Nowadays, large-scale human activity data from online social platforms, such as Twitter, Facebook, Reddit, Stackoverflow, Wikipedia and Yelp, are becoming increasing available and in increasing spatial and temporal resolutions. Such data provide great opportunities for understanding and modeling both macroscopic (network level) and microscopic (node-level) patterns in human dynamics. Such data have also fueled the increasing efforts on developing realistic representations and models as well as learning, inference and control algorithms to understand, predict, control and distill knowledge from these dynamic processes over networks. It has emerged as a trend to take a bottom-up approach which starts by considering the stochastic mechanism driving the behavior of each node in a network to later produce global, macroscopic patterns at a network level. However, this bottom-up approach also raises significant modeling, algorithmic and computational challenges. In this talk, I will present machine learning framework for representing, modeling, and performing learning and inference for human activity data. The framework leverage methods from temporal point process theory, probabilistic graphical models and optimization, and often produce state-of-the-art results on various modeling and time-sensitive inference tasks.