Michel studied electrical engineering and applied mathematics at ENS Paris-Saclay in France. After a Doctorate in Physics at Paris-Saclay University, he joined as a postdoc the Cognitive Neurophysiology department of the Max Planck Institute for Biological Cybernetics in Tübingen in 2008, where he developed data analysis tools to study distributed information processing in brain networks. He became project leader in the same department in 2014, directing the modeling and data analysis efforts to understand high-level brain functions, notably episodic memory. At the same time, Michel started investigating principles of causal machine learning in the department of Empirical Inference of the Max Planck Institute for Intelligent Systems of Tübingen. Since 2022, Michel Besserve leads the research on Machine Learning for Complex Systems at the Max-Planck-Institute for Intelligent Systems. He is particularly interested in how machine learning can help uncover the principles governing natural and artificial systems through the lens of causality.
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