Larry Wasserman is a statistician known for his work on high dimensional inference, nonparametric inference, machine learning,
topological data analysis and astrostatistics. Much of his work has been devoted to providing statistical foundations for algorithms in machine learning. Wasserman was born in Windsor, Ontario. He went to the University of Toronto, graduating with a Ph.D. in Biostatistics in 1988. He has been at Carnegie Mellon since 1988. He won the COPSS (Committee of Presidents of Statistical Societies) Presidents’ Award in 1999, the Centre de recherches mathematiques de Montreal – Statistical Society of Canada Prize in Statistics in 2002 and the DeGroot Prize in 2006. He is a Fellow of American Statistical Association and the Institute of Mathematical Statistics. He is a member of the National Academy of Sciences.

Research Interests

Larry Wasserman is interested in high dimensional inference, machine learning, clustering, nonparametric inference, topological data analysis and astrostatistics. Much of modern data analysis involves problems with more variables than observations. Wasserman and his colleagues have developed theory and methods to analyze data rigorously in this setting while minimizing the number of required assumptions. They have also developed new methods for clustering, which involves finding groups of observations. Recently, they have investigated topological data analysis, a new fields that uses ideas from topology to find structure in data. In particular, they developed statistical methods to separate real topological patterns from noise. Wasserman is a co-founder of the Astrostatistics group at Carnegie Mellon. This is one of the largest astrostatistics groups in the world and is devoted to using cutting edge statistical methods to analyze data from telescopes and satellites to help cosmologists address fundamental scientific questions about the universe.

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Primary Section

Section 32: Applied Mathematical Sciences

Secondary Section

Section 34: Computer and Information Sciences