Gillings School Directory

Eric Bair, PhD

Eric Bair, PhD

Associate Professor
Department of Biostatistics
Research Associate Professor
Department of Endodontics
  • 4503 Koury Building
  • CB# 7450
  • Chapel Hill, NC 27599
  • USA

Dr. Eric Bair is a research associate professor in the Departments of Biostatistics and Endodontics. His primary appointment is in UNC’s Center for Pain Research and Innovation, where he studies chronic pain conditions. He is involved with the OPPERA study, which is a large-scale prospective study to identify the risk factors of temporomandibular disorders (TMD). He also works on several smaller studies on other chronic pain conditions.

Dr. Bair's statistical research interests include machine learning and statistical genetics. He is 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. He is applying these methods to data collected in OPPERA to identify subtypes of TMD.

Research Activities

Reproductive health
Women’s health
Chronic pain
Temporomandibular disorders

Key Publications

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

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

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

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

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


PhD, Statistics, Stanford University, 2004

MS, Biology, Stanford University, 2004

MS, Statistics, Stanford University, 2002

BS, Mathematics/Statistics, Utah State University, 1998

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