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Explore advanced mathematical and computational methods through this comprehensive conference series featuring nine expert lectures on cutting-edge topics in applied mathematics and data science. Delve into transition time computations with Tony Lelièvre, master Bayesian data assimilation and filtering techniques through Claudia Schillings' instruction, and understand the statistical foundations of deep neural networks via Johannes Schmidt-Hieber's two-part presentation. Learn about innovative model coupling approaches from Olivier Lafitte, discover approximation and learning methodologies using tree tensor networks in Anthony Nouy's dual-lecture series, examine high-dimensional function approximation theory with Albert Cohen, and investigate Bayesian approaches to inverse problems through Massoumeh Dashti's expertise. Gain insights into the intersection of mathematics, statistics, and computational science through these research-level presentations delivered by leading experts in their respective fields.
