Diane Marie Lambert is an American statistician known for her work on zero-inflated models, a method for extending Poisson regression to applications such as the statistics of manufacturing defects in which one can expect to observe a large number of zeros.[1] A former Bell Labs Fellow, she is a research scientist for Google, where she lists her current research areas as "algorithms and theory, data mining and modeling, and economics and electronic commerce".[2]
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Education and career
Lambert earned her Ph.D. in 1978 from the University of Rochester. Her dissertation, supervised by W. Jackson Hall, was P-Values: Asymptotics and Robustness.[3] In the early part of her career, she worked as a faculty member at Carnegie Mellon University. As an assistant professor there, she did pioneering work on the confidentiality of statistical information.[4] She earned tenure at Carnegie Mellon, but moved to Bell Labs in 1986. At Bell Labs, she became head of statistics, and a Bell Labs Fellow. She moved again to Google in 2005.[5][6]
Recognition
Lambert became a Fellow of the American Statistical Association in 1991.[7] She is also a Fellow of the Institute of Mathematical Statistics,[8] was executive secretary of the institute from 1990 to 1993,[9] and was one of the institute's Medallion Lecturers in 1995.[10]
References
- ^ Lewis-Beck, Michael; Bryman, Alan E.; Liao, Tim Futing (2003), The SAGE Encyclopedia of Social Science Research Methods, Sage, p. 830, ISBN 9781452261454
- ^ "Diane Lambert", Research at Google, retrieved 2017-11-25
- ^ Diane Lambert at the Mathematics Genealogy Project
- ^ Behseta, Sam; Slavković, Aleksandra (November 2013), "Interview with Steve Fienberg", Chance, 26 (4), American Statistical Association: 18–29, doi:10.1080/09332480.2013.868752, S2CID 61142854, retrieved 2017-11-25
- ^ "Diane Lambert, Research Scientist, Google", Speaker biography for Computefest 2018, Harvard University, archived from the original on 2017-12-01, retrieved 2017-11-25
- ^ National Research Council Committee on the Analysis of Massive Data (2013), Frontiers in Massive Data Analysis, National Academies Press, p. 175, ISBN 9780309287814
- ^ ASA Fellows list, American Statistical Association, archived from the original on 2017-12-01, retrieved 2017-11-25
- ^ Honored Fellows, Institute of Mathematical Statistics, archived from the original on 2014-03-02, retrieved 2017-11-25
- ^ Past Executive Committee Members, Institute of Mathematical Statistics, archived from the original on 2012-02-08, retrieved 2017-11-25
- ^ Medallion Lectures, Institute of Mathematical Statistics, archived from the original on 2016-08-10, retrieved 2017-11-25
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This page was last edited on 4 April 2024, at 00:51