Faye Williamson

PhD student

Profile

I graduated from Lancaster University in July 2013 with a first class BSc honours degree in Mathematics. Throughout my undergraduate studies, I always maintained a balance between the pure and statistical elements of mathematics, but it progressively became apparent that statistics is of particular interest to me because of its useful applications in many real-life and industrial situations.

I was eager to learn more about these applications; therefore I completed an internship with STOR-i during summer 2012 which proved invaluable in confirming my aspirations to progress in an innovative research environment.During summer 2013, I was a Data Scientist Intern at Ascribe Ltd. where I worked alongside the Business Intelligence team at Ascribe, Intel, Leeds Teaching Hospitals and Two10degrees on the Leeds A&E Big Data Project funded by Microsoft to improve the analytic capabilities of Symphony; an unscheduled care product. I successfully completed the MRes component of the STOR-i programme in September 2014 which was a great way of opening my eyes to many diverse areas of Statistics and Operational Research that I had not previously encountered during my undergraduate studies. From October 2014, I will pursue my research interests in medical statistics and be working on a PhD project which involves developing methodology for clinical trials with a particular focus on utilising Bayesian bandit models. This project will be under the joint supervision of Dr. Thomas Jaki and Dr. Peter Jacko (in collaboration with Sofia Villar from the Medical Research Council Biostatistics Unit, Cambridge).I am greatly looking forward to contributing to the evolving and deserved success of the STOR-i Centre for Doctoral Training at Lancaster University and feel privileged to be part of one of few such centres in the UK.

Homepage: http://www.lancaster.ac.uk/pg/williasf/

A Bayesian adaptive design for clinical trials in rare diseases
Williamson, F., Jacko, P., Villar, S.S., Jaki, T.F. 09/2017 In: Computational Statistics and Data Analysis. 113, p. 136-153. 17 p.
Journal article