Human behavior in the real world is a difficult thing to study: it is not possible to have human observers follow someone around all day, and surveys often tend to be biased and unreliable. On the other hand, sensor data is easy to collect but inferring human behavior from this data is still a challenging problem. In this talk, I will present some of the computational methods we have developed for inferring the micro-level dynamics of human interactions as well as the macro-level latent social network structure from local, noisy sensor observations. By studying the micro and macro levels simultaneously we are able to explore the relationship between interaction dynamics (local behavior) and network prominence (a global property), and can identify the behavioral correlates of tie strengths within a network. We believe these methods have the potential to allow more quantitative inquiry into human behavior and social dynamics. They will also enable us to develop socially aware ubiquitous computing systems that are cognizant of and responsive to users' engagement with their social environment.