People

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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_profile_picture_2019_hochkantJakob Runge (Group leader)

Jakob Runge heads the Climate Informatics working group at the German Aerospace Center’s Institute of Data Science in Jena since 2017 and is guest professor of computer science at TU Berlin since 2021. His group combines innovative data science methods from different fields (graphical models, causal inference, nonlinear dynamics, deep learning) and closely works with experts in the climate sciences and beyond. Jakob studied physics at Humboldt University Berlin and obtained his PhD at the Potsdam Institute for Climate Impact Research in 2014. For his studies he was funded by the German National Foundation (Studienstiftung) and his thesis was awarded the Carl-Ramsauer prize by the Berlin Physical Society. In 2014 he won a $200.000 Fellowship Award in Studying Complex Systems by the James S. McDonnell Foundation and joined the Grantham Institute, Imperial College, from 2016 to 2017. In 2020 he won an ERC Starting Grant with his interdisciplinary project CausalEarth.  [Picture © Mathematisches Forschungsinstitut Oberwolfach]

Jakob’s research was published in Nature Communications, Science Advances, Physical Review Letters, UAI, 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.

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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.

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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).

XavierXavier-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.

VioletaVioleta 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.

DLR_ChrisChristian 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.

ChristianChristian 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.jpgMichael 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.

rvwestenbergerRafael Vieira Westenberger (PhD student)

Rafael Vieira Westenberger is a mathematical economist interested in the theoretical underpinnings of causal inference. His goal is to improve statistical estimation methods within causal frameworks, some of which are commonly used in the context of climate science.

He was awarded a bachelor in Economics (2017) from UFRJ, the Federal University of Rio de Janeiro and a masters in Mathematical Economics (2020) from IMPA, the National Institute for Pure and Applied Mathematics. In September 2020 he joined the German Aerospace Center’s (DLR) Institute of Data Science to work as a PhD student within IMIRACLI, a European Union funded Innovative Training Network.

Nicolas_ReiterNicolas Reiter (PhD student)

Nicolas is a Mathematician and works on combining causal discovery with filtering techniques for time series. The goal is to develop a theoretical and methodological framework via which causal relationships can be detected across different time-scales. Such a framework will hopefully be relevant in applications to the climate science in that it will reveal insights on the dynamics of selected processes in the climate system.

Nicolas holds a Bachelor degree from the Technical University of Munich (2018) and in 2020 he finished his Master studies at the University of Copenhagen where he focused on Algebraic Topology.

In February 2021 he joined the Climate Informatics group at DLR and started working on his PhD project which is also associated with the EU funded international training network IMIRACLI.