= Larry Wasserman
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<Larry Wasserman> works on statistical theory and its connections to <machine learning> and scientific applications. His research statement lists <nonparametric statistics>[nonparametric inference], large-sample theory, <mixture model>[mixture models], Bayesian methods, multiple testing and <Causal inference>. It also describes collaborations in computer science and astrophysics.https://www.stat.cmu.edu/GSS/wasserman.html[Author research statement, theoretical interests and scientific collaborations]{ref}
His expository books connect mathematical foundations with practical statistical procedures, including nonparametric methods that avoid specifying a small fixed family of <probability distribution>[probability distributions]. Such flexibility does not remove assumptions: sampling structure, smoothness and complexity still determine what can be learned and how uncertainty should be quantified. His homepage links the books and their accompanying datasets.https://www.stat.cmu.edu/~larry/[Author homepage, Books]{ref}
Homepage: https://www.stat.cmu.edu/~larry/
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{title=Portrait of <Larry Wasserman>}
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