David Watson is a doctoral candidate at the Oxford Internet Institute, where his research focuses on the epistemological foundations of machine learning. His interests fall at the intersection of philosophy, computer science, and sociology. He has written on a wide variety of topics, including quantum cosmology, crowdsourcing in the natural sciences, and efficient algorithms for unsupervised cluster detection. His most recent work concerns normative approaches to artificial intelligence research, with an emphasis on promoting open source analytics.
David received his MSc from the Oxford Internet Institute in 2015, studying under the supervision of Prof. Luciano Floridi. He went on to become a Data Scientist at Queen Mary University’s Centre for Translational Bioinformatics, where his responsibilities included identifying disease subtypes using high-throughput sequencing technologies and integrating multi-omic data for numerous stratified medicine projects. In addition to his academic work, David is a regular contributor to The Economist, where he writes articles and builds models for the Graphic Detail and Game Theory blogs.
- (2018) M3C: A Monte Carlo reference-based consensus clustering algorithm.
- (2018) "Sex differences in the nitrate-nitrite-NO ● pathway: role of oral nitrate-reducing bacteria", Free Radical Biology and Medicine. 126 113-121.
- (2018) "A framework for multi-omic prediction of treatment response to biologic therapy for psoriasis", Journal of Investigative Dermatology.
- (2018) "Bioinformatics for dermatology – why we should learn about code", British Journal of Dermatology. 178 (4) 984.
- (2017) "The RA-MAP Consortium: a working model for academia–industry collaboration", Nature Reviews Rheumatology. 14 (1) 53-60.
- (2017) "Research Techniques Made Simple: Bioinformatics for Genome-Scale Biology", Journal of Investigative Dermatology. 137 (9) e163-e168.
- (2017) "Signatures of inflammation and impending multiple organ dysfunction in the hyperacute phase of trauma: A prospective cohort study", PLOS Medicine Schreiber, M. (eds.). 14 (7) e1002352.
- (2016) "Crowdsourced Science: Sociotechnical Epistemology in the e-Research Paradigm", Synthese. 195 (2) 741-764.