CV

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.

Education

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.

Ludwig Maximilian University of Munich
Master of Science in Statistics (Statistik mit wirtschafts- und sozialwissenschaftlicher Ausrichtung)

Ludwig Maximilian University of Munich
Postgraduate studies (60 ECTS), major Statistics, minor Sociology

Tecnológico de Monterrey
Semester abroad

Hochschule Pforzheim
Bachelor of Science in Business Administration / Market Research

Experience

BayWaT-AI Project
Researcher: deep-learning models for river water temperatures and riverine heatwaves in Bavaria

Statistical Consulting (StaBLab)
Advising students and researchers on statistical issues

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

Essential Data Science Training GmbH
Teaching assistant, R and statistics

ClimEx II Project
Climate modelling, drought prediction and bias correction

Chair for Statistical Learning & Data Science
Student assistant, mlr3 package and teaching

Affiliations

Software & tools

Category Tools
Languages R, Python, LaTeX
Version control Git, GitHub, GitLab
IDE RStudio, VS Code
Server LRZ: SuperMUC Linux Cluster, AI Systems

Languages

  • German (native)
  • English (fluent)
  • Spanish (basic)