Inference for stochastic processes, with applications to cell motion, ecology, epidemiology and neuroscience. Methodological work on inference for partially observed diffusion processes. Foundational research on statistical inference via maximum smoothed likelihood estimation.
Math / Stat 425, Introduction to Probability
Stat 531 / Econ 677, Time Series Analysis
Stat 620, Applied Probability and Stochastic Modeling
B.A. Mathematics, University of Cambridge, 1994
Ph.D. Statistics, University of California, Berkeley, 2001
Ionides, E. L., Breto, C. and King, A. A. (2006). Inference for nonlinear dynamical systems. Proceedings of the National Academy of Sciences 103 pp. 18438 - 18443.
Greene, S. K., Ionides, E. L. and Wilson, M. L. (2006). Patterns of Influenza-Associated Mortality Among U.S. Elderly by Geographic Region and Virus Subtype, 1968-1998, American Journal of Epidemiology 163 pp. 316-326.
Gage, G. J., Ludwig, K., Otto, K., Ionides, E. L. and Kipke, D. (2005). Naive coadaptive cortical control. Journal of Neural Engineering 2 pp. 52-63.
Ionides, E. L. (2005). Maximum Smoothed Likelihood. Statistica Sinica 15 pp. 1003-1014.
Ionides, E. L., Fang, K.S., Isseroff, R.R. and Oster, G.F. (2004). Stochastic models for cell motion and taxis. Journal of Mathematical Biology 48 pp. 23-37.