David Aldous’s technical work in probability theory has covered a wide range of topics: weak convergence, exchangeability, Markov chain mixing times, continuum random trees, stochastic coalescence and spatial random networks. A central theme has been the study of large finite random structures, obtaining asymptotic behaviour as the size tends to infinity via consideration of some suitable infinite random structure.
He has recently become interested in articulating critically what mathematical probability says about the real world, via public lectures and his web site. He was the first winner of the Loève Prize in probability, and is a Foreign Associate of the US National Academy of Sciences.
Professor, Department of Statistics, University of California, Berkeley
Interest and expertise
Applied mathematics and theoretical physics, Pure mathematics
Probability theory, Stochastic processes, Mathematical modeling