Ephraim M. Hanks is an Associate Professor of Statistics at Penn State.
Hanks received his Ph.D. in Statistics from Colorado State University in 2013. He received his M.S. in Statistics from Utah State University in 2010, and a B.S. in Mathematics from Utah State University in 2002.
His research focuses on advances in statistical modeling and computation to increase our ability to ask and answer scientific questions in ecology, epidemiology and other fields, with an emphasis on spatial and spatio-temporal systems.
Hanks currently serves as the graduate program chair in the Department of Statistics at Penn State. He also currently serves on the executive board for the American Statistical Association's Section on Statistics and the Environment.
Honors and Awards
2017 Early Investigator award for the ASA Section on Statistics and the Environment
- EM Hanks (2017). Modeling spatial covariance using the limiting distribution of spatio-temporal random walks. Journal of the American Statistical Association 112(518):497-507.
- PC Cross, DJ Prosser, AM Ramey, EM Hanks, KM Pepin (2019). Confronting models with data: the challenges of estimating disease spillover. Philosophical Transactions of the Royal Society B. 374(1782):20180435.
- JC Russell, EM Hanks, M Haran, DP Hughes (2018). A spatially-varying stochastic differential equation model for animal movement. The Annals of Applied Statistics 12(2):1312-1331.
- EM Hanks, MB Hooten, ST Knick, SJ Oyler-McCance, JA Fike, TB Cross, MK Schwartz (2016). Latent spatial models and sampling design for landscape genetics. The Annals of Applied Statistics 10(2):1041-1062.
- EM Hanks, MB Hooten, MW Alldredge (2015). Continuous-Time, Discrete-Space Models of Animal Movement. The Annals of Applied Statistics 9(1):145-165.
- EM Hanks, EM Schliep, MB Hooten, JA Hoeting (2015). Restricted spatial regression in practice: geostatistical models, confounding, and robustness under model misspecification. Environmetrics 26(4): 243-254.
- EM Hanks, MB Hooten (2013). Circuit theory and model-based inference for landscape connectivity. Journal of the American Statistical Association. 108(501), 22-33.
- J Fricks, EM Hanks (2018). Stochastic population models. Integrated Population Biology and Modeling 39:11443.
STAT 511 - Regression
STAT 461 - Design of Experiments
STAT 416 - Stochastic Processes