Fang Liu is a Chinese-American statistician and data scientist whose research topics include differential privacy, statistical learning theory, Bayesian statistics, regularization, missing data, and applications in biostatistics. She is a professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame.^[1][2]

Liu was talented in mathematics as a child, competed in mathematics competitions, and wanted to become a mathematician, but was discouraged from doing so by her parents, who wanted her to become a physician. As a compromise, she studied biology at Peking University,^[2] where she earned a bachelor's degree in 1997.

She began her graduate studies at Iowa State University intending to study genetics, but quickly switched to a program in statistics,^[2] and earned a master's degree there in 1999, and a Ph.D. from the University of Michigan in 2003.^[1] Her dissertation, Bayesian Methods for Statistical Disclosure Control in Microdata, involved both data privacy and Bayesian statistics, and was supervised by Roderick J. A. Little.^[3]

After completing her doctorate, she became a researcher at the Merck Research Laboratories. She returned to academia, joining the Notre Dame faculty, in 2011.^[4]

Liu was named a Fellow of the American Statistical Association in 2021, "for novel contributions to differentially private synthetic data and Bayesian modeling; for outstanding interdisciplinary research in clinical and public health studies; for leadership in education and training; and for service to the profession".^[4]