Abstract : We study an appointment scheduling (AS) problem where a finite pool of jobs must be scheduled at a single server queue over a finite fixed time horizon.

Abstract: For applications with geo-distributed client base such as Twitter, Amazon, Wikipedia, etc., in order to provide an always-on service with uniform low latency, application data needs to be replicated in multiple locations across the world

Abstract: Many recent advancements in computation have come from harnessing the power of very large sets of data.  This leads to an algorithmic question: how can we store a large dataset so that we can quickly query it later on? 

Abstract: k-means is a popular clustering objective that is NP-hard in theory but often optimized efficiently in practice.

Abstract: We consider the problem of interactively visualizing a distributed tabular dataset with billions of rows.