Title: Bayesian Functional Data Analysis: Ideas and Applications

Abstract:

Motivated by applications in biomedicine and social science, we review several ideas associated with modeling functional data.  From a Bayesian perspective, flexible probability models are formulated and related to classical contributions in the theory of Gaussian processes and rank-regularized estimation. This basic construction is extended to represent highly-structured observations in functional brain imaging. Specifically, we discuss applications to electroencephalography (EEG) data, collected in a study of neurocognitive development in children with Autism Spectrum Disorder (ASD). If time permits, we also discuss the representation and discovery of latent functional features through finite Indian buffet processes. 

Bio:

Dr. Telesca is associate professor of Biostatistics at UCLA. He received a Ph.D. in Statistics from the University of Washington and spent two years at the University of Texas M.D. Anderson Cancer Center as a postdoctoral fellow.  His research interests include Bayesian methods in multivariate statistics, functional data analysis, statistical methods in bio- and nano-informatics.  

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