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
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PhD in Theoretical Physics, 2018
Humboldt University of Berlin
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MSc in Physics, 2014
Humboldt University of Berlin
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Year Abroad, 2012
The Chinese University of Hong Kong
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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