As we live our daily lives, our surroundings know about it. Our surroundings consist of people, but also our electronic devices. Our mobile phones, for example, continuously sense our movements and interactions. This socio-geographic data could be continuously captured by hundreds of millions of people around the world and promises to reveal important behavioral clues about humans in a manner never before possible. Mining patterns of human behavior from large-scale mobile phone data has deep potential impact on society. For example, by understanding a community's movements and interactions, appropriate measures may be put in place to prevent the threat of an epidemic. The study of such human-centric massive datasets requires advanced mathematical models and tools. In this thesis, we investigate probabilistic topic models as unsupervised machine learning tools for large-scale socio-geographic activity mining. We first investigate two types of probabilistic topic models for large-scale location-driven phone data mining. We propose a methodology based on Latent Dirichlet Allocation, followed by the Author Topic Model, for the discovery of dominant location routines mined from the MIT Reality Mining data set containing the activities of 97 individuals over the course of a 16 month period. We investigate the many possibilities of our proposed approach in terms of activity modeling, including differentiating users with high and low varying lifestyles and determining when a user's activities fluctuate from the norm over time. We then consider both location and interaction features from cell tower connections and Bluetooth, in single and multimodal forms for routine discovery, where the daily routines discovered contain information about the interactions of the day in addition to the locations visited. We also propose a method for the prediction of missing multimodal data based on Latent Dirichlet Allocation. We further consider a supervised approach for day type and student type classification using similar socio-geographic features. We then propose two new probabilistic approaches to alleviate some of the limitations of Latent Dirichlet Allocation for activity modeling. Large duration activities and varying time duration activities can not be modeled with the initially proposed methods due to problems with input and model parameter size explosion. We first propose a Multi-Level Topic Model as a method to incorporate multiple time duration sequences into a probabilistic generative topic model. We then propose the Pairwise-Distance Topic Model as an approach to address the problem of modeling long duration activities with topics. Finally, we consider an application of our work to the study of influencing factors in human opinion change with mobile sensor data. We consider the Social Evolution Project Reality Mining dataset, and investigate other mobile phone sensor features including communication logs. We consider the difference in behaviors of individuals who change political opinion and those who do not. We combine several types of data to form multimodal exposure features, which express the exposure of individuals to others' political opinions. We use the previously defined methodology based on Latent Dirichlet Allocation to define each group's behaviors in terms of their exposure to opinions, and determine statistically significant features which differentiate those who change opinions and those who do not. We also consider the difference in exposure features of individuals that increases their interest in politics versus those who do not. Overall, this thesis addresses several important issues in the recent body of work called Computational Social Science. Investigations principled on mathematical models and multiple types of mobile phone sensor data are performed to mine real life human activities in large-scale scenarios.