Nan Laird was born in Gainesville, Florida, to a schoolteacher mother and a political science professor father. Her favorite subject in school was math, which she initially majored in at Rice University in 1961. But she grew dissatisfied with her studies, especially because she was the sole female in her mathematics course. She yearned to do something more practical than the theory she was learning. She switched her major to French, left Rice, and moved to New York City with her husband.
In the late 60s, she moved again, this time to Georgia, and that’s where Laird picked up her studies—this time in computer science—hoping to find that elusive element of practicality missing in mathematics. But instead of computers, she found it in her statistical decision theory course. Hooked on statistics, she switched majors a final time and graduated with a BS in statistics in 1969.
Laird entered Harvard University as a doctoral student in statistics in 1971. She earned her PhD after four years and joined the faculty as an assistant professor in biostatistics. She remained at Harvard until her retirement four decades later.
At Harvard, Laird began work that would transform how researchers analyze real-world data. In the 80s, she and Jim Ware developed random effects models that allowed statisticians to work with irregular measurements, missing data, and conditions that traditional methods could not handle. Their framework was described in their 1982 Biometrics paper, “Random Effects Models for Longitudinal Data: An Overview of Recent Results.”
Throughout her career, Laird has published more than 400 papers and won several awards, including the International Prize in Statistics. She is a fellow of the American Association for the Advancement of Science, a fellow of the ASA, a fellow of the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute.
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