Nan Laird — LLMpedia

Nan Laird
Name	Nan Laird
Birth date	1943
Birth place	Baltimore
Nationality	United States
Fields	Statistics
Workplaces	Harvard University
Alma mater	University of Pennsylvania; Harvard University
Doctoral advisor	Arthur P. Dempster; Nan Laird
Known for	Expectation–maximization algorithm, Longitudinal data analysis

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Nan Laird Nan Laird is an American statistician noted for foundational work in biostatistics, longitudinal studies, and algorithms for incomplete data. She is a longtime professor at Harvard University and a former chair of the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. Her research has influenced methods used by agencies such as the National Institutes of Health, Centers for Disease Control and Prevention, and international collaborators at the World Health Organization.

Early life and education

Laird was born in Baltimore and completed undergraduate studies at University of Pennsylvania, where she studied mathematics and developed interests intersecting with scholars from Princeton University and Massachusetts Institute of Technology. She pursued graduate work at Harvard University, earning a Ph.D. under advisors connected to scholars at University of California, Berkeley and linking to methodological lineages including contributors from University of Washington and Columbia University. During training she engaged with faculty who collaborated with investigators from the National Bureau of Economic Research and the Social Science Research Council.

Laird joined the faculty at Harvard University in the Harvard T.H. Chan School of Public Health and served as Chair of the Department of Biostatistics where she worked with colleagues from Brigham and Women's Hospital, Dana-Farber Cancer Institute, and the Massachusetts General Hospital. She held visiting appointments at Stanford University, University of Michigan, and Yale University, and collaborated with researchers at the Johns Hopkins Bloomberg School of Public Health and the University of Minnesota School of Public Health. Laird served on editorial boards of journals associated with the American Statistical Association and the International Biometric Society and contributed to panels convened by the National Research Council and Institute of Medicine.

Laird is best known for work on the Expectation–maximization algorithm developed with collaborators linked to the University of Chicago and for advances in mixed-effects models applied to longitudinal studies from clinical trials at institutions like Mayo Clinic and population cohorts coordinated with the Framingham Heart Study. Her methodological contributions include development of random-effects models, marginal modeling approaches, and robust variance estimation used in analyses at the Food and Drug Administration and in multicenter studies funded by the National Cancer Institute. Laird's techniques for handling missing data and correlated responses drew upon and influenced work by researchers at Carnegie Mellon University, Northwestern University, and Rice University. Her methods have been implemented in statistical software developed by teams at Bell Labs and the R Project for Statistical Computing, and have been applied in collaborations with investigators from University College London and Imperial College London.

Laird's recognitions include election to the National Academy of Sciences sphere of members working in statistics, awards from the American Statistical Association, and honors from the International Statistical Institute. She received fellowships and medals that align with distinctions given by the Institute of Mathematical Statistics, the Royal Statistical Society, and the MacArthur Fellows Program-style competitive awards in scientific leadership. Laird has been honored with named lectures at institutions such as Columbia University, University of California, Los Angeles, and Duke University, and invited keynote roles at meetings of the Joint Statistical Meetings and the Royal Society's statistical symposia.

Laird authored and coauthored influential articles in journals associated with the American Journal of Epidemiology, Journal of the American Statistical Association, and Biometrika. Her papers on the Expectation–maximization algorithm and on mixed models appear alongside work by investigators from Princeton University and Brown University. She contributed chapters to volumes published by the Institute of Mathematical Statistics and coedited proceedings for meetings organized by the International Biometric Society. Her methods are cited in guidelines produced by the World Health Organization and the European Medicines Agency.

Laird's mentorship of students who later joined faculties at Harvard Medical School, Stanford School of Medicine, University of Washington School of Public Health, and Yale School of Public Health has created a legacy reflected across centers such as Fred Hutchinson Cancer Research Center and the Vanderbilt University Medical Center. Her methodological lineage connects to practitioners working at the National Institutes of Health and policy analysts at the Centers for Disease Control and Prevention. Laird's influence persists through software contributions to the R Project for Statistical Computing ecosystem and through training programs at the Harvard School of Public Health.