With over a decade of dedicated research experience, Dr. Shahram Eivazi is leading the Festo Autonomous Systems lab at Tübingen University. He received his PhD from Finland in 2016 on the topic of a hands-free neurosurgical microscope. Then he joined Tübingen University as a postdoctoral researcher for two years. In 2019, Dr. Eivazi joined FESTO company to continue his research in the industry with a focus on robotic AI

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Currently, my research is centered on Reinforcement Learning and its practical applications within the field of robotics. I delve into novel techniques for optimizing the learning process, with a specific focus on advancing offline reinforcement learning methods

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My area of expertise lies in reinforcement learning, specifically emphasizing curriculum learning and model-based strategies. My primary mission is the efficient development of robust, adaptable agents that operate effectively with limited resources. This entails utilizing a sparse domain knowledge base and minimal environmental interactions for agent training. In the long term, my aim is to contribute significantly to the field of reinforcement learning, enabling cost-effective training techniques that can revolutionize the development of intelligent systems.

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My research lies in the field of algorithmic morphology design of robots and machines, where I primarily focus on generating optimal designs given a certain set of modular parts. 

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