Summary
Onno Eberhard is a Ph.D. student at the Max Planck Institute for Intelligent Systems and the University of Tübingen, specializing in reinforcement learning with strong roots in theoretical computer science, physics, and mathematics. With nine years of experience spanning industry and academia, he has interned at Google and contributed to research groups focused on learning, dynamical systems, and autonomous exploration. His work bridges optimal control and probabilistic inference, and he has published on noisy exploration for continuous-action deep RL and transfer learning for low-resource speech recognition. Comfortable shipping ML pipelines in production settings from his Siemens data-science work, he combines hands-on engineering with rigorous theoretical inquiry. Colleagues describe him as a researcher who connects abstract theory to practical algorithms, often exploring less obvious links between control theory and probabilistic modeling.
9 years of coding experience
2 years of employment as a software developer
IHK Apprenticeship Electronics Technician for Machines and Drive Technology, IHK Apprenticeship Electronics Technician for Machines and Drive Technology at Siemens Professional Education
Bachelor of Science Electrical Engineering and Information Technology, Bachelor of Science Electrical Engineering and Information Technology at University of Duisburg-Essen
Master of Science Machine Learning, Master of Science Machine Learning at University of Tübingen
Abitur, Abitur at Georg-Büchner-Gymnasium Seelze
Visiting Student Electrical and Electronics Engineering, Visiting Student Electrical and Electronics Engineering at Nanyang Technological University Singapore
German, English, Chinese