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Jeff Goldsmith


Jeff Goldsmith has worked for several years to advance the state of the art in functional data analysis by developing methods for understanding patterns in large, complex data sets in neuroscience, physical activity monitoring, and other fields. Working closely with clinicians and neuroscientists around the world, he and his staff have focused on improving the understanding of qualified movements. This work deals with gripping movements of stroke patients: In these experiments, the fingertip position of a patient is recorded hundreds of times per second for the duration of the gripping. Dr. Goldsmith developed new statistical methods to understand the impact of stroke on movement quality and applied them to large longitudinal data sets. In parallel, he has proposed methods for researching wearable devices, with a particular focus on accelerometers. These devices can provide up-to-the-minute (or even finer) activity observations for hundreds of participants over several days, weeks or months. The developed methods include regression approaches with activity courses as a result; for interpretable dimension reduction; and for comparing important patterns (such as waking up from sleep, activity breakdowns at noon and falling asleep) between the test subjects. Dr. Goldsmith has worked to incorporate data science techniques for transparency and reproducibility into biostatistical analysis. Research projects are accompanied by robust, publicly available software and analytical pipelines that ensure the reproducibility of the results. This approach is shaped by his work in teaching data science.

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PhD, 2012, Johns Hopkins School of Public Health BS, 2007, Dickinson College

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Goldsmith J, Huang L, Crainiceanu C M (2014). Smooth scalar-on-image regression using spatial Bayesian variables. Journal of Computational and Graphical Statistics, 23 46-64. Goldsmith J, Scheipl F (2014). Selection and combination of estimators in scalar-on-function regression. Computer 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 Nonlinear 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

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