Introduction

I am a second year student at the STOR-i Centre for Doctoral Training at Lancaster University, a four year doctoral training programme in statistics and operational research. My PhD project focuses on uncertainty quantification within large-scale computer simulation models and I am supervised by Lucy Morgan, Andrew Titman, and Richard Williams at Lancaster University and Susan Sanchez at the Naval Postgraduate School in Monterey, California.

Research

Input models that drive stochastic simulations are often estimated via observations collected from a real-world system. The finiteness of this data introduces a source of uncertainty into the simulation which propagates through to the output giving rise to an error known as input uncertainty. If this propagation of input model uncertainty is not considered in simulation output analysis, then crucial and expensive decisions are at risk of being made with misleading levels of confidence.

Methods for quantifying input uncertainty exist for simulation models with both homogeneous and non-homogeneous input processes, and many of these methods also provide ways for quantifying the contribution of each input model to the overall input uncertainty. However advances in computing power and modelling paradigms means that stochastic simulation is being used to model and analyse increasingly complex systems and the current input uncertainty quantification techniques do not scale well to large-scale simulation models that contain many input models. The technical question of interest is how to tackle the problem of scalability for input uncertainty quantification techniques with an overarching goal of providing decision makers with better risk assessments.

Interests

  • Stochastic Simulation
  • Input Uncertainty Quantification
  • Data Farming
  • Design of Experiments

Conferences

  • STOR-i Annual Conference 2018, 2019, 2020
  • Winter Simulation Conference 2019

Graduate Teaching Assistant Modules

  • MSCI101 - Statistics and Computing for Management
  • MSCI103 - Introduction to Operational Research
  • MATH230 - Probability II

Education

  • Durham University, BSc Mathematics (First-Class Honours)
  • London School of Economics, MSc Applicable Mathematics (Distinction)
  • Lancaster University, MRes Statistics and Operational Research (Distinction)

STOR-i

The STOR-i Centre for Doctoral Training, a joint venture between the departments of Mathematics and Statistics and Management Science, offers a four-year PhD programme in Statistics and Operational Research (STOR) developed and delivered with industrial partners. To read more click here

Centres for Doctoral Training, as funded by the Engineering and Physical Sciences Research Council, offer exciting and innovative learning environments to give students the opportunity to develop and carry out their PhD-level research.