Tian Zheng is a Chinese-American applied statistician whose work concerns Bayesian modeling and sparse learning of complex data from applications including social networks, bioinformatics, and geoscience.
[3] Her interest in statistics was sparked by a junior-year project in medical data processing.
[2] She went to Columbia University for graduate study in statistics, and earned a master's degree in 2000 and a Ph.D. in 2002.
[3] Her dissertation, Multiple-Marker Screening Approach Towards the Study of Complex Traits in Human Genetics, was supervised by Shaw-Hwa Lo.
[3] She was named to the 2022 class of Fellows of the Institute of Mathematical Statistics, for "fundamental research on sparsity and variable importance, and for significant contributions to social network theory and to genetics".