take you to the personal homepage in AU's system (pure), where you find publications and contact information.

Professor

Machine learning, statistical learning, statistical methods in molecular biology, stochastic processes.

Associate professor

Topological data analysis, large deviations theory, spatial random networks, stochastic geometry, spatial statistics

Associate professor

Nonparametric statistics, stochastic processes, statistical learning

Associate professor

Applied probability, stochastic processes, extremes, distributional robustness, simulation, high-frequency statistics

Associate professor

Statistics, Statistical Learning / Machine Learning, Deep learning for medical images, Monte Carlo Simulation, Bioinformatics

Associate professor

Stochastic geometry, convex geometry, geometric tomography, spatial statistics

Associate professor

Free probability theory, random matrices, Lévy processes, operator algebras.

Associate professor

Statistics – stochastic geometry, stereology, computational and nonparametric statistics, multiple testing

Postdoc

Probability theory, Stochastic processes, Malliavin calculus, convex hulls, rate of convergence.

PhD Student

Postdoc

Emerita professor

Statistics – stochastic geometry, stereology, spatial statistics

Emeritus associate professor

Operations research, inventory control, Markov decision processes, simulation, cost accounting