ERC Starting Grant · 2018
The Power of Randomization in Uncertain Environments
Much of the research on the foundations of graph algorithms is carried out under the assumption that the algorithm has full knowledge of the input data. In spite of the theoretical appeal and simplicity of this setting, the assumption that the algorithm has full knowledge does not always hold. Indeed uncertainty and partial knowledge arise in many settings. One example is where the data is very large, in which case even reading the entire data once is infeasible, and sampling is required. Another example is where data changes occur over time (e.g., social networks where information is fluid). A third example is where processing of the data is distributed over computation nodes, and each…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.