Professor of Statistics
University of Michigan, USA
Ed Ionides works on statistical analysis of dynamic systems with applications in the health sciences and specifically the transmission of infectious diseases. His group at University of Michigan develops inference methodology for relating time series data to partially observed stochastic nonlinear dynamic models such as arise in disease systems. He is also interested in inference on pathogen dynamics from panel data, spatiotemporal data and genomic data.
Website: https://ionides.github.io/
Professor of Statistics
University of Michigan, USA
Ed Ionides works on statistical analysis of dynamic systems with applications in the health sciences and specifically the transmission of infectious diseases. His group at University of Michigan develops inference methodology for relating time series data to partially observed stochastic nonlinear dynamic models such as arise in disease systems. He is also interested in inference on pathogen dynamics from panel data, spatiotemporal data and genomic data.
Website: https://ionides.github.io/