Dávid is leading teams of machine learning engineers who power the perception systems of automated driving and ADAS products of Continental. Graduated in computer science on the Budapest University of Technology and Economics, enriched his studies on the Karlsruhe Institute of Technology and later on ETH Zurich. Dávid started his professional career at IBM Research in Switzerland working on machine learning research and industry applications. He contributed to deep learning methods in several projects like server outage prediction, medical imaging, sequence prediction of chemical reactions and natural language understanding. He co-authored award-winning papers, finished with high results on a MICCAI challenge and had one of his projects selected as a Franz Edelmann Award finalist in 2020. Dávid decided to relocate to Hungary in 2018 to help building up Continental’s Deep Learning Competence Center in Budapest and to save lives on the roads.
AI for Road Safety – Before the World Reaches Full Autonomy
Promises around mass-scale fully autonomous driving are cooling down as the community is facing challenges ahead. While people and organizations are working full steam to make the long standing dream a reality, there are many things we do to improve road safety even in the short term using the world’s most advanced AI achievements. Driver assistance systems can do a lot for that but require strong and finely tuned AI solutions under the hood. Neural networks ready for safety-critical world perception, running on a few watts of power: an engineering problem that requires a carefully designed chain of AI components. Hear more about what we learned during our journey to achieve that.