Stochastic processes with dynamics at multiple time scales are pervasive in science and engineering. In neuroscience experiments, the spiking dynamics of neurons can exhibit variability both within a given trial (fast time scale) and across trials (fast time scale). The behavior of users on social media often exhibits interesting dynamics within a day (fast time scale), as well as dynamics across days of the year (slow time scale
The breadth of technology developed over the past 20 years to record neural activity is reflective of the diversity of temporal and spatial scales that characterize neural dynamics. We are developing efficient algorithms for estimation, inference and fusion in multiscale stochastic process models of neural activity. This works is at the interface of algorithmic computer science (trees, data-structures), classical filtering (e.g. Kalman) and statistical inference.