- Email: jessewheeler@isu.edu
About Me
I am an Assistant Professor in the Department of Mathematics and Statistics at Idaho State University. I earned my Ph.D. in Statistics from the University of Michigan in 2025, under the mentorship of Edward Ionides.
My research focuses on time series analysis through likelihood-based inference algorithms for state space models. I work at the intersection of methodology, applications, and software development related to these models. The unifying theme of my work in statistics and computing is to empower researchers to fit models of scientific interest, rather than being limited to models that are mathematically convenient.
While my primary application area has been modeling infectious disease outbreaks, I am also interested in various other topics and applications, including ecology, fisheries, and agriculture. If you are interested in learning more about my research or are interested in collaborating, do not hesitate to reach out to me via email (jessewheeler@isu.edu).
Education
PhD in Statistics | University of Michigan | 2020 - 2025
B.S. in Mathematics and Statistics | Utah State University | 2016 - 2020
Selected Papers
Wheeler, J., et al. 2024. “Informing policy via dynamic models: Cholera in Haiti”. PLOS Computational Biology, 20(4), e1012032.
Wheeler, J., Ionides, E. L. 2023. “Likelihood Based Inference of ARMA Models”. ArXiv preprint. arXiv.2310.01198.
Bretó, C., Wheeler, J., King, A. A., Ionides, E. L. 2025. “panelPomp: Analysis of Panel Data via Partially Observed Markov Processes in R”. The R Journal, to appear. arXiv:2410.07934v2.
Yang, B., Wheeler, J., Ionides, E. L. 2025. “Mechanistic Models for Panel Data: Analysis of Ecological Experiments with Four Interacting Species”. ArXiv preprint. arXiv:2506.04508v2.
Ionides, E. L., Ning, N., Wheeler, J. 2024. “An Iterated Block Particle Filter for Inference on Coupled Dynamic Systems with Shared and Unit-Specific Parameters”. Statistica Sinica, 34, 1241-1262.
Wheeler, J., Bean, B., Maguire, M. 2022. “Creating a universal depth-to-load conversion technique for the conterminous United States using random forests”. Journal of Cold Regions Engineering, 36(1), 04021019.
Wagstaff, J., Bean, B., Wheeler, J., Maguire, M., Sun, Y. 2024. “Adaptive Mapping of Design Ground Snow Loads in the Conterminous United States”. Journal of Structural Engineering, 150(1), 04023193.
Software
- arima2: This library aids maximum likelihood estimation of parameters of ARIMA time series models. The package is currently on CRAN, and there is an associated pre-print paper on ArXiv.
- panelPomp: This R package on CRAN is used for inference of Panel POMP models. As associated software paper was recently accepted at The R Journal (ArXiv.2410.07934v2).
This website was created using Quarto. To learn more about Quarto websites, see slides I created for a Statistics Student Seminar at the University of Michigan, 2024.
