Biography
Robbins was born in New Castle, Pennsylvania.
As an undergraduate, Robbins attended Harvard University, where Marston Morse influenced him to become interested in mathematics. Robbins received a doctorate from Harvard in 1938 under the supervision of Hassler Whitney and was an instructor at New York University from 1939 to 1941. After World War II, Robbins taught at the University of North Carolina at Chapel Hill from 1946 to 1952, where he was one of the original members of the department of mathematical statistics, then spent a year at the Institute for Advanced Study. In 1953, he became a professor of mathematical statistics at Columbia University. He retired from full-time activity at Columbia in 1985 and was then a professor at Rutgers University until his retirement in 1997. He was also a dedicated and helpful advisor to many Ph.D. students and he has 567 descendants listed at the Mathematics Genealogy Project.
In 1955, Robbins introduced empirical Bayes methods at the Third Berkeley Symposium on Mathematical Statistics and Probability. Robbins was also one of the inventors of the first stochastic approximation algorithm, the Robbins-Monro method, and worked on the theory of power-one tests and optimal stopping. In 1985, in the paper "Asymptotically efficient adaptive allocation rules", with TL Lai, he constructed uniformly convergent population selection policies for the Multi-armed bandit problem that possess the fastest rate of convergence to the population with highest mean, for the case that the population reward distributions are the one-parameter exponential family. These policies were simplified in the 1995 paper "Sequential choice from several populations", with MN Katehakis.
He was a member of the National Academy of Sciences and the American Academy of Arts and Sciences and was past president of the Institute of Mathematical Statistics.
Read more about this topic: Herbert Robbins
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