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:
- methods-oriented PIs and
- domain-specific PIs, who are more application-oriented.
Below 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.
Pascal Falter-Braun
Helmholtz Munich / LMU
PRINCIPAL INVESTIGATOR
Technical Domains:
High-Dimensional Statistics, Experimental / Trial Design, Reinforcement Learning, Topological Data Analysis
Application Domain
Life Sciences
Subdomain: Molecular Interaction Networks
Matthias Heinig
Helmholtz Munich
Niki Kilbertus
Helmholtz Munich / TUM
Gitta Kutyniok
DLR/LMU
PRINCIPAL INVESTIGATOR
Technical Domains:
Statistical Learning Theory, Reliable AI, Mathematical Foundations of ML / AI, Sustainable AI, including novel hardware such as Neuromorphic
Computing
Application Domains:
Medicine & Health, Robotics
Julia Schnabel
MUDS Scientific Director Helmholz Munich / TUM
Martin Schulz
TUM
PRINCIPAL INVESTIGATOR
Technical Domains:
Time Series Analysis of Observational and Operational Data, Efficient Modelingt, Performance and Energy Optimization of ML Methods on Limited Hardware, Time Series Analysis,
Application Domain:
Earth Observation
Subdomain: Operational Data Analytics of Sensor Data for Complex Systems (e.g. HPC)
