Scholar
Michel Besserve
Google Scholar ID: Nbq6kI0AAAAJ
Max Planck Institute for Intelligent Systems
Citations & Impact
Citations
2,067
H-index
24
i10-index
29
Publications
20
Co-authors
55
list available
Contact
Resume (English only)
Academic Achievements
- Published papers such as 'Targeted Reduction of Causal Models' (arXiv 2023), 'Learning soft interventions in complex equilibrium systems' (UAI 2022), etc. Theoretical contributions focus on principles and assumptions for the identifiability of causal models, i.e., the possibility of recovering information about true mechanisms based on data.
Research Experience
- Research projects include developing causal machine learning tools to uncover the internal structure and transformations of complex artificial, physical, and socioeconomic systems; using causal generative AI to simulate meaningful changes to data generating mechanisms and produce 'what-if' scenarios to explore possible system transformations, thereby anticipating failures and informing decision makers.
Background
- Research interests: Causal machine learning, understanding and anticipating changes in complex systems. Professional field: Application of AI in complex systems, particularly focusing on enhancing the trustworthiness and interpretability of AI.
