B.S., University of Massachusetts, Boston; M.S., University of Massachusetts, Amherst; Ph.D., University of Massachusetts, Amherst; Postdoctoral Research Associate, Biostatistics, University of Massachusetts, Amherst
Area(s) of Specialization:
time series, forecasting, infectious disease
My research focuses on non-parametric and flexible parametric models for prediction and classification tasks with time series data that are relevant to public health. In the last few years, this work has centered on forecasting infectious diseases such as Dengue fever, influenza, and COVID-19. I have developed forecasting models using kernel conditional density estimation, copulas, and hierarchical splines or Gaussian processes, as well as ensemble methods to combine forecasts from multiple individual models. In my Ph.D. dissertation I developed approaches to classifying physical activity using accelerometer data, using conditional random field models in combination with random forests or gradient tree boosting.
Ray EL, Qian J, Brecha R, Reilly MP, and Foulkes AS (2019). Stochastic imputation for integrated transcriptome association analysis of a longitudinally measured trait. Statistical Methods in Medical Research. DOI: 10.1177/0962280219852720.
Ray EL and Reich NG (2018). Prediction of infectious disease epidemics via weighted density ensembles. PLOS Computational Biology 14(2): e1005910
Ray EL, Sasaki JE, Freedson PS, and Staudenmayer J (2018). Physical Activity Classification with Dynamic Discriminative Methods. Biometrics. DOI: 10.1111/biom.12892
Ray EL, Sakrejda, K, Lauer, SA, Johansson, MA, and Reich, NG (2017). Infectious disease prediction with kernel conditional density estimation. Statistics in Medicine, 36:4908–4929.