Modeling event dynamics is central to many disciplines. In particular, point processes models have been applied to explain patterns seen in event arrival times. Such data often exhibits heterogeneous and sporadic trends, which is challenging to conventional methods. It is reasonable to assume that there exists a hidden state process that drives different event dynamics at different states. In this paper, we propose a Markov Modulated Hawkes Process (MMHP) model and develop corresponding inference algorithms. Numerical experiments using synthetic data and data from an animal behavior study demonstrate that MMHP with the proposed estimation algorithms consistently recover the true hidden state process in simulations, and separately captures distinct event dynamics with interesting social structure in real data.