Eric Bair, PhD


bair_eric Research Assistant Professor

Research Assistant Professor

REACH NC (Collexis) Research Profile

Koury 4503
School of Dentistry
Campus Box 7450
Chapel Hill 27599-7450
USAT: 919-537-3276


2004 Stanford University PhD, Statistics
2004 Stanford University MS, Biology
2002 Stanford University MS, Statistics
1998 Utah State University BS, Mathematics/Statistics

BIOS 610 Biostatistics for Laboratory Scientists

Research interests

  • Cancer
  • Disabilities
  • Reproductive health
  • Women’s health
  • Chronic pain
  • Temporomandibular disorders

Research activities

My primary appointment is in UNC’s Center for Neurosensory Disorders, where I study chronic pain conditions. I am involved with the OPPERA study, which is a large-scale prospective study to identify the risk factors of temporo-mandibular disorder. I also work on several smaller studies on arthritis and other chronic pain conditions. In particular, I recently began a study to identify risk factors for dysmenorrhea (severe pain during menstruation) and other chronic pain conditions that are specific to women. My statistical research interests include machine learning and statistical genetics. I am developing novel methods to predict the risk of disease (or other outcomes) based on high-throughput genetic data (such as microarrays or genome-wide association studies) and methods to identify clusters in complex, high-dimensional data sets.

Key publications

Bair, E., Brownstein, N., Ohrbach, R., Greenspan, J., Dubner, R., Fillingim, R., Maixer, W., Diatchenko, L., Gonzalez, Y., Gordon, S., Lim, P., Ribeiro-Dasilva, M., Dampier, D., Knott, C., and Slade, G.D. “Study protocol, sample characteristics and loss-to-follow-up: the OPPERA prospective cohort study,” Journal of Pain, 14(12SUPPL):T2-T19 (2013).

Bair, E., Ohrbach, R., Fillingim, R., Greenspan, J.D., Dubner, R., Diatchenko, L., Helgeson, E.,  Knott, C.,  Maixer, W., and Slade, G.D. “Multivariable modeling of phenotypic risk factors for first-onset TMD: the OPPERA prospective cohort study,” Journal of Pain, 14(12SUPPL):T102-T115 (2013).

Debashis, P., Bair, E., Hastie, T., and Tibshirani, R. “Pre-conditioning for feature selection and regression in high-dimensional problems,” Annals of Statistics, 36(4):1595-1618 (2008).

Bair, E., Hastie, T., Debashis, P., and Tibshirani, R. “Prediction by supervised principal components,” Journal of the American Statistical Association, 101:119-137 (2006).

Bair, E., and Tibshirani, R. “Semi-supervised methods to predict patient survival from gene expression data,” Plos Biology,  2(4):511-522 (2004).