Staff Profile

Roles and Responsibilities

Global Director

• International Student Recruitment
• Student Mobility 
• Engagement and Partnerships

School Administrative duties

• Peer Mentor Co-ordinator
• Mathematics and Statistics Outreach

Esteem Indicators

• Royal Statistical Society William Guy Lecturer 2019
• Advance HE National Teaching Fellow

Memberships

• Royal Statistical Society
• The International Environmetrics Society

Research Interests

Extremes

• Bayesian Inference for Environmental Extremes
• Extremes of Dependent Processes

Road Safety

• Statistical Modelling of Road Traffic Accidents
• Road Safety Hotspot Prediction

Statistics Education

• Technological Innovations in Statistics Education
  
• Research-Informed Teaching

Undergraduate Teaching (2022-2023)

MAS2903: Introduction to Bayesian Methods

MAS2906: Computational Probability and Statistics with R

MAS3091: Group Project

MAS8391: MMathStat Project

Postgraduate Teaching (2022-2023)

HSS8005: Introduction to Quantitative Methods

• Articles

  • Griffiths B, Fawcett L, Green AC. Bayesian Inference for Solar Flare Extremes. Space Weather 2022, 20(3), e2021SW002886.
  • Thorpe N, Fawcett L, Linsley M, Cherry B. Saving lives with safety cameras and statistical analysis. Public Sector Executive 2019, Oct/Nov 2019, 48-49.
  • Guo X, Wu L, Zou Y, Fawcett L. Comparative Analysis of Empirical Bayes and Bayesian Hierarchical Models in Hotspot Identification. Transportation Research Record 2019, 2673(7), 111-121.
  • Fawcett L. A severe forecast. Significance 2019, 16(6), 14-19.
  • Matthews JT, Newman K, Green AC, Fawcett L, Thorpe N, Kremer K. A decision support toolkit to inform road safety investment decisions. Municipal Engineer 2019, 172(1), 53-67.
  • Fawcett L. Using interactive Shiny applications to facilitate research-informed learning and teaching. Journal of Statistics Education 2018, 26(1), 2-16.
  • Fawcett L, Green AC. Bayesian posterior predictive return levels for environmental extremes. Stochastic Environmental Research and Risk Assessment 2018, 32(8), 2233-2252.
  • Fawcett L. The CASE Project: Evaluation of Case-Based Approaches to Learning and Teaching in Statistics Service Courses. Journal of Statistics Education 2017, 25(2), 79-89.
  • Fawcett L, Thorpe N, Matthews J, Kremer K. A novel Bayesian hierarchical model for road safety hotspot prediction. Accident Analysis & Prevention 2017, 99(Part A), 262-271.
  • Fawcett L, Newman K. The storm of the century! Promoting student enthusiasm for applied statistics. Teaching Statistics 2016, 39(1), 2-13.
  • Fawcett L, Walshaw D. Sea-surge and wind speed extremes: optimal estimation strategies for planners and engineers. Stochastic Environmental Research and Risk Assessment 2016, 30(2), 463-480.
  • Fawcett L, Walshaw D. Estimating the probability of simultaneous rainfall extremes within a region: a spatial approach. Journal of Applied Statistics 2014, 41(5), 959-976.
  • Fawcett L, Thorpe N. Mobile safety cameras: estimating casualty reductions and the demand for secondary healthcare. Journal of Applied Statistics 2013, 40(11), 2385-2406.
  • Thorpe N, Fawcett L. Linking road casualty and clinical data to assess the effectiveness of mobile safety enforcement cameras: a before and after study. BMJ Open 2012, 2(6), e001304.
  • Fawcett L, Walshaw D. Estimating return levels from serially dependent extremes. Environmetrics 2012, 23(3), 272-283.
  • Fawcett L, Foster WH, Youd AJ. Using computer based assessments in a large statistics service course. MSOR Connections 2008, 8(3), 43-46.
  • Fawcett L, Walshaw D. Bayesian inference for clustered extremes. Extremes 2008, 11(3), 217-233.
  • Fawcett L, Walshaw D. Improved estimation for temporally clustered extremes. Environmetrics 2007, 18(2), 173-188.
  • Fawcett L, Walshaw D. Markov chain models for extreme wind speeds. Environmetrics 2006, 17(8), 795-809.
  • Fawcett L, Walshaw D. A hierarchical model for extreme wind speeds. Journal of the Royal Statistical Society. Series C: Applied Statistics 2006, 55(5), 631-646.