"Functional and High-dimensional Data Analysis: Regularization, Representation and Regression"

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Large complex data collected in modern scientific experiments impose tremendous challenges for traditional statistical methods due to high-dimensional and complicated structures. Functional data analysis (FDA) has emerged as a promising field that employs realizations of latent or observed stochastic processes as model units, whose applications are commonly found in many fields, such as longitudinal studies, microarray data, brain images, internet investigations, etc. A fundamental issue of modeling functional data arises from the "curse of dimensionality", as functional objects are conceptually framed as infinite-dimensional processes. This naturally relates to the recently revolutionized area of high-dimensional model selection and regularization.