An essential part of an expert-finding task, such as matching reviewers to submitted papers, is the ability to model the expertise of a person based on documents. We evaluate several measures of the association between a document to be reviewed and an author, represented by their previous papers. We compare language-model-based approaches with a novel topic model, Author-Persona-Topic (APT). In this model, each author can write under one or more "personas," which are represented as independent distributions over hidden topics. Examples of previous papers written by prospective reviewers are gathered from the Rexa database, which extracts and disambiguates author mentions from documents gathered from the web. We evaluate the models using a reviewer matching task based on human relevance judgments determining how well the expertise of proposed reviewers matches a submission. We find that the APT topic model outperforms the other models.