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.

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: A famous result of Ahlswede in the theory of Arbitrarily Varying Channels (AVCs) stated that the deterministic coding capacity of an AVC is either zero or is equal to its random coding capacity.

Abstract: In a recent breakthrough work Kasper Green Larsen, Omri Weinstein and Huacheng Yu [1] proved the first superlogartithmic lowerbound for a dynamic data structure problem.

Abstract: Suppose we have N time series available where one time-series could be causally dependent on others. For example, such dependence can be found in economic data or weather data.