Diane Lambert

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This article is about the statistician. For the fictional soap-opera character, see Diane Sugden. For the songwriter, see Every Road Leads Back to You.

Diane Marie Lambert is an American statistician known for her work on

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
electronic commerce".[2]

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

  1. ISBN 9781452261454
  2. ^ "Diane Lambert", Research at Google, retrieved 2017-11-25
  3. ^ Diane Lambert at the Mathematics Genealogy Project
  4. S2CID 61142854
    , retrieved 2017-11-25
  5. ^ "Diane Lambert, Research Scientist, Google", Speaker biography for Computefest 2018, Harvard University, archived from the original on 2017-12-01, retrieved 2017-11-25
  6. ISBN 9780309287814
  7. ^ ASA Fellows list, American Statistical Association, archived from the original on 2017-12-01, retrieved 2017-11-25
  8. ^ Honored Fellows, Institute of Mathematical Statistics, archived from the original on 2014-03-02, retrieved 2017-11-25
  9. ^ Past Executive Committee Members, Institute of Mathematical Statistics, archived from the original on 2012-02-08, retrieved 2017-11-25
  10. ^ Medallion Lectures, Institute of Mathematical Statistics, archived from the original on 2016-08-10, retrieved 2017-11-25
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