Dave Woods

Dave Woods

Professor of Statistics at the University of Southampton

Head of Statistics in the School of Mathematical Sciences.

Invited presentations and seminars (since 2017)

  • 20 June 2022: Approximate Laplace importance sampling for Bayesian design for nonlinear models. Richard Boys Memorial Workshop, Newcastle, UK.
  • 20 December 2021: Design of experiments with functional independent variables. CMStats, London, UK and online.
  • 5 March 2021: Design of experiments with functional independent variables. SIAM Computational Science and Engineering, online.
  • 20 February 2020: Statistical learning through designed experiments. RSS Highlands Local Group, Aberdeen, UK.
  • 16 December 2019: Bayesian optimal design of experiments motivated by challenges from science and technology. CMStats, London, UK.
  • 18 October 2019: Statistical learning through designed experiments. Alan Turing Institute Workshop on Optimization & Machine Learning, Southampton, UK.
  • 9 July 2019: Statistical learning through designed experiments. Machine Learning and AI in (Bio)Chemical Engineering, Cambridge, UK.
  • 25 June 2019: Design of experiments with functional independent variables. mODa 12, Smolenice, Slovakia.
  • 21 May 2019: Design of experiments for the calibration of computational models. International Conference on the Design of Experiments, Memphis, USA.
  • 16 May 2019: Design of experiments for the calibration of computational models. Statistical Perspectives on Uncertainty Quantification, Chapel Hill, USA.
  • 11 May 2019: Designing industrial experiments with functional independent variables with pharmaceutical applications, WuFest, Atlanta, USA.
  • 4 September 2018: Designing industrial experiments with functional independent variables with pharmaceutical applications, Royal Statistical Society Annual Meeting, Cardiff, UK.
  • 30 July 2018: Design of experiments for the calibration of computational models. Joint Statistical Meetings, Vancouver, Canada.
  • 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.