AI and Data Science

Studying Data Science at University of Regensburg

I am pursuing a Master in Data Science at the Faculty of Informatics and Data Science at the University of Regensburg, with the specializations on Machine Learning and Statistics.

Bachelor Thesis

Examined the feasibility of generating synthetic radar point-cloud data using diffusion models and evaluated the Fréchet Radar Distance (FRD), a radar-specific adaptation of FID for distribution-level evaluation. Designed and implemented the full experimental pipeline in Python/PyTorch, focusing on statistical validation, generative convergence, and principled performance measurement.

Studying AI and Data Science at University of Applied Science Regensburg

Completed a 7-semester dual Bachelor’s degree in AI and Data Science at OTH Regensburg, including an integrated industry placement semester. The program combined academic training in machine learning, statistics, data engineering, and artificial intelligence with continuous practical experience in my dual studi partner company Continental / Aumovio. Focus areas included supervised and unsupervised learning, neural networks, data pipelines, model evaluation, and generative ai.

Dual Study Program at Continental AG / Aumovio

As part of my dual study program, I worked full-time during semester breaks, the dedicated practical semester, and throughout my Bachelor’s thesis, resulting in approximately 1.5 years of hands-on full-time industry experience. I was embedded in an R&D department for AI, contributing to applied AI development in an industrial context. During my practical semester, I evaluated integrating generative AI methods for sensor data within an ongoing series production project under real-world constraints.

Tutor for Nonlinear Methods in AI at OTH

Led tutorial sessions for “Nonlinear Methods in AI” (Analysis II / multivariable calculus), covering gradients, Hessians, and nonlinear optimization.

Tutor for Mathematics I (Linear Algebra I) — OTH Regensburg

Led weekly tutorial sessions for undergraduate students, covering core concepts in linear algebra including vector spaces, linear transformations, and eigenvalue problems.