Jeff Goldsmith is an associate professor in Biostatistics at the Columbia University Mailman School of Public Health. Dr. Goldsmith joined Columbia after receiving his PhD in Biostatistics from Johns Hopkins in 2012, where his dissertation focused on statistical methods for high-dimensional structured data. Dr. Goldsmith has research interests in scientific domains including neuroimaging, physical activity monitoring using accelerometers, motion kinematics and motor learning, and urban environments. His work is generally focused on functional data analysis, and draws heavily from ongoing collaborations with public health researchers, clinicians, and neuroscientists. Dr. Goldsmith has worked to incorporate data science techniques into methods development and applied analyses in each of these domains.
Goldsmith J, Huang L, Crainiceanu C M (2014). Smooth Scalar-on-Image Regression via Spatial Bayesian Variable Selection. Journal of Computational and Graphical Statistics, 23 46-64.
Goldsmith J, Scheipl F (2014). Estimator Selection and Combination in Scalar-on-Function Regression. Computational Statistics and Data Analysis, 70 362-372.
Goldsmith J, Greven S, Crainiceanu C M, (2013). Corrected Confidence Bands for Functional Data Using Principal Components. Biometrics, 69 41-51.
Goldsmith, J, Crainiceanu, CM, Caffo, BS, Reich, DS Longitudinal Penalized Functional Regression for Cognitive Outcomes on Neuronal Tract Measurements Journal of the Royal Statistical Society: Series C 61 453-469 2012
Goldsmith, J, Caffo, BS, Crainiceanu, CM, Du, Y, Reich, DS, Hendrix, CW Non-linear Tube Fitting for the Analysis of Anatomical and Functional Structures Annals of Applied Statistics 5 337-363 2011
Goldsmith, J, Bobb, J, Crainiceanu, CM, Caffo, BS, Reich, DS Penalized Functional Regression Journal of Computational and Graphical Statistics 20 830-851 2011
Goldsmith, J, Wand, MP, Crainiceanu, CM Functional Regression via Variational Bayes Electronic Journal of Statistics 5 572-602 2011