Rate My Professor Rebecca Killick

Killick's research focuses on developing novel statistical methodologies for nonstationary time series analysis, including changepoint detection, multiscale methods, wavelets, and streaming data processing, with applications in environmental monitoring, health informatics, oceanography, and climate science. She is the primary developer of the 'changepoint' R package, available on CRAN, which facilitates changepoint analysis and was introduced in her seminal 2014 paper 'changepoint: An R Package for Changepoint Analysis' co-authored with Idris A. Eckley in the Journal of Statistical Software. Other significant publications include 'Optimal detection of changepoints with a linear computational cost' (Journal of the American Statistical Association, 2012), 'Changepoint Detection: An Analysis of the Central England Temperature Series' (Journal of Climate, 2022), 'Detecting changes in the covariance structure of high dimensional data using random matrix theory' (Technometrics, 2023), and 'Modelling Time-Varying First and Second-Order Structure of Time Series via Wavelets and Differencing' (Electronic Journal of Statistics, 2022). Her work has amassed over 7,000 citations according to Google Scholar.

Killick earned the 2019 Young Statistician Award from the European Network for Business and Industrial Statistics (ENBIS), becoming the first UK recipient for her contributions to industrial statistics. She serves as Co-Editor-in-Chief of the Journal of Statistical Software (2020-present), Associate Editor for Data Science in Science (2021-present), ROpenSci (2021-present), and other journals, and holds leadership roles including ENBIS Council member, Royal Statistical Society Data Science Task Force member, and Secretary of the RSS Statistical Computing Section.