Juniorprofessor für Mathematische Stochastik
Mathematische Statistik und Stochastische Prozesse
Bundesstraße 55 (Geomatikum)
Phone: +49 40 42838-4931
E-Mail: mathias.trabs (at) uni-hamburg.de
Secretary: Brigitte Deest (Tel: -4924, room T11)
Consultation-hour (Sprechstunde): Currently only via telephone or skype.
My skype name is trabsmat.
- Nonparametric and high-dimensional Statistics
- Statistics for stochastic processes
- Statistical inverse problems
- Stochastic (partial) differential equations
- M. Sc. Florian Hildebrandt
- M. Sc. Maximilian F. Steffen
- M. Sc. Vladislav Sukharnikov
We have a regular Colloquium on Mathematical Statistics and Stochastic Processes and a Working group seminar in our research group.
- Data Science in Hamburg - Helmholtz Graduate School for the Structure of Matter (DASHH)
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
- LD-SODA: "Lernbasierte Datenanalyse – Stochastik, Optimierung, Dynamik und Approximation" (Landesforschungsförderung Hamburg)
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.