Research
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Major funded research projects
- Oct 2018 - Mar 2020 EPSRC: Active learning for computational polymorph landscape analysis (Co-investigator; £251,033).
- May 2018 - Apr 2021 EPSRC: Combining chemical robotics and statistical methods to discover complex functional products (Co-investigator; £1.3 milion).
- Nov 2017 - Nov 2020 US DTRA: Crystalcast Toolset – empowering decision making from uncertain disease forecasts (Co-investigator; $3.4 million US).
- Oct 2012 - Sep 2017 EPSRC Fellowship: Statistical design of experiments for complex nonparametric and mechanistic models (Principal Investigator; £390,713).
- Oct 2013 - Sep 2015 EPSRC: Closed loop optimisation for sustainable chemical manufacture (Co-investigator; £973,523)
- Jan 2012 - Dec 2014 GlaxoSmithKline: Efficient and effective experimental strategies to develop process understanding and control (Principal Investigator; £195,187).
Jan 2012 - Dec 2014 Australian Research Council Discovery Grant: Innovating optimal experimental design through Bayesian statistics (Co-investigator; $270,000 Australian)
- Jul 2008 - Jul 2012 US DTRA: Quantification of uncertainty in dispersion models via computer experiments (Principal Investigator; $963,936 US).
- Jan 2006 - Dec 2009 Australian Research Council Discovery Grant: Efficient designs for generalized linear models (Co-investigator; $170,468 Australian).
- Apr 2005 - Jul 2010 EPSRC: Combechem platform grant (Co-investigator; £415,486).
Postdocs
- Sam Jackson (Emulation and calibration of ensembles and chains of computer models)
- Emily Matthews (Bayesian design of experiments applied to chemical manufacturing)
- Olga Egorova (Sequential design of computer experiments for active learning in crystallography)
PhD students
- Ketil Tvermosegaard (Design for nonlinear mixed effect models with pharmaceutical applications)
- Stephen Gow (Uncertainty propagation through chains of computer models)
- Mia Tackney (Design of experiments for causal inference)
- Damianos Michaelides (Bayesian design of experiments for functional inputs)
Former postdocs and PhD students
- Tim Waterhouse (Postdoc, now Eli Lilly, USA)
- Peter van de Ven (Postdoc, now VU University Medical Centre, Netherlands)
- Antony Overstall (Postdoc, now University of Southampton, UK)
- Chris Marley (Postdoc and PhD)
- Sarah Carnaby (Postdoc and PhD, now IBM Hursley, UK)
- Tim Waite (Postdoc and PhD, now a Lecturer at the University of Manchester, UK)
- Verity Fisher (Postdoc and PhD, now GlaxoSmithKline, UK)
- Maria Adamou (Postdoc and PhD, now SensEye, UK)
- Emily Matthews (PhD, now postdoc at the University of Southampton)
- Andrew Rose (PhD, now Lubrizol, Hazelwood, UK)
- Kieran Martin (PhD, now Roche)
- Phil Adler (PhD, now Python developer)
- Yiolanda Englezou (PhD, now postdoc at the University of Cyprus)
Working papers
- Overstall, A.M., Woods, D.C. and Parker, B.M. (2018). Bayesian optimal design for ordinary differential equation models with application in biological science. Submitted (arXiv:1509.04099).
- Overstall, A.M., Woods, D.C. and Adamou, M. (2017). acebayes: An R package for Bayesian optimal design of experiments via approximate coordinate exchange. Submitted (arXiv:1705.08096).
- Phoa, F.K.H., Chou, S.-K. and Woods, D.C. (2017). Summary of effect aliasing structure (SEAS): new descriptive statistics for factorial and supersaturated designs. Submitted (arXiv:1711.11488).
- Müller, W. G., Rappold, A. and Woods, D.C. (2018). Copula-based robust optimal block designs. Submitted (arXiv:1811.02414).
Publications
- Overstall, A.M., Woods, D.C. and Martin, K. (2018). Bayesian prediction for physical models with application to the optimization of the synthesis of pharmaceutical products using chemical kinetics. Computational Statistics and Data Analysis, in press (doi:10.1016/j.csda.2018.10.013).
- Ramkumar, P., Harvey, T.J., Wood, R.J.K., Rose, A.D., Woods, D.C. and Lewis, S.M. (2018). Factorial study of diesel oil contamination effects on steel and ceramic sliding contacts. Journal of Engineering Tribology, in press (doi:10.1177/1350650118794730).
- Woods, D.C., McGree, J.M. and Lewis, S.M. (2017). Model selection via Bayesian information capacity designs for generalised linear models. Computational Statistics and Data Analysis, 113, 226-238 (doi:10.1016/j.csda.2016.10.025).
- Woods, D.C., Overstall, A.M., Adamou, M. and Waite, T.W. (2017). Bayesian design of experiments for generalised linear models and dimensional analysis with industrial and scientific application (with discussion). Quality Engineering, 29, 91-118 (invited refereed paper (doi:10.1080/08982112.2016.12460452).
- Woods, D.C. and Lewis, S.M. (2017). Design of experiments for screening. In "Handbook of Uncertainty Quantification", editors: Ghanem, R., Hidgon, D. and Owhadi, H. Springer, New York, pp. 1143-1185 (arXiv:1510.05248).
- Overstall, A.M. and Woods, D.C. (2017). Bayesian design of experiments using approximate coordinate exchange. Technometrics, 59, 458-470 (doi:10.1080/00401706.2016.1251495).
- Bowman, V.E. and Woods, D.C. (2016). Emulation of multivariate simulators using thin-plate splines with application to atmospheric dispersion. SIAM/ASA Journal of Uncertainty Quantification, 4, 1323-1344 (doi:10.1137/140970148).
- Overstall, A.M. and Woods, D.C. (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, 65, 483-505 (doi:10.1111/rssc.12141).
- Lendrem, D.W., Lendrem, B.C., Woods, D.C., Rowland-Jones, R., Burke, M., Chatfield, M., Isaacs, J.D. and Owen, M.R. (2015). Lost in space: design of experiments and scientific exploration in a Hogarth universe. Drug Discovery Today, 20, 1365-1371 (doi:10.1016/j.drudis.2015.09.015).
- Atkinson, A.C. and Woods, D.C. (2015). Designs for generalized linear models. In "Handbook of Design and Analysis of Experiments",
editors: Dean, A.M., Morris, M.D., Stufken, J. and Bingham, D.R. Chapman & Hall/CRC Press, Boca Raton
(Publisher website;
arXiv:1510.05253).
- Waite, T.W. and 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).
- Draguljic, D., Woods, D.C., Dean, A.M., Lewis, S.M. and Vine, A.E. (2014). Screening strategies in the presence of interactions (with discussion). Technometrics, 56, 1-28 (doi:10.1080/00401706.2013.775900). Awarded the Youden prize for best expository paper in this volume of Technometrics.
- van de Ven, P. and 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).
- Fisher, V.A., Woods, D.C. and Lewis, S.M. (2013). Optimal design for prediction using local linear regression and the DSI-criterion. Statistics and Applications, 11, 33-54 (Journal website) (invited refereed paper).
- Bowman, V.E. and Woods, D.C. (2013). Weighted space-filling designs. Journal of Simulation, 7, 249-263 (doi:10.1057/jos.2013.8).
- Overstall, A.M. and Woods, D.C. (2013). A Strategy for Bayesian inference for computationally expensive models with application to the estimation of stem cell properties. Biometrics, 69, 458-468 (doi:10.1111/biom.12017).
- Biedermann, S., Dette, H. and Woods, D.C. (2011). Optimal design for additive partially nonlinear models. Biometrika, 98, 449-458 (doi:10.1093/biomet/asr001).
- Woods, D.C. and van de Ven, P. (2011). Blocked designs for experiments with non-normal response. Technometrics, 53, 173-182 (doi:10.1198/TECH.2011.09197).
- Woods, D.C. and Lewis, S.M. (2011). Continuous optimal designs for generalized linear models under
model uncertainty. Journal of Statistical Theory and Practice, 5, 137-145 (Invited refereed paper; JSTP website).
- Biedermann, S. and Woods, D.C. (2011). Optimal designs for generalised nonlinear models with application to second harmonic generation experiments. Journal of the Royal Statistical Society, Series C, 60, 281-299 (doi:10.1111/j.1467-9876.2010.00749.x).
- Marley, C.J. and Woods, D.C. (2010). A comparison of design and model selection methods for supersaturated designs. Computational Statistics and Data Analysis, 54, 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, 29-41 (doi:10.1080/00949650802445367).
- Russell, K.G., Woods, D.C., Lewis, S.M. and Eccleston, J.A. (2009). D-optimal designs for Poisson regression models. Statistica Sinica, 19, 721-730 (Statistica Sinica website).
- Russell, K.G., Eccleston, J.A., Lewis, S.M. and Woods,
D.C. (2009). Design considerations for small experiments and simple
logistic regression. Journal of Statistical Computation and Simulation, 79, 81-91 (doi:10.1080/00949650701609006).
- Waterhouse, T.H., Woods, D.C., Eccleston, J.A. and Lewis, S.M. (2008).
Design selection criteria for discrimination/estimation for nested
models and a binomial response. Journal of Statistical Planning and Inference, 138, 132-144 (doi:10.1016/j.jspi.2007.05.017).
- McNamara, C.A., Woods, D.C., Lewis, S.M., Bradley, M. and Frey, J.G. (2006).
Optimization of asymmetric dihydroxylation through optimal designed experiments (Southampton e-Print 15827).
- Woods, D.C., Grove, D.M., Liccardi, I., Lewis, S.M. and Frey, J.G. (2006).
An eLearning website for the design and
analysis of experiments with application to
chemical processes. Proceedings of Compstat 2006, 1641-1649.
- Woods, D.C., Lewis, S.M., Eccleston, J.A. and Russell, K.G.
(2006). Designs for generalized linear models with several
variables and model uncertainty. Technometrics, 48, 284-292 (doi:10.1198/004017005000000571).
- Woods, D.C. and Lewis, S.M. (2006). All-bias designs for
polynomial spline regression models. Australian and New Zealand Journal of
Statistics, 48, 49-58 (doi:10.1111/j.1467-842X.2006.00424.x).
- Woods, D.C. (2005). Designing experiments under random
contamination with application to polynomial spline regression.
Statistica Sinica, 15, 619-635 (Statistica Sinica website).
- Grove, D.G., Woods, D.C. and Lewis, S.M. (2004). Multifactor
B-spline mixed models in designed experiments for the engine
mapping problem. Journal of Quality Technology, 36, 380-391 (JQT website).
- Woods, D.C., Lewis, S.M. and Dewynne, J.N. (2003). Designing
experiments for multi-variable B-spline models. Sankhya, 65, 660-677 (Sankhya website).
Invited Presentations (since 2015)
- 15 June 2018: Design of computational and physical experiments for uncertainty quantification. Isaac Newton Institute for Mathematical Sciences, Cambridge, UK.
- 7 June 2018: Closed-loop automatic experimentation for Bayesian optimisation. Research seminar, Department of Chemical and Biochemical Engineering, University of Cambridge, UK.
- 28 March 2018: Bayesian optimal design for ordinary differential equation models with application in biological science. Research seminar, Isaac Newton Institute for Mathematical Sciences, Cambridge, UK.
- 16 March 2018: Bayesian design of experiments using approximate coordinate exchange. Research seminar, University of Antwerp, Belgium.
- 2 March 2018: Closed-loop automatic experimentation for Bayesian optimisation. Research seminar, Universidad Carlos III de Madrid, Spain.
- 25 January 2018: Bayesian design of experiments using approximate coordinate exchange. Research seminar, Cambridge Biostatistics Unit, UK.
- 18 December 2017: Bayesian optimal design of experiments: review, challenges and examples. CMStats. London, UK.
- 16 November 2017: Bayesian design of experiments using approximate coordinate exchange. Research seminar, National Sun Yat-sen University, Kaohsiung, Taiwan.
- 11 October 2017: Bayesian design of experiments using approximate coordinate exchange. Research seminar, UCLA, Los Angeles, USA.
- 6 September 2017: Designing experiments for interaction screening. Royal Statistical Society Annual Meeting, Glasgow, UK.
- 8 August 2017: Closed-loop auotomatic experimentation for Bayesian optimisation. Latest Advances in the Theory and Applications of Design and Analysis of Experiments, Banff, Canada.
- 3 August 2017: Emulation of multivariate simulators using thin-plate splines with application to atmospheric dispersion. Joint Statistical Meetings, Baltimore, USA.
- 22 June 2017: Closed-loop automatic experimentation for Bayesian optimisation. Research seminar, Johannes Kepler University, Linz, Austria.
- 18 May 2017: Closed-loop automatic experimentation for Bayesian optimisation. Spring Research Conference, Rutgers University, New Brunswick, USA.
- 14 March 2017: Bayesian optimal design for physical models derived from ordinary differential equations. Workshop on Optimal Experimental Design and Inverse Problems, Alan Turing Institute, London, UK.
- 3 March 2017: Challenges in computing Bayesian designs for complex models. SIAM Computational Science and Engineering, Atlanta, USA.
- 17 December 2016: Closed-loop automatic experimentation for Bayesian optimisation. Conference on Experimental Design and Analysis, Taipei, Tawian.
- 12 September 2016: Bayesian design of experiments via Gaussian process emulation (with application to discrete responses). ENBIS, Sheffield, UK.
- 1 August 2016: Bayesian optimal design for physical models derived from ordinary differential equations. Joint Statistical Meetings, Chicago, USA.
- 8 March 2016: Bayesian design of experiments for industrial and scientific applications via Gaussian processes. Stu Hunter Conference, Waterloo, Canada. (References)
- 10 December 2015: Computation for Bayesian optimal design of experiments with a pharmaceutical case study. Bayesian Optimal Design of Experiments, Brisbane, Australia.
- 24 September 2015: Model-robust automatic experimentation for optimisation. 8th International Workshop on Simulation, Vienna, Austria.
- 27 July 2015: Emulation of multivariate simulators using thin plate splines with application to atmospheric dispersion. ISI World Statistics Congress, Rio de Janeiro, Brazil.
- 19 June 2015: Designed experiments for semi-parametric models and functional data with a case study in Tribology. Frontiers in Industrial Statistics, Tianjin, China.
- 14 May 2015: Bayesian design of experiments for Gaussian process regression. Research seminar, University of Glasgow, UK.
- 26 February 2015: Bayesian design of experiments via Gaussian process emulation. Research seminar, University of Exeter, UK.
- 12 February 2015: Bayesian design of experiments via Gaussian process emulation. Research seminar, University College Dublin, Ireland