Eric Bair, PhD
|Research Assistant Professor
EndodonticsResearch Assistant Professor
School of Dentistry
Campus Box 7450
Chapel Hill 27599-7450
|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|
- Reproductive health
- Women’s health
- Chronic pain
- Temporomandibular disorders
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.
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).