Grace Wahba’s journey into statistics began in high school, where she developed a deep interest in math and science. Despite being told by her guidance counselor not to apply to Cornell University, she was accepted. At Cornell, she learned it was one of the few places where a woman could study mathematics alongside some of the world’s greatest mathematicians—an opportunity that helped shape her future career.
During her senior year at Cornell, Wahba married, and two months after she graduated, she had her son. After moving to Washington, DC, she joined Operations Research and later moved to IBM. While working full time and raising a young son, Wahba continued her education, earning a master’s degree in mathematics from the University of Maryland.
When IBM relocated her team to San Jose, California, she applied to go to Stanford University. She went on to earn her PhD in statistics in 1966 and joined the University of Wisconsin-Madison in 1967 as the first female faculty member in the department of statistics. She remained there for 51 years, before retiring in 2018 as I. J. Schoenberg-Hilldale Professor Emerita.
Wahba was an early pioneer in the use of nonparametric regression modeling. Recent advances in computing and availability of large data sets have further popularized these models—especially under the guise of machine learning algorithms like gradient boosting and neural networks. Nevertheless, the use of smoothing splines remains a mainstay of nonparametric regression.
In seminal research that began in the early 1970s, Wahba developed theoretical foundations and computational algorithms for fitting smoothing splines to noisy data. Her sustained contributions led to a rigorous mathematical framework and practical techniques for extracting meaningful patterns from imperfect observations—a challenge that lies at the heart of statistical analysis.
In addition to her stellar research contributions, Wahba has been a mentor and role model for women in mathematics and statistics throughout her career. She’s advised numerous PhD students, who’ve gone on to become leading figures in the field, with several department chairs and one member of the National Academy of Sciences among her academic descendants.
Wahba’s achievements have been recognized with numerous honors, including memberships in the National Academy of Sciences and the American Academy of Arts and Sciences. In 2021, the Institute of Mathematical Statistics established the Grace Wahba Award and Lecture in her honor, and in 2025, she received the International Prize in Statistics for her groundbreaking work on smoothing splines, which has transformed modern data analysis and machine learning.
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