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.

David Bang
(University of Warwick)

Thursday 13 April 2023
13:15–14:00
TBA

Stochastics Seminar
(Stochastics Group)

26–30 June 2023
Aalborg University

Summer School
(Stochastics Group)

Hirsch, C., Neumann, M. & Schmidt, V. (2023). Asymptotic properties of one-layer artificial neural networks with sparse connectivity. *Statistics & Probability Letters*, *193*, [109698]. https://doi.org/10.1016/j.spl.2022.109698

Cipriani, A., Hirsch, C. & Vittorietti, M. (2023). Topology-based goodness-of-fit tests for sliced spatial data. *Computational Statistics and Data Analysis*, *179*, [107655]. https://doi.org/10.1016/j.csda.2022.107655

Rheinbay, E., Nielsen, M. M., Abascal, F., Wala, J. A., Shapira, O., Tiao, G., Hornshøj, H., Hess, J. M., Juul, R. I., Lin, Z., Feuerbach, L., Sabarinathan, R., Madsen, T., Kim, J., Mularoni, L., Shuai, S., Lanzós, A., Herrmann, C., Maruvka, Y. E. ... PCAWG Drivers and Functional Interpretation Working Group (2023). Erratum: Author Correction: Analyses of non-coding somatic drivers in 2,658 cancer whole genomes (Nature (2020) 578 7793 (102-111)). *Nature*, *614*(7948), E40. https://doi.org/10.1038/s41586-022-05599-9

Aeckerle-Willems, C. & Strauch, C. (2022). Sup-norm adaptive drift estimation for multivariate nonreversible diffusions. *Annals of Statistics*, *50*(6), 3484-3509. https://doi.org/10.1214/22-AOS2237

Borup, K., Kidmose, P., Phan, H. & Mikkelsen, K. (2023). Automatic sleep scoring using patient-specific ensemble models and knowledge distillation for ear-EEG data. *Biomedical Signal Processing and Control*, *81*, [104496]. https://doi.org/10.1016/j.bspc.2022.104496

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

Emerita professor

Statistics – stochastic geometry, stereology, spatial statistics

Emeritus associate professor

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