Current courses

I currently teach the following courses at the University of Glasgow and the University of Bergamo.

PhD Short Course on Regularised Estimation in Geostatistics

University of Bergamo (January 2023)

My focus is on regularised estimation procedures applicable for geostatistical and spatial autoregression models. These methods are particularly relevant in the case of big geospatial data for dimensionality reduction or model selection.

Download the slides here

Probability and Sampling Fundamentals

University of Glasgow

Generalised Linear Models

University of Glasgow

Previous courses

Below I list all the courses I have held in the past (University of Bergamo, Leibniz University Hannover, University of Göttingen, European University Viadrina).

Big Geospatial Data

Leibniz University Hannover, since 2019, Master program Geodesy and Geoinformatics, and Mechatronics

First, basics in spatial data analysis and mining are discussed, along with methods to draw conclusions from (geospatial) data. Further, fundamentals of parallel computing are discussed, that is when algorithms can be processed in parallel, computational complexity, and methods of parallel computing. Following this, established approaches to process spatial data are covered. For that, aggregation functions (e.g. mean values, local entropy, rasterization, hotspot detection), data locality, statistical testing, and other topics are discussed based on examples and selected scientific papers.

Statistical Data Science

Leibniz University Hannover, since 2021, Master program Geodesy and Geoinformatics

This module aims to learn about statistical hypothesis testing and modelling in Data Science. The course covers parametric and non-parametric statistical approaches to deal with dependent discrete and continuous data. A particular focus is on how to derive statistically valid conclusions about the underlying random process. After completing the course, the students know which statistical methods are suitable for analysing different data sets. Moreover, they know how to apply these models using the software R.

Monitoring Spatiotemporal and Network Data (Seminar)

Leibniz University Hannover, since 2019, Master program Geodesy and Geoinformatics, and Computational Methods in Engineering

First, we discuss several concepts related to the reliability of networks and the temporal monitoring of spatiotemporal data. In particular, the focus is on procedures to detect deviations of an observed process from a specific target process, where the target process is a spatiotemporal stochastic process or has a complex network structure. Furthermore, statistical process control and control charts are introduced as essential tools. In the second part, the students work on a seminar paper discussing selected methods and procedures. These papers should include a literature review and a small simulation study or practical application. Then, based on a double-blind review (each participant writes a report) and discussing the results in a colloquium, the students can revise their seminar papers before the final submission.

Statistics for Digital and Organisational Innovation

University of Bergamo, Master program in Management Engineering

In this course, the student will develop statistical tools to monitor and understand innovation processes: they will learn how to compute trends and forecasts, quantify uncertainty and assess the impact of changes in the era of innovation and global warming mitigation. Working in an advanced computing environment (Matlab, R or Python), the student will learn to provide an appropriate graphical representation of data and build statistical models for descriptive, interpretative, predictive and simulation purposes. They will develop significant operational experience through participation in a working group for the development of a statistical project. In particular, the student will work with methods of time series analysis and statistical forecasting useful for describing time dynamics, predicting future short-term behaviour, and performing scenario and simulation analyses. Particular attention will be given to uncertainty assessment.

Statistics (in German)

University Göttingen, 2020, Bachelor program in Economics (about 600 participants); European University Viadrina Frankfurt (Oder), 2017-2018, Bachelor program in Business Administration (about 300 participants)

Data Science II - Statistics

University Göttingen, 2020, Bachelor program Applied Data Science (about 30 participants)

Applied Statistics

European University Viadrina Frankfurt (Oder), 2017-2018, Bachelor program in Business Administration (two different courses)

Statistical Models

European University Viadrina Frankfurt (Oder), 2017-2018, Bachelor program in Business Administration

Spatial Statistics and Big Data

Leibniz University Hannover, 2018, Master program Geodesy and Geoinformatics

Further courses

Empirische Methoden in der Steuerforschung (Seminar), Tutorials in Mathematics, Microeconomics, Statistics, Applied Statistics, etc.