Problem solver and builder with a background in Industrial Engineering.
Practical experience in Machine Learning research, Reinforcement Learning for financial markets, and developing end-to-end ML systems.
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First Author - Enhancing Data Efficiency for Training Object Detectors, presented at IEEE Intelligent Vehicles Symposium 2025 (Link).
My journey into tech started during my Industrial Engineering bachelor's, where I took a C/C++ programming course with in-person coding exams. I experimented with Python on my own, and dabbled in IT security out of curiosity.
During my master's, a data science talk (German) completely shifted my focus. I began taking every university course I could find on statistics and machine learning, alongside specialized online Progams, such as the IBM Data Science Specialization. Afterwards I got the opportunity to do two internships.
First internship – Large German bank: built a proof of concept for a reinforcement learning trading system for the stock and forex markets. This involved implementing market data ingestion, designing the RL environment, and running performance benchmarks.
Second internship – Major German automotive company: researched dataset reduction techniques for object detection, leveraging coreset selection to improve model efficiency.
After the internships we continued the research and the results led to the first-authored paper Enhancing Data Efficiency for Training Object Detectors, presented at IEEE Intelligent Vehicles Symposium 2025 (see above).