ICB Institute of Computational Biology (Helmholtz Center Munich)
Period: April 2024 – Present
Advanced microbial behavior understanding by linking single-cell RNA sequencing and flow cytometry data using optimal transport theory and autoencoders, while expanding an R package for bacterial growth curve analysis.
University of Munich, Department of Statistics (LMU)
Period: March 2023 – November 2023
Created a full open-source S4 R program for bacterial growth curve analysis, which includes preprocessing, low-dimensional embedding, and hypothesis testing of numerous substances.
ICB Institute of Computational Biology (Helmholtz Center Munich)
Period: December 2022 - March 2023
Analyzed 64,000 bacterial growth curves using Python and Gaussian processes to contribute to antibiotic resistance research, and performed differential testing with R and Python to explore new areas and demonstrate biostatistical innovation.
Technical University of Munich, Professorship for Public Health and Prevention (TUM)
Period: October 2022 - March 2023
Using deep learning on 300,000 data entries, we were able to predict strokes in diabetic patients, boosting accuracy by 2% over standard methods. Using Python and PyTorch, we improved risk assessment approaches in recall, precision, and F1 scores, allowing us to swiftly identify high-risk patients for more effective therapy.
University of Munich (LMU)
Period: October 2021 – November 2023
Focused on Biostatistics and High-Dimensional Data Analysis
Master's Thesis: Degrowth - Using Gaussian Processes for Bacterial Growth Curve Analysis in R
Awards: Best Master’s Thesis in the Statistics Department, Santander Universitäten Scholarship
University of Munich (LMU)
Period: October 2018 - September 2021
Focused on Applied Mathematics and Statistics