Phone:: (023) 8059 5117
Email:: D.Woods@soton.ac.uk

Professor Dave Woods

Professor of Statistics and EPSRC Fellow

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Dave Woods is a Professor of Statistics and an EPSRC Research Fellow in the Southampton Statistical Sciences Research Institute (S3RI) and Mathematical Sciences. His research interests focus on the design and analysis of both physical and computer experiments, Bayesian methods, and the application of Statistics to science and technology. Much of his work is interdisciplinary and involves collaborations with researchers from other disciplines, and with government and industry.

Current position

Professor of Statistics

Previous position

Reader in Statistics, 2011 - 2013.

Qualifications

PhD in Statistics, University of Southampton

Research

Responsibilities

Publications

Teaching

Contact

Research interests

My research interests are in the design and analysis of experiments, particularly the development of new methods and criteria for design selection and assessment under linear and nonlinear models. A particular emphasis of my work is finding efficient designs when there is uncertainty in one or more aspects of the model for the response. I develop methodology that combines theory and computation to solve problems motivated by real experiments in science and industry. Application areas include engineering, chemistry and the pharmaceutical, automotive and aeronautics industries.

Research group

Statistics

Research project(s)

Design and Analysis of Experiments

Prediction of thermophysical properties of pharmeceutical compounds

External:
Associate Editor for Technometrics

Internal:
S3RI Research Theme Coordinator for Design and Analysis of Experiments

Articles

Woods, D. C., Overstall, A. M., Adamou, M., & Waite, T. W. (2017). Bayesian design of experiments for generalised linear models and dimensional analysis with industrial and scientific application. Quality Engineering, 29(1), 91-103. DOI: 10.1080/08982112.2016.1246045

Woods, D. C., McGree, J. M., & Lewis, S. M. (2017). Model selection via Bayesian information capacity designs for generalised linear models. Computational Statistics & Data Analysis, 113, 226-238. DOI: 10.1016/j.csda.2016.10.025

Overstall, A., & Woods, D. (2016). Bayesian design of experiments using approximate coordinate exchange. Technometrics, 1-20. DOI: 10.1080/00401706.2016.1251495

Bowman, V., & Woods, D. (2016). Emulation of Multivariate Simulators Using Thin-Plate Splines with Application to Atmospheric Dispersion. Journal of Uncertainty Quantification, 4, 1323-1344.

Overstall, A., & Woods, D. (2016). Multivariate emulation of computer simulators: model selection and diagnostics with application to a humanitarian relief model. Journal of the Royal Statistical Society. Series C: Applied Statistics, 65(4), 483-505. DOI: 10.1111/rssc.12141

Waite, T. W., & Woods, D. C. (2015). Designs for generalized linear models with random block effects via information matrix approximations. Biometrika, 102, 677-693. DOI: 10.1093/biomet/asv005

van de Ven, P. M., & Woods, D. C. (2014). Optimal blocked minimum-support designs for non-linear models. Journal of Statistical Planning and Inference, 144, 152-159. DOI: 10.1016/j.jspi.2013.02.001

Draguljic, D., Woods, D. C., Dean, A. M., Lewis, S. M., & Vine, A-J. E. (2014). Screening strategies in the presence of interactions (with discussion). Technometrics, 56(1), 1-28. DOI: 10.1080/00401706.2013.775900

Overstall, A., & Woods, D. C. (2013). A strategy for Bayesian inference for computationally expensive models with application to the estimation of stem cell properties. Biometrics, 69(2), 458-468. DOI: 10.1111/biom.12017

Fisher, V. A., Woods, D. C., & Lewis, S. M. (2013). Optimal design for prediction using local linear regression and the DSI-criterion. Statistics and Applications, 11(1 & 2), 33-54.

Bowman, V. E., & Woods, D. C. (2013). Weighted space-filling designs. Journal of Simulation, 7, 249-263. DOI: 10.1057/jos.2013.8

Woods, D. C., & van de Ven, P. (2011). Blocked designs for experiments with correlated non-normal response. Technometrics, 53(2), 173-182. DOI: 10.1198/TECH.2011.09197

Woods, D. C., & Lewis, S. M. (2011). Continuous optimal designs for generalized linear models under model uncertainty. Journal of Statistical Theory and Practice, 5(1), 137-145.

Biedermann, S., Dette, H., & Woods, D. C. (2011). Optimal design for additive partially nonlinear models. Biometrika, 98(2), 449-458. DOI: 10.1093/biomet/asr001

Biedermann, S., & Woods, D. C. (2011). Optimal designs for generalized non-linear models with application to second-harmonic generation experiments. Journal of the Royal Statistical Society, Series C (Applied Statistics), 60(2), 281-299. DOI: 10.1111/j.1467-9876.2010.00749.x

Marley, C. J., & Woods, D. C. (2010). A comparison of design and model selection methods for supersaturated experiments. Computational Statistics & Data Analysis, 54(12), 3158-3167. DOI: 10.1016/j.csda.2010.02.017

Woods, D. C. (2010). Robust designs for binary data: applications of simulated annealing. Journal of Statistical Computation and Simulation, 80(1), 29-41. DOI: 10.1080/00949650802445367

Russell, K. G., Eccleston, J. A., Lewis, S. M., & Woods, D. C. (2009). Design considerations for small experiments and simple logistic regression. Journal of Statistical Computation and Simulation, 79(1), 81-91. DOI: 10.1080/00949650701609006

Russell, K. G., Woods, D. C., Lewis, S. M., & Eccleston, J. A. (2009). D-optimal designs for Poisson regression models. Statistica Sinica, 19(2), 721-730.

Waterhouse, T. H., Woods, D. C., Eccleston, J. A., & Lewis, S. M. (2008). Design selection criteria for discrimination/estimation for nested models and a binomial response. Journal of Statistical Planning and Inference, 138(1), 132-144. DOI: 10.1016/j.jspi.2007.05.017

Woods, D. C., & Lewis, S. M. (2006). All-bias designs for polynomial spline regression models. Australian and New Zealand Journal of Statistics, 48(1), 49-58. DOI: 10.1111/j.1467-842X.2006.00424.x

Woods, D. C., Lewis, S. M., Eccleston, J. A., & Russell, K. G. (2006). Designs for generalized linear models with several variables and model uncertainty. Technometrics, 48(2), 284-292. DOI: 10.1198/004017005000000571

Woods, D. C., Lewis, S. M., Eccleston, J. A., & Russell, K. G. (2006). Designs for Generalized Linear Models with Several Variables and Model Uncertainty. Technometrics.

Woods, D. C. (2005). Designing experiments under random contamination with application to polynomial spline regression. Statistica Sinica, 15(3), 619-633.

Grove, D. M., Woods, D. C., & Lewis, S. M. (2004). Multifactor B-spline mixed models in designed experiments for the engine mapping problem. Journal of Quality Technology, 36(4), 380-391.

Woods, D. C., Lewis, S. M., & Dewynne, J. N. (2003). Designing experiments for multi-variable B-spline models. Sankhya, 65, 660-670.

Book Chapter

Woods, D., & Lewis, S. (2015). Design of experiments for screening. In R. Ghanem, D. Higdon, & H. Owhadi (Eds.), Handbook of Uncertainty Quantification. Springer.

Conferences

Woods, D. C., Grove, D. M., Liccardi, I., Lewis, S. M., & Frey, J. G. (2006). An eLearning website for the design and analysis of experiments with application to chemical processes. In A. Rizzi, & M. Vichi (Eds.), COMPSTAT 2006: Proceedings In Computational Statistics. (pp. 1641-1649). Heidelberg: Physica-Verlag.

Woods, D. C., & Lewis, S. M. (2005). Designing experiments for binary data using search algorithms. In Proceedings of the 55th Session of the International Statistical Institute, Sydney 5-12 April 2005. Voorburg, The Netherlands: International Statistical Institute.

Woods, D. C. (2005). Designs for generalized linear models under model uncertainty. International Conference on Design of Experiments, .

Woods, D. C. (2003). A Monte Carlo exchange algorithm for finding near-optimal designs under model contamination. Poster session presented at International Biometric Society British Region Conference, .

Woods, D. C. (2003). Minimum bias designs under random contamination: application to polynomial spline models. Conference on New Directions in Experimental Design DAE 2003, .

Ward, S. C., Hursthouse, M. B., Woods, D. C., & Lewis, S. M. (2003). Systematic study into the salt formation of functionalised organic substrates. In S. J. Cox (Ed.), Proceedings of UK e-Science All Hands Meeting. (pp. 208-211). Engineering and Physical Sciences Research Council.

Ward, S. C., Hursthouse, M. B., Woods, D. C., & Lewis, S. M. (2003). Systematic Study into the Salt Formation of Functionalised Organic Substrates. Paper presented at E-Science All Hands Meeting, .

Ward, S. C., Hursthouse, M. B., Woods, D. C., & Lewis, S. M. (2003). Systematic Study into the Salt Formation of Functionalised Organic Substrates: Nottingham 2003 E-Science Poster. Poster session presented at E-Science All Hands Meeting, .

Report

McNamara, C. A., Woods, D. C., Lewis, S. M., Bradley, M., & Frey, J. G. (2005). Optimization of asymmetric dihydroxylation through optimal designed experiments. (School of Mathematics Technical Report; No. 386). Southampton, UK: University of Southampton, School of Mathematics.

Working Papers

Overstall, A., Woods, D., & Adamou, M. (2017). acebayes: An R package for Bayesian optimal design of experiments via approximate coordinate exchange. University of Southampton.

Atkinson, A. C., & Woods, D. C. (2013). Handbook of the design and analysis of experiments: designs for generalized linear models. London, GB: Chapman and Hall/CRC.

Overstall, A. M., & Woods, D. C. (2011). Bayesian lightweight emulators for multivariate computer models. (S3RI Methodology Working Papers; No. M11/01). Southampton, GB: Southampton Statistical Sciences Research Institute.

Draguljic, D., Woods, D. C., Dean, A. M., Lewis, S. M., & Vine, A. E. (2011). Screening strategies in the presence of interactions. (Southampton Statistical Sciences Research Institute Working Papers; No. M11/10). Southampton, GB: Southampton Statistical Sciences Research Institute, University of Southampton.

Biedermann, S., & Woods, D. C. (2009). Optimal designs for generalised nonlinear models with application to second harmonic generation experiments. (S3RI Methodology Working Papers; No. M09/19). Southampton, UK: Southampton Statistical Sciences Research Institute.

Biedermann, S., Dette, H., & Woods, D. C. (2009). Optimal designs for multivariable spline models. (S3RI Methodology Working Papers; No. M09/16). Southampton, UK: University of Southampton, Southampton Statistical Sciences Research Institute.

Russell, K. G., Woods, D. C., Lewis, S. M., & Eccleston, J. A. (2008). D-optimal designs for Poisson regression models. (S3RI Methodology Working Papers; No. M08/01). Southampton, UK: Southampton Statistical Sciences Research Institute.

Woods, D. C. (2008). Robust Designs For Binary Data: Applications Of Simulated Annealing. (S3RI Methodology Working Papers; No. M08/03). Southampton, UK: Southampton Statistical Sciences Research Institute.

Waterhouse, T. H., Woods, D. C., Eccleston, J. A., & Lewis, S. M. (2006). Design selection criteria for discrimination between nested models for binomial data. (S3RI Methodology Working Papers; No. M06/02). Southampton, UK: Southampton Statistical Sciences Research Institute.

MATH6157 Applied Statistical Modelling

Professor Dave Woods
Building 54 Mathematical Sciences University of Southampton Highfield Southampton SO17 1BJ

Room Number:39/3023

Telephone:(023) 8059 5117
Facsimile:(023) 8059 5147
Email:D.Woods@soton.ac.uk

Professor Dave Woods's personal home page

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