Statistical Science


David Watson

PositionPostdoctoral Research Fellow
Email (@ucl.ac.uk)david.watson
Personal webpagehttp://dswatson.github.io/
ThemesBiostatistics, Computational StatisticsMultivariate and High Dimensional Data

Photo of David Watson
Biographical Details

David is a Postdoctoral Research Fellow at the Department of Statistical Science. Prior to joining UCL, he earned his MSc and DPhil in computational social science at the University of Oxford while working as a Data Scientist for Queen Mary University's Centre for Translational Bioinformatics. David was a doctoral enrichment student at the Alan Turing Institute and is a founding member of Oxford's Digital Ethics Lab, where he maintains a Research Associate position.

Research Interests

Machine learning, causality, explainability, clustering, high-dimensional inference.

Selected publications

  • Kinney, D. & Watson, D. (2020). Causal feature learning for utility-maximizing agents. International Conference on Probabilistic Graphical Models.
  • Nicholls, H.L., John, C.R., Watson, D., Munroe, P.B., Barnes, M.R., & Cabrera, C.P. (2020). Reaching the end-game for GWAS: Machine learning approaches for the prioritization of complex disease loci. Frontiers in Genetics, 11, 350.
  • Watson, D. & Floridi, L. (2020). The explanation game: A formal framework for interpretable machine learning. Synthese.
  • John, C.R., Watson, D., Russ, D., Goldmann, K., Ehrenstein, M., Pitzalis, C., … Barnes, M. (2020). M3C: Monte Carlo reference-based consensus clustering. Scientific Reports, 10(1), 1816.
  • Watson, D. (2019). The rhetoric and reality of anthropomorphism in artificial intelligence. Minds & Machines, 29(3), 417-440.
  • John, C.R., Watson, D., Barnes, M.R., Pitzalis, C., & Lewis, M. (2019). Spectrum: Fast density-aware spectral clustering for single and multi-omic data. <em>Bioinformatics</em>, <em>36</em>(4), 1159–1166.
  • Watson, D. (2019). The price of discovery: A model of scientific research markets. In Öhman, C. & Watson, D. (Eds.), The 2018 Yearbook of the Digital Ethics Lab. Heidelberg: Springer.
  • Öhman, C. & Watson, D. (2019). Are the dead taking over Facebook? A big data approach to the future of death online. Big Data & Society, 6(1), 1-13.
  • Watson, D., Krutzinna, J., Bruce, I.N., Griffiths, C.E.M., McInnes, I.B., Barnes, M.R., & Floridi, L. (2019). Clinical applications of machine learning algorithms: Beyond the black box. BMJ, 364.
  • O’Toole, S.M., Watson, D., Novoselova, T.V., Romano, L.E.L., King, P., Bradshaw, T.Y., … Chapple, J.P. (2019). Oncometabolite induced primary cilia loss in pheochromocytoma. Endocrine-Related Cancer, 26(1), 165-180.
  • Watson, D. & Floridi, L. (2018). Crowdsourced science: Sociotechnical epistemology in the e-research paradigm. Synthese, 195(2), 741–764.
  • Foulkes, A.C., Watson, D., Carr, D.F., Kenny, J.G., Slidel, T., Parslew, R., … Barnes, M.R. (2018). A framework for multi-omic prediction of treatment response to biologic therapy for psoriasis. Journal of Investigative Dermatology.