Jakob Runge (Group leader)
Jakob is a complex systems scientist with a focus on causal discovery techniques from high-dimensional, nonlinear time series. His main research interests are causal inference algorithms using advanced machine learning techniques, causal complex network theory, information flow in complex systems, and nonlinear prediction. Jakob collaborates with researchers from many applied fields to help in better understanding real world complex systems, in particular the climate system.
Jakob studied physics at Humboldt University Berlin funded by the German National Foundation (Studienstiftung). In 2014 he obtained his PhD on causal inference from dynamical complex systems at the Potsdam Institute for Climate Impact Research and Humboldt University Berlin, again funded by the German National Foundation. For his thesis he was awarded the Carl-Ramsauer doctoral thesis prize by the Berlin Physical Society. From 2016 to 2017 he did a Postdoctoral Fellowship in Studying Complex Systems at the Grantham Institute, Imperial College, funded by the James S. McDonnell Foundation.
Jakob’s research was published in Nature Communications, Physical Review Letters, and Journal of Climate, among others. On https://github.com/jakobrunge/tigramite.git he provides Tigramite, a time series analysis python module for causal inference.