Mr David Watson, B.Phil, MSc

Profile

David Watson is a Data Scientist at QMUL’s Centre for Translational Bioinformatics and a fourth-year DPhil candidate studying explainable artificial intelligence at the University of Oxford. He earned a BA in philosophy from Dartmouth College and an MSc in social data science from the University of Oxford. David joined the Centre for Translational Bioinformatics in 2015, working under Prof. Michael Barnes. He specialises in high-dimensional clustering and classification, as well as differential expression analysis and data visualization. He has served as an analyst on a number of IMID-Bio projects, including clinical trials of biologic therapies for psoriasis and rheumatoid arthritis. David is a founding member of Oxford’s Digital Ethics Lab and a regular contributor to The Economist. 

His current research focuses on the conceptual and technical foundations of machine learning, with an emphasis on applications in functional genomics.

Current position: 

• Data Scientist

Academic Background

• Research Associate: Oxford Internet Institute
• Enrichment Student: Alan Turing Institute
• DPhil Candidate in Machine Learning: University of Oxford
• MSc in Social Science of the Internet: University of Oxford
• Bachelor’s in Philosophy: Dartmouth College – USA

Research

Research Interests:

Areas

1. Machine learning

2. High-dimensional inference

3. Data visualisation

4. Clustering

5. Causal modelling

 

Projects

Psoriasis Stratification to Optimise Relevant Therapy (PSORT)

Rheumatoid Arthritis Map (RA-MAP)

Immune-Mediated Inflammatory Disease Biobanks (IMID-Bio)

Publications

Publications

Kinney, D. & Watson, D. (2020). Causal feature learning for utility-maximizing agents. Accepted to International Conference on Probabilistic Graphical Models. arXiv preprint, 2005.08792.

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. DOI: 10.3389/fgene.2020.00350.

Watson, D. & Floridi, L. (2020). The explanation game: A formal framework for interpretable machine learning. Synthese. DOI: 10.1007/s11229-020-02629-9.

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. DOI:  10.1038/s41598-020-58766-1.

Watson, D. (2019). The rhetoric and reality of anthropomorphism in artificial intelligence. Minds & Machines, 29(3), 417-440. DOI: 10.1007/s11023-019-09506-6.

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. Bioinformatics. DOI: 10.1093/bioinformatics/btz704.

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. DOI: 10.1007/978-3-030-17152-0_5.

Öhman, C. & Watson, D. (Eds.) (2019). The 2018 Yearbook of the Digital Ethics Lab. Heidelberg: Springer. DOI: 10.1007/978-3-030-17152-0.

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. DOI: 10.1177/2053951719842540.

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. DOI: 10.1136/bmj.l886.

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. DOI: 10.1530/ERC-18-0134.

Watson, D. & Floridi, L. (2018). Crowdsourced science: Sociotechnical epistemology in the e-research paradigm. Synthese, 195(2), 741–764. DOI: 10.1007/s11229-016-1238-2.

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. DOI: 10.1016/j.jid.2018.04.041.

Cabrera, C.P., Manson, J., Shepherd, J.M., Torrance, H.D., Watson, D., Longhi, M.P., … Brohi, K. (2017). Signatures of inflammation and impending multiple organ dysfunction in the hyperacute phase of trauma: A prospective cohort study. PLOS Medicine, 14(7), e1002352. DOI: 10.1371/journal.pmed.1002352.

Foulkes, A.C., Watson, D., Griffiths, C.E.M., Warren, R.B., Huber, W., & Barnes, M.R. (2017). Research techniques made simple: Bioinformatics for genome-scale biology. Journal of Investigative Dermatology, 137(9), e163–e168. DOI: 10.1016/j.jid.2017.07.095.