Nearly a quarter century ago, the basic idea of multiple imputation was proposed as a way to deal with missing values due to nonresponse in sample surveys. Since that time, the essential formulation has expanded to be proposed for use in a remarkably broad range of empirical problems, from many standard social science and biomedical applications involving missing data in surveys and experiments, to nonstandard survey and experimental applications, such as preserving confidentiality in public-use surveys and dealing with noncompliance and “censoring due to death” in clinical trails, to common “hard science” applications such as dealing with below-threshold chemometric measurements, to other scientific or medical applications such as imaging brains for tumors, and exploring the genetics of schizophrenia. The purpose of this presentation is to provide some links to this broad range of applications and to indicate the associated computing requirements, primarily using examples in which I am currently involved.