Student Research Topics for 2019/20

We are excited to share with you the research topics of the first cohort of Compass students.

Supervised by the Institute for Statistical Science:

Alessio Zakaria is working with Vladislav Tadic and Christophe Andrieu on a project entitled Online Methods for Complex Stochastic Optimization Problems. 

Alexander Modell is working with Patrick Rubin-Delanchy on a project entitled Statistical analysis of large graphs.

Daniel Williams is working with Song Liu on a project entitled Estimating Probabilistic Models on Curved Surfaces using Score Matching.

Dom Owens is working with Haeran Cho and Edmund Barter (CheckRisk) on a project entitled Network analysis of high-dimensional financial data with non-linear and time-evolving dependence. This project is in partnership with CheckRisk.

Doug Corbin is working with Anthony Lee and Mathieu Gerber on a project entitled Non-parametric supervised learning. In particular, Doug’s work focusses on the application of nonparametric methods to sequential decision making problems.

Jake Spiteri is working with Mathieu Gerber and Anthony Lee on a project entitled Kernel methods for non-parametric supervised learning.

Michael Whitehouse is working with Nick Whiteley on a project entitled Dynamics and Uncertainty in Financial Factor Models.

The following projects are supervised in collaboration with the Institute for Statistical Science (IfSS) and our other internal partners at the University of Bristol:

Andrea Becsek is working with William Browne and George Leckie from the School of Education, Christophe Andrieu (IfSS) and Stephen Welbourne (Sparx) on a project entitled Longitudinal Statistical Modelling of a Personalised Learning System for Mathematics students. This project is in partnership with Sparx.

Mauro Camara Escudero is working with Christophe Andrieu (IfSS) and Mark Beaumont from the School of Biological Sciences on a project entitled Using Variational approaches to perform computationally efficient ‘divide and conquer’ Monte Carlo inference on demographic models.

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