Marc Wiedermann
PostDoc / Data Scientist
Robert Koch Institute
marcw@physik.hu-berlin.de
Nordufer 20, 13353 Berlin
Marc joined the group during the Corona pandemic in Mid 2020 as a PostDoc and Data Scientist. He is a theoretical physicist by training and has widespread interest and experience in the development of novel methods for time series analysis, data visualization and the reduction of large-scale system properties to core principles. He is involved in predictive modeling tasks for the Corona Datenspende as well as exploratory analyses of anomalous patterns in the COVID-19 Mobility Project.
- Complex Systems & Network Analysis
- Machine Learning & Predictive Modeling
- Modeling spreads of human behavior and opinions
PhD in Theoretical Physics, 2018
Humboldt University of Berlin
MSc in Physics, 2014
Humboldt University of Berlin
Year Abroad, 2012
The Chinese University of Hong Kong
BSc in Physics, 2011
Humboldt University of Berlin
Experience
- Data-driven modelling of human behaviour and opinion spreading on social networks
- Spatio-temporal extreme event statistics
Technologies: python (numpy, scipy, pandas, networkx), C++, MPI
- Prediction of emerging music trends and customer preferences based on artist similarties and streaming/sales histories
- Monitoring and comparison of google searches for key music artists
Technologies: python, javascript (d3.js), HTML, CSS, SQL, AWS EC2, Hive, Qubole
- Development of nonlinear classifiers for different events in environmental data
- Classification of network-like complex systems based on spatial, topological or information-theoeric indicators
Technologies: python (numpy, scipy, pandas), C++, MPI
- Compilation, harmonization and quantification of a data set to uncover differences in infrastructure networks across Europe and North America from spatial features
Technologies: OpenStreetMap, python