From left to right: Michael Niebisch, Andreas Gerhardus, Christian Reimers, Violeta Teodora Trifunov, Jakob Runge, Xavier-Andoni Tibau, Christoph Käding, Yanira Guanche Garcia, Christian Requena-Mesa.
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, Science Advances, Physical Review Letters, AISTATS, 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.
Christoph Käding (Postdoc)
Christoph is a computer scientist focusing on conditional independence testing as one of the building blocks for causal discovery. In detail, he is working on effective and efficient machine learning methods to discover dependencies in data with variables of mixed types. His goal is to provide techniques that support a better understanding of the structure and processes in complex dynamical systems such as the Earth’s climate system.
Christoph studied computer science at Friedrich Schiller University Jena, Germany, until 2014. After obtaining his master’s degree, he joined the Computer Vision Group of Friedrich Schiller University in October 2014 as a PhD student and worked on the project ‘Incremental Learning of Object Categories’. While he is about to finish his thesis, he became a part of the Climate Informatics Group at DLR in March 2019.
Andreas Gerhardus (Postdoc)
Andreas is a theoretical physicist working on causal discovery techniques with a focus on nonlinear time series. His goal is to apply those methods in collaboration with domain experts in order to support the research on Earth’s climate system.
Andreas studied physics at the University of Bonn from 2010 to 2015, staying abroad at UC Berkeley for half a year. After obtaining his doctor’s degree in theoretical high energy physics from the University of Bonn, he joined the Climate Informatics Group at DLR in November 2019. During his studies as well as the work on his doctoral thesis Andreas was funded by the German Academic Scholarship Foundation (Studienstiftung).
Xavier-Andoni Tibau (PhD student)
Xavier is an environmental scientist specialized in machine learning and artificial intelligence who is working on reducing the uncertainty of climate change projections. In his project, “Constraining uncertainties of climate change projections – A machine learning approach”, he aims to develop a method based on machine learning and advanced statistics for finding reliable emergent constraints in climate model simulations.
Between 2005 and 2010, Xavier obtained a Bachelor degree in environmental science at the Universitat Autònoma de Barcelona, followed by a Masters Degree in Bioinformatics and Biostatistics at the University of Barcelona. Before joining the Climate Informatics group, he also worked on applying machine learning in Social and Medical Science and as a research technician at the University of Barcelona. Privately, Xavier loves informatics, nature and wildlife.
Violeta Teodora (PhD student)
Violeta Teodora is a computer science researcher mainly interested in deep learning, graphical models and how existing expert knowledge can be used to improve artificial learning systems.
Violeta Teodora studied mathematics at the University of Novi Sad from 2012 to 2015 where she obtained a Bachelor’s degree. In 2017, she finished her master studies in mathematics at the University of Bonn and since September 2017 she is working on a PhD project in computer science in the Climate Informatics group at the DLR Institute for Data Science in Jena in collaboration with the Computer Vision group at the Friedrich-Schiller University Jena.
Christian Requena Mesa (PhD student)
Christian is an environmental scientist invested in machine learning and artificial intelligence. He works on how novel computer vision and generative algorithms can improve environmental monitoring, as well as how artificial general intelligence can lead to a better environmental management and decision making. His long-term vision for environmental problem solving relies on the use of novel artificial intelligence methods as a dynamic adviser to help us set the rules by which humans best interact with the environment, maximizing both: the benefits we get from nature, and the stability and resilience of natural systems.
Christian studied environmental science at the University of Málaga (Spain) from 2010 to 2015 while staying abroad at GSU (GA, USA), SNU (South Korea) and Radboud University (Netherlands) obtaining a Bachelor’s degree. In 2017, he finished his master studies in applied ecology at the University of Poitiers (France), Coimbra (Portugal) and UFRGS (Brazil) and since August 2017 he is working on his PhD project “Deep learning approaches for analyzing spatio-temporal memory effects in Earth System data” in computer science in the Climate Informatics group at the DLR Institute for Data Science in Jena in collaboration with the Max Planck Institute for Biogeochemistry and the Computer Vision group at the Friedrich-Schiller University Jena.
Christian Reimers (PhD student)
Christian is a mathematician who works on deep neural networks and focuses on formalizing and understanding deep learning. This includes to make concepts learned by deep neural networks more comprehensible to humans.
Christian got his Bachelor and Master degree in analytical number theory from the Georg-August University in Göttingen and is part of the Computer Vision Group at Friedrich Schiller University Jena and the Climate Informatics Group at the German Aerospace Center since November 2017. The title of his project is “Understanding Deep Learning”.
Michael Niebisch (PhD student)
Michael is a computer scientist mainly interested in deep learning, environmental science, and causality.
After studying at BTU Cottbus-Senftenberg from 2011 to 2016, Michael obtained a Bachelor’s degree in environmental engineering. He got his Master degree in computational and data science in 2018 at the Friedrich-Schiller University Jena. Since 2019 he is part of the Computer Vision group at the Friedrich-Schiller University Jena and the Climate Informatics group at the DLR Institute for Data Science in Jena.