Explore recent advancements in diversity maximization for offline and composable coreset models in this 46-minute lecture by Sepideh Mahabadi from Microsoft Research. Delve into the concept of selecting a subset with maximum diversity from a set of points in a metric space, and its applications in data summarization, recommendation systems, and search. Examine the power of composable coresets as a tool for handling massive data across various computational models. Gain insights into the latest research findings and their implications for practical tasks involving large-scale data analysis and algorithm design.