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Grace Goldsmith Wahba (born August 3, 1934) is an American statistician and now-retired I. J. Schoenberg-Hilldale Professor of Statistics at the University of Wisconsin–Madison. She is a pioneer in methods for smoothing noisy data. Best known for the development of generalized cross-validation and "Wahba's problem", she has developed methods with applications in demographic studies, machine learning, DNA microarrays, risk modeling, medical imaging, and climate prediction.
Biography
Grace Wahba had an interest in science from an early age, when she was in Junior High she was given a chemistry set. At this time she also interested in becoming an engineer.
Wahba studied at Cornell University for her undergraduate degree. When she was there women were severely restricted in their privileges, for example she was required to live in a dorm and had a curfew. She received her bachelor's degree in 1956 and a master's degree from the University of Maryland, College Park in 1962. She worked in industry for several years before receiving her doctorate from Stanford University in 1966 and settling in Madison in 1967.
She is the author of Spline Models for Observational Data. She retired in August 2018 from the University of Wisconsin-Madison.
Honors and awards
She was elected to the United States National Academy of Sciences in 2000 and received an honorary degree of Doctor of Science from the University of Chicago in 2007.
Wahba is a member of the National Academy of Sciences, and a fellow of several academic societies including the American Academy of Arts and Sciences, the American Association for the Advancement of Science, the American Statistical Association, and theInstitute of Mathematical Statistics. Over the years she has received a selection of notable awards in the statistics community:
- R. A. Fisher Lectureship, COPSS, August 2014
- Gottfried E. Noether Senior Researcher Award, Joint Statistics Meetings, August 2009
- Committee of Presidents of Statistical Societies Elizabeth Scott Award, 1996
- First Emanuel and Carol Parzen Prize for Statistical Innovation, 1994