Machine Learning · Tabular Data
Master's student in Computer Science at Albert-Ludwigs-Universität Freiburg, focused on machine learning and tabular data.
I'm a computer science graduate student researching how machine learning models can be made both accurate and interpretable when applied to structured, real-world data.
Expected Sep 2028
Albert-Ludwigs-Universität Freiburg
Focus on Machine Learning and Tabular Data.
Completed
DHBW Mosbach — in cooperation with TecAlliance
Dual-study program combining coursework with applied industry work at TecAlliance.
Academic and applied work spanning predictive modeling, interpretability, and computer vision.
Predicted machine failure using Random Forests, Causal Forests, and Neural Networks. Used SHAP and feature importance analysis to make model predictions interpretable for maintenance decision-making.
Evaluated image classification tools for vehicle spare part recognition, comparing models including ViT and Amazon Titan. Compared preprocessing and scoring methods such as grayscale conversion and background removal, evaluated performance on an unseen test set, and produced a cost analysis of each approach.
TecAlliance
Worked with the AI & ML team on applied machine learning projects as part of the DHBW Mosbach dual-study program.
TecAlliance
Data specialist for vehicle data, supporting data quality and structuring work during graduate studies.
Feel free to reach out about research, projects, or opportunities.