C. Yan, J. Johndrow, and D.B. Woodard (2022). Statistical efficiency of travel time prediction. ArXiv technical report arXiv:2112.09993.

C. Yan, H. Zhu, N. Korolko, and D.B. Woodard (2019). Dynamic pricing and matching in ride-hailing platforms. Naval Research Logistics. Winner of the 2021 Kuhn Award for the best paper in Naval Research Logistics over the previous three years. Published; preprint.

D. B. Woodard, G. Nogin, P. Koch, D. Racz, M. Goldszmidt, and E. Horvitz (2017). Predicting travel time reliability using mobile phone GPS data. Transportation Research Part C, 75: 30-44. pdf

B. S. Westgate, D. B. Woodard, D. S. Matteson, and S. G. Henderson (2016). Large-network travel time distribution estimation for ambulances. European Journal of Operational Research, 252:322-333. pdf.

L. Bornn, N. Pillai, A. Smith, and D. Woodard (2016). The use of a single pseudo-sample in Approximate Bayesian Computation MCMC. Statistics and Computing. doi:10.1007/s11222-016-9640-7. arXiv:1404.6298.

Z. Zhou, D.S. Matteson, D.B. Woodard, S.G. Henderson and A.C. Micheas (2015). A spatio-temporal point process model for ambulance demand. Journal of the American Statistical Association, 110:6-15. Winner of the 2014 American Statistical Association Health Policy Statistics Student Paper Competition and Finalist in the 2013 INFORMS Data Mining Student Paper Competition. pdf

D. Singhvi, S. Singhvi, P.I. Frazier, S.G. Henderson, E. O’Mahony, D.B. Shmoys, and D.B. Woodard (2015). Predicting bike usage for New York City’s bike sharing system. AAAI 2015 Workshop on Computational Sustainability. pdf.

D.B. Woodard (2014). A lower bound on the mixing time of uniformly ergodic Markov chains in terms of the spectral radius. ArXiv technical report arXiv:1405.0028.

D.B. Woodard, C. Crainiceanu, and D. Ruppert (2013). Hierarchical adaptive regression kernels for regression with functional predictors. J. of Computational and Graphical Statistics, 22: 777-800. published; preprint; web appendix.

D. B. Woodard and J. S. Rosenthal (2013). Convergence rate of Markov chain methods for genomic motif discovery. Annals of Statistics, 41: 91-124. published (pdf); web appendix; journal website.

D. B. Woodard, T. M. T. Love, S. W. Thurston, D. Ruppert, S. Sathyanarayana, and S. H. Swan (2013). Latent factor regression models for grouped outcomes. Biometrics, 69: 785-794. published; preprint (pdf)

B. S. Westgate, D. B. Woodard, D. S. Matteson, and S. G. Henderson (2013). Travel time estimation for ambulances using Bayesian data augmentation. Annals of Applied Statistics, 7: 1139-1161. published (pdf); web appendix; journal website.

D. B. Woodard, R. Bilina Falafala, and C. Crainiceanu (2013). Model-based image segmentation via Monte Carlo EM, with application to DCE-MRI. pdfweb appendix.

S.C. Schmidler and D. B. Woodard (2013). Lower bounds on the convergence rates of adaptive MCMC methods.  pdf.  Originally published as an ORIE Technical report in Jan 2011.

D. Woodard. Comment on article by Schmidl et al. (2013). Bayesian Analysis, 8:23-26. published pdf. This note provides bounds on the computational efficiency of the method proposed by Schmidl et al., showing that its run time increases exponentially in dimension for some representative cases.

D.B. Woodard and M. Goldszmidt (2011). Online model-based clustering for crisis identification in distributed computing. J. of the American Statistical Association, 106:49-60. published, preprint (pdf).

D.B. Woodard, D. S. Matteson and S. G. Henderson (2011). Stationarity of generalized autoregressive moving average models. Electronic Journal of Statistics, 5:800-828. published.

D.S. Matteson, M.W. McLean, D.B. Woodard, and S.G. Henderson (2011). Forecasting Emergency Medical Service call arrival rates. Annals of Applied Statistics, 5:1379-1406. published (pdf); Journal website.

M. Goldszmidt, D.B. Woodard and P. Bodik (2011). Real-time identification of performance problems in large distributed systems. In A. Srivastava and J. Han, ed., Machine Learning and Knowledge Discovery for Engineering Systems Health Management. Boca Raton, FL: Taylor and Francis. 502 pp.

D.B. Woodard (2011). Detecting poor mixing of posterior samplers due to multimodality. Technical report, Duke University Department of Statistical Science. pdf

D.B. Woodard, R.L. Wolpert and M.A. O’Connell (2010). Spatial inference of nitrate concentrations in groundwater. J. of Agricultural, Biological, and Environmental Statistics, 15:209-227. published, preprint (pdf) .

P. Bodik, M. Goldszmidt, A. Fox, D.B. Woodard and H. Andersen (2010). Fingerprinting the datacenter: Automated classification of performance crises. In G. Muller, editor, EuroSys 2010: Proc. of the 5th European Conference on Computer Systems, pp.111-124. New York: Association for Computing Machinery. pdf

D.B. Woodard, S.C. Schmidler, M.L. Huber (2009). Conditions for rapid mixing of parallel and simulated tempering on multimodal distributions. Annals of Applied Probability, 19:617-640. published (pdf), Journal website.

D.B. Woodard, S.C. Schmidler, M.L. Huber (2009). Sufficient conditions for torpid mixing of parallel and simulated tempering. Electronic Journal of Probability, 14:780-804. published, preprint (pdf)

D.B. Woodard, A. E. Gelfand, W. E. Barlow, and J. G. Elmore (2007). Performance assessment for radiologists interpreting screening mammography. Statistics in Medicine, 26:1532-1551. published, preprint (pdf).

D.B. Woodard (2007). Conditions for rapid and torpid mixing of parallel and simulated tempering on multimodal distributions. Thesis, Duke U. pdf

M. O’Connell, D.B. Woodard, J. Hoffman, and A. Jack (2007). Bayesian modeling with S-PLUS and the S+flex Bayes library. In D. Spruck, ed., Proceedings of the Pharmaceutical Users Software Exchange Conference, #ST07, 11 pages. Kent, U.K.: Pharmaceutical Users Software Exchange. Microsoft Word (.doc)