Prof. Dr. Mathias Trabs
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on appointment
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Kollegiengebäude Mathematik (20.30)
2.020
0721 608 43709
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trabs@kit.edu
Research interests:
- Nonparametric and high-dimensional Statistics
- Statistics for stochastic processes
- Statistical inverse problems
- Statistical Learning
- Stochastic (partial) differential equations
Publications
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
- Deputy speaker of the KIT Center MathSEE Mathematics in Sciences, Engineering, and Economics
- Member of the steering board of the DMV-Fachgruppe Stochastik (Probability and Statistics Group of the German Mathematical Society)
- Member of the steering board of the KIT Graduate School Computational and Data Science (KCDS)
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.
- 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.
| Semester | Titel | Typ |
|---|---|---|
| Winter Semester 2024/25 | Statistik | Lecture |
| Seminar (Numerical and Statistical Aspects of Machine Learning) | Seminar | |
| AG Stochastik | Seminar | |
| Summer Semester 2024 | Einführung in die Stochastik für das Lehramt | Lecture |
| AG Stochastik | Seminar | |
| Proseminar | Proseminar | |
| Winter Semester 2023/24 | Grundlagen der Wahrscheinlichkeitstheorie und Statistik für Studierende der Informatik | Lecture |
| Nichtparametrische Statistik | Lecture | |
| Numerical and Statistical Aspects of Machine Learning | Seminar | |
| AG Stochastik | Seminar | |
| Summer Semester 2023 | Wahrscheinlichkeitstheorie | Lecture |
| Statistical Learning | Lecture | |
| AG Stochastik | Seminar | |
| Winter Semester 2021/22 | Statistical Learning | Lecture |