Jump to content

Rebecca Willett

From Wikipedia, the free encyclopedia

Rebecca Willett is an American statistician and computer scientist whose research involves machine learning, signal processing, and data science. She is a professor of statistics and computer science at the University of Chicago.[1]

Willett has a Ph.D. in electrical and computer engineering from Rice University, completed in 2005. She worked as a faculty member in electrical and computer engineering at Duke University from 2005 until 2013, when she moved to the University of Wisconsin–Madison.[1] She moved again to the University of Chicago in 2018.[2]

Her research has included machine learning methods for the analysis of corn crop quality,[3] and weather patterns.[4] She was named a SIAM Fellow in the 2021 class of fellows, "for contributions to mathematical foundations of machine learning, large-scale data science, and computational imaging",[5] and an IEEE Fellow in 2022 "for contributions to the foundations of computational imaging and large-scale data science".[6] In 2022, she was elected Vice Chair of the Society for Industrial and Applied Mathematics Activity Group on Imaging Science (SIAM SIAG/IS).[7]

References

[edit]
  1. ^ a b Profile: Rebecca Willett, University of Chicago Computer Science Department
  2. ^ DSP Alum Rebecca Willett Joins University of Chicago, Digital Signal Processing at Rice University, June 25, 2018
  3. ^ "New Silage App Designed to Improve Corn Silage Quality", Dairy Herd Management, June 1, 2018
  4. ^ Mitchum, Rob (September 11, 2018), "Multi-university collaboration will use climate data analysis to improve regional forecasts", UChicago News
  5. ^ "SIAM Announces Class of 2021 Fellows", SIAM News, Society for Industrial and Applied Mathematics, March 31, 2021, retrieved 2021-04-03
  6. ^ 2022 newly elevated fellows (PDF), IEEE, archived from the original (PDF) on November 24, 2021, retrieved 2022-03-02
  7. ^ "SIAM Activity Groups Election Results". SIAM News. Retrieved 2022-05-28.
[edit]