Applicants should have, or be expecting to obtain in the near future, a first class or good 2.1 honours degree (or equivalent) in mathematics, engineering or a mathematical science.
This project is suitable for someone who has an interest in Numerical Linear Algebra and Data Science.
Spectral clustering has proven to be one of the most prolific fields of research in network science and many effective methods have been derived for dividing a network into coherent communities using spectral information obtained from the adjacency matrix, or some other matrix closely associated with the network. But biclustering, where we attempt to link two separate sets of data, has received far less attention despite being equally important. We look to develop new methods and analysis for this problem, starting from recent work on the symmetric problem using a bistochastic representation of a network.
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