Allison Chaney will present her FPO, Computational Methods for Exploring Human Behavior", on Thursday, 9/8/2016 at 3:00pm in CS 402. The members of her committee are David Blei (adviser), readers: David Blei and Barbara Engelhardt; Examiners: David Blei, Elad Hazan, and Brandon Stewart (Sociology). A copy of her thesis is available in Room 310. Everyone is invited to attend her talk. The abstract follows below: Researchers and analysts from many diverse fields are interested in unstructured observations of human behavior; this variety of data is constantly increasing in quantity. In this dissertation, we describe a suite of computational methods to assist investigators in interpreting, organizing, and exploring this data. We develop two Bayesian latent variable models for human-centered applications; specifically, we rely on additive Poisson models, which allow behavior to be associated with various sources of influence. Given observed data, we estimate the posterior distributions of these models with scalable variational inference algorithms. These models and inference algorithms are validated on real-world data. Developing statistical models and corresponding inference algorithms only addresses part of the needs of investigators. Non-technical researchers faced with analyzing large quantities of human behavior data are not able to use the results of inference algorithms without tools to translate estimated posterior distributions into accessible visualizations, browsers, or navigators. We present visualization based on an underlying statistical model as a first-class research problem, and provide principles to guide the construction of these systems. We demonstrate these principles with exploratory tools for two latent variable models. By considering the interplay between developing statistical models and tools for visualization, we are able to develop computational methods that provide for the full needs of investigators interested in exploring human behavior.