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Photo of Mathias Trabs

Prof. Dr. Mathias Trabs

Research interests:

  • Nonparametric and high-dimensional Statistics
  • Statistics for stochastic processes
  • Statistical inverse problems
  • Statistical Learning
  • Stochastic (partial) differential equations


A list of my publications and preprints as well as a link to my book can be found here.

Current and past PhD students:

2022 - now Lea Kunkel Karlsruhe Institute of Technology
2021 - now Thea Engler Joint supervision with Christian Schroer and Johannes Hagemann Desy
2021 - now Jan Rabe Joint supervision with Natalie Neumeyer Universität Hamburg
2021 - now Sebasian Bieringer Joint supervision with Gregor Kasieczka Universität Hamburg
2019 - 2024 Maximilian F. Steffen Multivariate estimation in nonparametric models: Stochastic neural networks and Lévy processes Karlsruhe Institute of Technology
2017 - 2021 Florian Hildebrandt Parameter estimation for SPDEs based on discrete observations in time and space Universität Hamburg

Short CV

Since 2021 Professor at Karlsruhe Institute of Technology
2021 Heisenberg professor at Universität Hamburg
2016 - 2021 Assistant professor at Universität Hamburg
2015 - 2016 DFG research fellow at Université Paris-Dauphine
2014 - 2015 Postdoctoral researcher at Humboldt-Universität zu Berlin
2014 Visiting Ph.D. student at the University of Cambridge, UK
2011 - 2014 Ph.D. study in Mathematics at Humboldt-Universität zu Berlin
2007 - 2011 Studies in Mathematics with minor Economics at Humboldt-Universität zu Berlin

Further activities

Current Projects

DASHH is a Helmholtz graduate school involving several partner institutions in Hamburg. In DASHH we harness data, computer and applied mathematical science to advance our understanding of nature. We aim to educate the future generation of data- and information- scientists that will tackle tomorrow’s scientific challenges that come along with large-scale experiments.

  • DFG project TR 1349/3-1 "High-dimensional statistics for point and jump processes"

While most of the statistical research for stochastic processes is restricted to one-dimensional or low-dimensional models, an important feature of data sets in modern applications is high dimensionality. The aim of this project is to combine the statistical theory for stochastic processes with high-dimensional statistics to construct and analyse new statistical methods for high-dimensional stochastic processes.

This research project aims at the mathematical analysis of machine learning methods in sufficient width and depth. On the grounds of the mathematical findings, we further aim to improve existing learning methods, or to develop new ones. In this way, we provide a fundamental account to the construction of more advanced learning algorithms The project is a collaboration between scientists from four mathematical disciplines: Stochastics, Optimization, Dynamical Systems and Approximation.