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November 2022 A Conversation with David J. Aldous
Shankar Bhamidi
Author Affiliations +
Statist. Sci. 37(4): 607-624 (November 2022). DOI: 10.1214/22-STS849


David John Aldous was born in Exeter U.K. on July 13, 1952. He received a B.A. and Ph.D. in Mathematics in 1973 and 1977, respectively from Cambridge. After spending two years as a research fellow at St. John’s College, Cambridge, he joined the Department of Statistics at the University of California, Berkeley in 1979 where he spent the rest of his academic career until retiring in 2018. He is known for seminal contributions on many topics within probability including weak convergence and tightness, exchangeability, Markov chain mixing times, Poisson clumping heuristic and limit theory for large discrete random structures including random trees, stochastic coagulation and fragmentation systems, models of complex networks and interacting particle systems on such structures. For his contributions to the field, he has received numerous honors and awards including the Rollo Davidson prize in 1980, the inaugural Loeve prize in Probability in 1993, and the Brouwer medal in 2021, and being elected as an IMS fellow in 1985, Fellow of the Royal Society in 1994, Fellow of the American Academy of Arts and Sciences in 2004, elected to the National Academy of Sciences (foreign associate) in 2010, ICM plenary speaker in 2010 and AMS fellow in 2012.

Funding Statement

SB was partially supported by NSF DMS-2113662 and NSF RTG Grant DMS-2134107.


We would like to thank Sonia Petrone for her encouragement throughout this process. We would like to thank Manjunath Krishnapur for detailed comments on an initial draft of the interview. We would like to thank an anonymous referee for detailed comments that improved this interview.


Download Citation

Shankar Bhamidi. "A Conversation with David J. Aldous." Statist. Sci. 37 (4) 607 - 624, November 2022.


Published: November 2022
First available in Project Euclid: 13 October 2022

Digital Object Identifier: 10.1214/22-STS849

Keywords: exchangeability , Local weak convergence , Markov chain mixing times , network models , Random graphs , scaling limits

Rights: Copyright © 2022 Institute of Mathematical Statistics


Vol.37 • No. 4 • November 2022
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