CV

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I am a PhD candidate in Statistics at LMU Munich, supervised by Prof. Dr. Helmut Küchenhoff and working with Prof. Dr. Ralf Ludwig’s group for Applied Physical Geography and Environmental Modelling. 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

since 2022
Munich

Ludwig Maximilian University of Munich
PhD Candidate at the Department of Statistics, supervised by Prof. Dr. Helmut Küchenhoff, in cooperation with Prof. Dr. Ralf Ludwig’s working group for Applied Physical Geography and Environmental Modeling, Department of Geography. Affiliated with the Munich Center for Machine Learning (MCML) through the Küchenhoff research group.

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

Experience

from 10/2026
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 (WS 25/26); 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

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)