Professor of Statistics
Department of Statistics
(510) 642-2781

Research Expertise and Interest

statistics, machine learning, semiparametric models, asymptotic theory, hidden Markov models, applications to molecular biology


Peter Bickel's research spans a number of areas. In his work on semiparametric models (he is a co-author of the recent book Efficient and Adaptive Estimation for Semiparametric Models), he uses asymptotic theory to guide development and assessment of such models. His studies of hidden Markov models, which are important in such diverse fields as speech recognition and molecular biology, are directed toward understanding how well the method of maximum likelihood performs. He is also interested in the bootstrap, in particular in constructing diagnostic measures to detect malfunction of this technique. Recently he has become involved in developing empirical statistical models for genomic sequences. He is a co-author of the well known book Mathematical Statistics: Basic Ideas and Selected Topics. He is past President of the Bernoulli Society and of the Institute of Mathematical Statistics, a MacArthur Fellow, and a member of the American Academy of Arts and Sciences and of the National Academy of Sciences.

Update Faculty Profile