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PhD Stats Defence: Incorporating contact network uncertainty in individual level models of infectious disease within a bayesian framework

CANDIDATE:  WALEED ALMUTIRY ABSTRACT: Individual-level infectious disease models enable the study of transmission mechanisms of infectious disease while accounting for heterogeneity within the population. As well as via covariates, such heterogeneity is often best modelled through a contact network or a series of networks. However, contact network and exact time of infection (and removal) for individuals are often completely or partially unobserved.

Computational Statistics Research Talk - Saied Amiri

Title: Revisiting the clustering methods with the application for the BIG DATA Abstract: According to the Moore’s and Kryder’s laws, the computational power and information storage increase exponentially, so researchers and workers need more sophisticated and advanced methods for managing and interrogating Big Data.

PhD Math Defence: Complex Matrix Scalings, Extremal Permanents, and the Geometric Measure of Entanglement

CANDIDATE:   GEORGE HUTCHINSON ABSTRACT: An n x n matrix with complex entries is said to be doubly quasi-stochastic (DQS) if all row and column sums are equal to one. Given a positive definite matrix A and a diagonal matrix D, we say that D*AD is a (complex matrix) scaling of A if D*AD is doubly quasi-stochastic. Motivated by a result of Pereira and Boneng concerning the application of complex matrix scalings to the geometric measure of entanglement of certain symmetric states, we embark upon an investigation of these scalings and their properties.

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