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Mathias Trabs

Juniorprofessor für Mathematische Stochastik

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Fachbereich Mathematik
Mathematische Statistik und Stochastische Prozesse
Bundesstraße 55 (Geomatikum)
20146 Hamburg

Room: T23
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.

Research interests:

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

Team:

  • M. Sc. Florian Hildebrandt
  • M. Sc. Maximilian F. Steffen

We have a regular Colloquium on Mathematical Statistics and Stochastic Processes and a Working group seminar in our research group.

Projects:

  • 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 processes.
  • LD-SODA: "Lernbasierte Datenanalyse – Stochastik, Optimierung, Dynamik und Approximation" (Landesforschungsförderung Hamburg)
    Um Risiken beim Einsatz von Lernverfahren zu reduzieren und um bestehende Algorithmen zu verbessern, ist ein Verständnis der zugrundeliegenden mathematischen Methoden der Datenanalyse essentiell. In Zusammenarbeit mit Sarah Hallerberg (HAW), Ivo Nowak (HAW) und Armin Iske (UHH) widmen wir uns im Projekt LD-SODA relevanten mathematischen Fragestellungen des maschinellen Lernens aus den beteiligten Disziplinen Stochastik, Optimierung, Dynamik und Approximation.

 
  Seitenanfang  Impressum 2020-03-16, Mathias Trabs