• Source: Larry A. Wasserman

Larry Alan Wasserman (born 1959) is a Canadian-American statistician and a professor in the Department of Statistics & Data Science and the Machine Learning Department at Carnegie Mellon University.




Biography


Wasserman received his Ph.D. from the University of Toronto in 1988 under the supervision of Robert Tibshirani.
He received the COPSS Presidents' Award in 1999 and the CRM-SSC Prize in 2002.
He was elected a fellow of the American Statistical Association in 1996, of the Institute of Mathematical Statistics in 2004, and of the American Association for the Advancement of Science in 2011. He was elected to National Academy of Sciences in May, 2016.




Selected works


Wasserman has written many research papers about nonparametric inference, asymptotic theory, causality, and applications of statistics to astrophysics, bioinformatics, and genetics. He has also written two advanced statistics textbooks, All of Statistics and All of Nonparametric Statistics.

2004. All of Statistics: A Concise Course in Statistical Inference. Springer-Verlag, New York. ISBN 978-0387402727
won DeGroot Prize 2005.
2006. All of Nonparametric Statistics. Springer. ISBN 978-0-387-25145-5
2013. Topological Inference. Reitz Lecture 2013.




Honors and awards


2016, Member of National Academy of Sciences
Wasserman was elected to National Academy of Sciences in recognition of his distinguished and continuing achievement in original research.




See also


Peer review
Computational thinking




References






External links


Wasserman's home page
Wasserman's Blog on Statistics and Machine Learning

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