Edward Ionides Professor of Statistics The University of Michigan 1085 South University Ave Ann Arbor, MI 48109-1107 Phone: 734.615.3332 Fax: 734.763.4676 E-mail: ionides@umich.edu Office: 453 West Hall Curriculum vitae. Publications; Student research. Research interests: Time series analysis with applications including ecology, epidemiology and health economics. Methodological work on inference for partially observed stochastic dynamic systems. Teaching: STATS 401: Applied Statistical Methods II. MATH/STATS 425: Introduction to Probability. STATS/DATASCI 531: Analysis of Time Series. STATS/DATASCI 631: Modeling and Analysis of Time Series Data. STATS 620: Applied Probability and Stochastic Modeling. STATS 700: Topics in Applied Statistics: Phylodynamic Inference. STATS 810: Literature Proseminar. Research links: Slides for some talks. The R package pomp. The R package pomp. The R package spatPomp. The Python package pypomp. Simulation-based inference for epidemiological dynamics. Likelihood-based inference for dynamic systems. Selected publications: Wheeler, J., Rosengart, A. L., Jiang, Z., Tan, K., Treutle, N. and Ionides, E. L. (2024). Informing policy via dynamic models: Cholera in Haiti. PLOS Computational Biology 20 e1012032. doi. arxiv. github. Ionides, E. L., Asfaw, K., Park, J., and King, A. A. (2023). Bagged filters for partially observed interacting systems. Journal of the American Statistical Association 118 1078-1089. doi. arxiv. github. Ning, N. and Ionides, E. L. (2023). Iterated block particle filter for high-dimensional parameter learning: Beating the curse of dimensionality. Journal of Machine Learning Research 24(82) 1-76. pdf. arxiv. github. Breto, C., Ionides, E. L., and King, A. A. (2019). Panel data analysis via mechanistic models. Journal of the American Statistical Association 115 1178-1188. doi. arxiv. github. Ionides, E. L., Nguyen, D., Atchade, Y., Stoev, S. and King, A. A. (2015). Inference for dynamic and latent variable models via iterated, perturbed Bayes maps. Proceedings of the National Academy of Sciences of the USA 112 719-724. doi. pdf. Ionides, E. L, Bhadra, A., Atchade, Y. and King, A. A. (2011). Iterated filtering. Annals of Statistics 39 1776-1802. doi. pdf. arxiv. Breto, C., He, D., Ionides, E. L. and King, A. A. (2009). Time series analysis via mechanistic models. Annals of Applied Statistics 3 319-348. doi. pdf. arxiv. King, A. A., Ionides, E. L., Pascual, M. and Bouma, M. J. (2008). Inapparent infections and cholera dynamics. Nature 454 877-880. doi. Ionides, E. L., Breto, C. and King, A. A. (2006). Inference for nonlinear dynamical systems. Proceedings of the National Academy of Sciences 103 18438-18443. doi. Supporting online material. pdf. supporting text.