Facioscapulohumeral muscular dystrophy (FSHD) represents a compelling case study in computational biology, characterized by high patient heterogeneity and complex genetic mechanisms operating across multiple molecular scales. The emergence of high-dimensional, multimodal datasets – spanning multi-omics, and including spatial and single-cell resolution data – creates opportunities for sophisticated mathematical modeling and analysis. This workshop will focus on the novel mathematical frameworks and numerical methods for analyzing these complex biological systems, with particular emphasis on machine learning approaches for multi-omics integration, topological data analysis, and dynamical systems modeling of disease progression.