Kenneth W. Wachter
University of California, Berkeley
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Primary Section: 53, Social and Political Sciences Secondary Section: 32, Applied Mathematical Sciences Membership Type:
Member
(elected 1999)
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Biosketch
Ken Wachter was born in Brooklyn, NY, studied at the Pingry School, and received a B.A. in History and Literature from Harvard in 1968 and a Ph.D. in Statistics from Cambridge in 1974. He worked at Bell Labs, was a Junior Research Fellow at St. Catherine's College, Oxford and taught in the Statistics Department at Harvard. In 1977 he became a founding member of the new Department of Demography at the University of California at Berkeley and from then onward has served as Professor of Demography and Statistics, becoming Emeritus in 2014. With Eugene Hammel he developed the SOCSIM demographic microsimulation program for studies of historical household structure, kinship projections, and social support, and has contributed to nonlinear mathematical demography, anthropometric history, statistical endocrinology, census adjustment, biodemography, and evolutionary demography. His textbook Essential Demographic Methods was published in 2014. He was elected to the NAS in 1999. He chaired the NRC Committee on Population 2003--2008 and the Scientific Advisory Board for the new Max Planck Institute for Demographic Research from 1999-2004, and, for 17 years off and on, he has served on the Editorial Board of PNAS, and recently on the NAS Committee on Publications.
Research Interests
Ken Wachter's current research is devoted to genetic evolutionary demography, to empirical studies of extreme longevity in human populations, and to mathematical demography with applications to COVID vaccination strategies. With David Steinsaltz at Oxford and Steve Evans at Berkeley, he helped develop mathematical models for the evolutionary process of mutation accumulation including explicit age-specific demographic structure. These models aim to account for underlying similarities across species in the shapes of demographic schedules. As richer genomic data become available, work is going on to calibrate the models against empirical estimates and eventually to marshal them for characterizing long-term evolutionary impacts on individual and aggregate patterns of survival.
