A person’s career trajectory is composed of her/his past work or educational affiliations (institutions) at different points of times. Knowing people’s, especially scholars’, career trajectories can help the government make more scientific strategies to allocate resources and attract talent and help companies make smart recruiting plans. It could also support individuals find appropriate co-researchers or job opportunities. The paper focuses on inferring career trajectories in the academic social network. For about 1/3 of authors not having any affiliations in the dataset, we need to infer the missings at various years. Traditional affiliation/location inferring methods focus on inferring a stationary location (one and only) for a person. Nevertheless, people won’t stay at a place all their lives. We propose a Space-Time Factor Graph Model (STFGM) incorporating spatial and temporal correlations to fulfill the challenging and new task of inferring temporal locations. Experiments show our approach significantly outperforms baselines. At last, as case study, we develop several applications based on our approach which demonstrate the effectiveness further.