since 2022
Munich
Ludwig Maximilian University of Munich
PhD candidate, Department of Statistics. Supervisor: Prof. Dr. Helmut Küchenhoff, in cooperation with Prof. Dr. Ralf Ludwig (Geography). Affiliated with MCML.
I am a PhD candidate in Statistics at LMU Munich. My cumulative dissertation develops statistical methods for decomposing climate model uncertainty into bias, internal variability and forced signal, and applies them to drought characterisation and forecasting in Europe: a vine-copula-based multivariate bias correction, a generalised additive model framework for internal variability, and an uncertainty-aware deep-learning drought forecasting system. Alongside my research I teach statistics and R, and I advise researchers through StaBLab, LMU’s statistical consulting unit.
since 2022
Munich
Ludwig Maximilian University of Munich
PhD candidate, Department of Statistics. Supervisor: Prof. Dr. Helmut Küchenhoff, in cooperation with Prof. Dr. Ralf Ludwig (Geography). Affiliated with MCML.
2020 – 2022
Munich
Ludwig Maximilian University of Munich
Master of Science in Statistics (Statistik mit wirtschafts- und sozialwissenschaftlicher Ausrichtung)
2018 – 2020
Munich
Ludwig Maximilian University of Munich
Postgraduate studies (60 ECTS), major Statistics, minor Sociology
2017
Mexico
Tecnológico de Monterrey
Semester abroad
2014 – 2018
Pforzheim
Hochschule Pforzheim
Bachelor of Science in Business Administration / Market Research
from Oct 2026
Munich
BayWaT-AI Project
Researcher: deep-learning models for river water temperatures and riverine heatwaves in Bavaria
since 2022
Munich
Statistical Consulting (StaBLab)
Advising students and researchers on statistical issues
since 2022
Munich
Teaching
Climate & Statistics seminars for Bachelor and Master students; Statistics for Geophysicists lecture and exercise (winter term 2025/26); R-Kurs at StaBLab. See Teaching
since 2021
Munich
Essential Data Science Training GmbH
Teaching assistant, R and statistics
2022 – 2025
Munich
ClimEx II Project
Climate modelling, drought prediction and bias correction
2020 – 2022
Munich
Chair for Statistical Learning & Data Science
Student assistant, mlr3 package and teaching
| Category | Tools |
|---|---|
| Languages | R, Python, LaTeX |
| Version control | Git, GitHub, GitLab |
| IDE | RStudio, VS Code |
| Server | LRZ: SuperMUC Linux Cluster, AI Systems |