Principal Investigators at the Munich School for Data Science

Each PhD candidate is supervised by two principal investigators (PIs) to ensure the best possible training in methods development in data science applied in one of the four research topics: biomedicine (HMGU), plasma physics (IPP), earth observation (DLR), or robotics (DLR).

There are two groups of PIs:

  1. methods-oriented PIs and
  2. domain-specific PIs, who are more application-oriented.

Below there is a list of MUDS "core PIs". MUDS "core PIs" are mainly methods-oriented, which means that they can pair with a more application-oriented PI. But they can also pair with methods-oriented PIs and provide in that case the domain-specific topic. With whom core PIs pair depends solely on the PhD research topic of a specific thesis.

Core PIs submit project proposals together with an application-oriented PI (or methods-oriented PI). One of the PIs need to be affiliated with one of the three Munich Helmholtz Centers. All PIs participate in the selection and recruitment of PhD candidates as well as in teaching and supervision.

List of current core PIs:

Principal investigator

Area of expertise

Richard Bamler, DLR

earth observation, signal processing, inverse problems

Bernd Bischl, LMU

supervised machine learning, model selection, bayesian optimization, statistical software

Christian Böhm, LMU

high-performance data mining, big data analytics, medical applications

Hans-Joachim Bungartz, TUM

scientific computing, high-performance computing

Wolfgang Castell, HMGU

mathematical analysis, machine learning, data analysis in biology

Maria Colomé-Tatché, HMGU

(single cell) epigenomics - computational approaches

David Coster, IPP

plasma physics, scientific computing, high-performance computing

Daniel Cremers, TUM

computer vision, optimization, machine learning, image analysis

Mihai Datcu, DLR

signal processing, data science, earth observation

Pascal Falter-Braun, HMGU

protein interaction network function

Julien Gagneur, TUM

genomics, machine learning, statistical modeling

Stephan Günnemann, TUM

machine learning, data mining, data analytics

Irena Hajnsek, DLR

earth observation, signal processing, EM modeling and inversion

Matthias Heinig, HMGU

computational biology, statistical genetics

Frank Jenko, IPP

computational physics, high-performance computing, plasma physics

Göran Kauermann, LMU

statistics

Dieter Kranzlmüller, LMU

high-performance computing, parallel and distributed computing

Annalisa Marsico, HMGU

applied machine learning, statistical learning, RNA bioinformatics, post-transcriptional regulation in systems medicine, high-throughput genomics data analysis

Thomas Neumann, TUM

database systems, query optimization

Martin Otter, DLR

multi-domain modeling, nonlinear control systems, industrial robots

Martin Schulz, TUM

computer architecture, parallel systems, high-performance computing

Thomas Seidl, LMU

data mining and database technology

Eric Sonnendrücker, IPP

numerical methods for PDEs, scientific computing, plasma physics

Fabian Theis, HMGU

computational biology, biostatistics, dynamical systems, machine learning, quantitative imaging, single-cell profiling

Daniel Told, IPP

plasma physics, computational physics, high-performance computing

Udo von Toussaint, IPP

scientific computing, inverse problems, Bayesian inference, data analysis

Rudolph Triebel, DLR

computer vision, machine learning, robotics

Eleftheria Zeggini, HMGU

translation from genomics to mechanisms of disease development and progression, big data, large-scale genetics and genomics studies, epidemiology

Xiaoxiang Zhu, DLR

earth observation, signal processing, machine learning and data science, big data analytics

 

 

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