PhD in AI and Robotics
I am a researcher and systems architect working at the intersection of computer vision, MLOps, and automotive testing. My work focuses on bridging the gap between traditional ECU-based validation and real-world perception by integrating semantic vision systems into test environments. I develop end-to-end pipelines—from synthetic data generation using digital twins to real-time inference and continuous learning loops—designed to improve robustness, traceability, and automation in industrial settings.
With a strong emphasis on human-in-the-loop systems and self-improving AI, my research explores how feedback from runtime environments can be leveraged to continuously refine models. My approach combines cutting-edge deep learning with practical constraints of embedded and safety-critical systems, making my work highly relevant for next-generation validation frameworks in the automotive industry.
