Johannes’ research focuses on developing practical andprincipled algorithms for sequential decision-making. His expertise spans awide range of topics from reinforcement learning theory and control, Bayesian optimization, safety and robustness to modern deep learning. He worked on challenging application domains, including deploying state-of-the-art data-driven optimization algorithms on two particle accelerators at the Paul Scherrer Institute. Before joining the SDSC in August 2023, Johannes was a postdoctoral researcher at the University of Alberta and completed an internship at Google DeepMind. Johannes earned his PhD in Computer Science in 2016 at ETH Zurich with Prof. Andreas Krause, and he holds a Master in Mathematics from ETH Zurich.
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