  The large volumes of data generated by human activities such as online
purchases, health records, spatial mobility etc. are stored as a sequence of
events over a continuous time. Learning deep learning methods over such
sequences is a non-trivial task as it involves modeling the ever-increasing
event timestamps, inter-event time gaps, event types, and the influences
between events -- within and across different sequences. This situation is
further exacerbated by the constraints associated with data collection e.g.
limited data, incomplete sequences, privacy restrictions etc. With the research
direction described in this work, we aim to study the properties of
continuous-time event sequences (CTES) and design robust yet scalable neural
network-based models to overcome the aforementioned problems. In this work, we
model the underlying generative distribution of events using marked temporal
point processes (MTPP) to address a wide range of real-world problems.
Moreover, we highlight the efficacy of the proposed approaches over the
state-of-the-art baselines and later report the ongoing research problems.
