Jakob Runge
Professor at University of Potsdam
Potsdam, Brandenburg, Germany
Summary
🤩
Rockstar🎓
Top SchoolJakob Runge is a professor and group lead with a decade of expertise at the intersection of complex systems, causal discovery, and climate data science, currently heading the Climate Informatics group at DLR and holding a professorship at the University of Potsdam. He brings a physics-rooted analytical rigor from Humboldt and a PhD on causal inference in dynamical systems from PIK, blended with postdoctoral experience in complex systems at Imperial College under a JSMF fellowship. Jakob develops production-grade tools for time series causal inference—most notably contributing to the tigramite Python package—combining graphical-model theory with deep learning to make causal methods practical for climate applications. His work is notable for bridging theoretical advances in conditional-independence testing with accessible software and documentation, enabling reproducible research on real-world climate dynamics.
10 years of coding experience
Doctor of Philosophy - PhD, Theoretical and Mathematical Physics, Doctor of Philosophy - PhD, Theoretical and Mathematical Physics at Potsdam Institute for Climate Impact Research and Humboldt University BerlinDiplom, Physics, Diplom, Physics at Humboldt-Universität zu Berlin
Github Skills (8)
data-analysis10
time-series10
python10
scientific-computing10
causal-inference10
algorithm9
algorithms9
documentation8
Programming languages (3)
Github contributions (5)
jakobrunge/tigramite
Oct 2017 - Jan 2023
Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:4 releases, 12 reviews, 361 commits in 5 years 4 months
Contributions summary:Jakob's commits indicate they were involved in updating and modifying conditional independence tests, specifically the Python scripts within the `tigramite` package, that pertain to time series analysis. They removed obsolete print commands, fixed a bug, and integrated new tests and features. They also worked on updating the documentation for the python package in the docs folder.
causal-inferencepythontime-series
jakobrunge/tigramite_old
Dec 2015 - May 2017
Contributions:2 releases, 39 pushes, 1 branch in 1 year 5 months
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