William S. Cleveland

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William Swain Cleveland II (born 1943) is an American computer scientist and Professor of Statistics and Professor of Computer Science at Purdue University, known for his work on data visualization, particularly on nonparametric regression[1] and local regression.[2]

Biography[edit]

Cleveland obtained his AB in Mathematics mid 1960s from Princeton University, where he graduated under William Feller. For his PhD studies in Statistics he moved to Yale University, where he graduated under Leonard Jimmie Savage.[3]

After graduation Cleveland started at Bell Labs, where he was staff member of the Statistics Research Department and Department Head for 12 years. Eventually he moved to the Purdue University, where he became Professor of Statistics and Courtesy Professor of Computer Science. In 1982 he was elected as a Fellow of the American Statistical Association.[4]

His research interests are in the fields of "data visualization, computer networking, machine learning, data mining, time series, statistical modeling, visual perception, environmental science, and seasonal adjustment."[5]

Selected publications[edit]

  • Cleveland, William S. The elements of graphing data. Monterey, CA: Wadsworth Advanced Books and Software, 1985.
  • Cleveland, William S. Visualizing data. Hobart Press, 1993.

Articles, a selection:[6]

References[edit]

  1. ^ Armitage, Peter, Geoffrey Berry, and John NS Matthews. Statistical methods in medical research. John Wiley & Sons, 2008.
  2. ^ Venables, William N., and Brian D. Ripley. Modern applied statistics with S. Springer Science & Business Media, 2002.
  3. ^ William S. Cleveland, CV, at stat.purdue.edu. Accessed 10-04-2015.
  4. ^ View/Search Fellows of the ASA, accessed 2016-10-15.
  5. ^ William S. Cleveland: Bio, at stat.purdue.edu. Accessed 10-04-2015.
  6. ^ William S. Cleveland, Google scholar profile.

External links[edit]