《与大卫·j·奥多斯的对话

IF 3.9 1区 数学 Q1 STATISTICS & PROBABILITY
S. Bhamidi
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引用次数: 0

摘要

戴维·约翰·奥尔德斯1952年7月13日出生于英国埃克塞特。他分别于1973年和1977年在剑桥大学获得数学学士和博士学位。在剑桥圣约翰学院做了两年研究员后,他于1979年加入加州大学伯克利分校统计系,在那里度过了他的学术生涯,直到2018年退休。他在概率学的许多主题上做出了开创性的贡献,包括弱收敛性和紧密性、可交换性、马尔可夫链混合时间、泊松聚集启发式和大型离散随机结构的极限理论,包括随机树、随机凝聚和碎片系统、复杂网络模型和此类结构上的相互作用粒子系统。由于他在该领域的贡献,他获得了许多荣誉和奖项,包括1980年的罗洛-大卫森奖、1993年的首届洛夫概率奖和2021年的布劳沃奖章,并于1985年当选为IMS研究员,1994年当选为皇家学会院士,2004年当选为美国艺术与科学院院士,2010年当选为美国国家科学院院士(外籍院士),2010年当选ICM全体议长,2012年当选AMS研究员。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Conversation with David J. Aldous
. 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 David-son 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.
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来源期刊
Statistical Science
Statistical Science 数学-统计学与概率论
CiteScore
6.50
自引率
1.80%
发文量
40
审稿时长
>12 weeks
期刊介绍: The central purpose of Statistical Science is to convey the richness, breadth and unity of the field by presenting the full range of contemporary statistical thought at a moderate technical level, accessible to the wide community of practitioners, researchers and students of statistics and probability.
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