Jeff Goldsmith, PhD

  • Associate Professor of Biostatistics
Profile Headshot


For several years, Jeff Goldsmith has worked to advance the state-of-the-art in functional data analysis by developing methods for understanding patterns in large, complex datasets in neuroscience, physical activity monitoring, and other areas.

Working closely with clinicians and neuroscientists around the world, he and his collaborators have focused on improving the understanding skilled movements. This work involves reaching movements made by stroke patients: in these experiments, a patient's fingertip position is recorded hundreds of time per second for the duration of the reach. Dr. Goldsmith has developed new statistical methods to understand the impact of stroke on movement quality, and applied these to large, longitudinal datasets. In parallel, he has proposed methods for wearable device research, especially focusing on accelerometers. These devices can produce minute-by-minute (or even finer) resolution observations of activity for hundreds of participants over several days, weeks, or months. The methods developed include approaches for regression with activity trajectories as outcomes; for interpretable dimension reduction; and for aligning major patterns (like wake from sleep, mid-day dips in activity, and sleep onset) across subjects.

Dr. Goldsmith has worked to incorporate data science techniques for transparency and reproducibility into biostatistical analyses. Research projects are accompanied by robust, publicly available software and analytical pipelines that ensure the reproducibility of the results. This approach is informed by his work in teaching data science.

Academic Appointments

  • Associate Professor of Biostatistics

Administrative Titles

  • Associate Dean, Data Science

Credentials & Experience

Education & Training

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

Committees, Societies, Councils

  • Member of the American Statistical Association (ASA)
  • Member of the Eastern North American Region (ENAR) of the International Biometric Society

Honors & Awards

  • 2024: Presidential Award for Outstanding Teaching, Columbia University
  • 2021: Dean's Excellence in Leadership Award, Mailman School of Public Health
  • 2021: COPSS Emerging Leader Award, American Statistical Association
  • 2017: Tow Faculty Scholar, Mailman School of Public Health


Research Interests

  • Biostatistics

Selected Publications

Crainiceanu, C.M., Goldsmith, J., Leroux, A. and Cui, E (2024). Functional Data Analysis with R.

Hilden, P., Schwartz, J.E., Pascual, C., Diaz, K.M., Goldsmith, J.(2023). How Many Days are Needed? Measurement Reliability of Wearable Device Data to Assess Physical Activity PLOS ONE, 18 e0282162. (

Goldsmith, J., Kitago, T., Garcia de la Garza, A., Kundert, R., Luft, A., Stinear, C., By- blow, W.D., Kwakkel, G., Krakauer, J.W. (2022). Arguments for the biological and predictive relevance of the proportional recovery rule. eLife, 11 e80458. (

J Goldsmith, Y Sun, L Fried, J Wing, G W Miller, K Berhane (2021). The Emergence and Future of Public Health Data Science. Public Health Reviews, 42, 1604023. (

D. Backenroth, J. Goldsmith, M. D. Harran, J. C. Cortes, J. W. Krakauer, and T. Kitago (2018). Modeling motor learning using heteroskedastic functional principal components analysis. Journal of the American Statistical Association, 113 1003-1015. (

J. Goldsmith, X. Liu, J. S. Jacobson and A. Rundle (2016). New insights into activity patterns in children, found using functional data analyses. Medicine & Science in Sports & Exercise, 48 1723-1729. (

J. Goldsmith, V. Zipunnikov, J. A. Schrack (2015). Generalized Multilevel Functional-on- Scalar Regression and Principal Component Analysis. Biometrics, 71 344-353. (

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, Greven S, Crainiceanu C M, (2013). Corrected Confidence Bands for Functional Data Using Principal Components. Biometrics, 69 41-51. (

Goldsmith, J, Bobb, J, Crainiceanu, CM, Caffo, BS, Reich, DS (2011). Penalized Functional Regression. Journal of Computational and Graphical Statistics 20 830-851. (