New challenges for scientists have emerged in the past several years as the size of data generated from simulations has experienced an exponential growth. One major factor that is contributing to the growth of data size is the increasingly widespread ability to perform very large scale time-varying simulations. Although intensive research has been undertaken to optimize the performance of visualizing very large data sets, most of the existing methods have not targeted time-varying data. What is lacking is a comprehensive study of an end-to-end solution to facilitate efficient processing of large-scale time-varying data. More specifically, we look to minimize visualization computation cost, to minimize data transfer cost, and to maximize the user’s ability to query the underlying data. In this talk, I will give an overview of the algorithms for visualizing large scale time-varying data that are developed at the Ohio State University. I will address the following issues in particular: (1) Lossless spatio-temporal data encoding and indexing schemes allowing for interactive visualization of time-varying data at arbitrary spatial and temporal scales. (2) Run time data reduction, including efficient visibility culling of time-varying isosurfaces, and data structures to identify data blocks that exhibit high temporal coherence (3) Time-varying feature tracking and visualization, including high dimensional data projection and tracking using high dimensional geometry.