Larry Wasserman works on statistical theory and its connections to machine learning and scientific applications. His research statement lists nonparametric inference, large-sample theory, mixture models, Bayesian methods, multiple testing and Causal inference. It also describes collaborations in computer science and astrophysics.[ref]
https://www.stat.cmu.edu/GSS/wasserman.html. Author research statement, theoretical interests and scientific collaborations
His expository books connect mathematical foundations with practical statistical procedures, including nonparametric methods that avoid specifying a small fixed family of 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.[ref]
Homepage: www.stat.cmu.edu/~larry/
Portrait of Larry Wasserman
. Capolitsch, own work, 23 June 2021. Creative Commons Attribution-Share Alike 4.0 International., via Wikimedia Commons. Source.
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