Abstract: Recently there has been a great interest in the analytic understanding of the behavior of large server systems with randomized routing.

Abstract: We consider the model-free reinforcement learning problem and study the popular Q-learning algorithm with linear function approximation for finding the optimal policy.

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: Given a graph $G$ with one source vertex $s$ and several target vertices, a shortest path tree rooted at $s$ is a subgraph of $G$ that preserves distances from $s$ to each of the target vertices.

Abstract:  The task of manipulating randomness has been a subject of intense investigation in computational complexity with dispersers, extractors, pseudorandom generators, condensers, mergers being just a few of the objects of interest.