July 14, 2025
Grace Wahba ’56, a pioneer in developing statistical methods for smoothing noisy data, has been awarded the International Prize in Statistics Award for groundbreaking work on smoothing splines, which transformed data analysis and machine learning.
Her contributions in nonparametric regression laid the foundation for modern statistical techniques that power machine learning algorithms such as gradient boosting and neural networks, according to the International Prize in Statistics Foundation, which administers the award.
Often called “the mother of smoothing splines,” Wahba has conducted work that has influenced climate science, medical imaging, and machine learning, including the development of support vector machines. She is also the namesake behind “Wahba’s problem,” an applied mathematics problem she first posed in 1965 to determine how to orient a satellite to a fixed point, like a known spot on the moon, based on the satellite’s position relative to specific stars.
Wahba will receive the $80,000 prize in October at the World Statistics Congress, organized by the International Statistical Institute.
Wahba received a bachelor’s degree in mathematics from Cornell in 1956. Later in her career, she worked for IBM and, as a working, single mother, earned a Ph.D. in mathematics at Stanford in 1966. A year later, Wahba became the first female faculty member in the Statistics Department at the University of Wisconsin-Madison, and remained there for the next half century, retiring in 2018. She was elected to the American Academy of Arts and Sciences in 1997 and to the National Academy of the Sciences in 2000. She is a fellow of the American Association for the Advancement of Science (AAAS), the American Statistical Association (ASA), and the Institute of Mathematical Sciences (IMS). In 2021, IMS established the Grace Wahba Award and Lecture in her honor. Wahba was the inaugural recipient of the Distinguished Alumni Award from the Cornell Department of Statistics and Data Science in 2009.