معرفی
Daniel Wilson is Professor of Infectious Disease Genomics at the University of Oxford and Director of Studies in Data Science in the Department for Continuing Education. He is a Fellow of St. Cross College and leads a research group at the Big Data Institute within the Nuffield Department of Population Health. His work is supported by the Wellcome Trust and Robertson Foundation, with collaborations including the Modernising Medical Microbiology consortium and Oxford's Department of Statistics.
Education includes:
- BA in Biological Sciences from St. John's College, Oxford (2002)
- DPhil in Population Genetics from University of Oxford (2005)
- Postdoctoral research at University of Chicago and Lancaster University
Wilson's research integrates genomic epidemiology, pathogen evolution, and machine learning to study infectious diseases. His interests focus on bacterial/viral genomics, genetic susceptibility, and developing statistical methods for analyzing pathogen transmission and antimicrobial resistance. Key themes include the evolution of virulence, host-pathogen coevolution, and applications of whole-genome sequencing in public health.
Publications demonstrate strong emphasis on genomic epidemiology of bacterial pathogens (e.g., Staphylococcus, TB, meningococcus), COVID-19 host genetics, and development of novel statistical methods for genetic association studies. Recent work shows increasing focus on machine learning applications for source attribution of infections and antibiotic resistance prediction.
Scientific awards include:
- PLoS Computational Biology Research Prize (2017)
- Head of Nuffield Department Public Engagement Prize (2015)
- North Senior Scholarship (2004)
Wilson welcomes graduate students for part-time DPhil programs and leads multidisciplinary teams at the Big Data Institute. His group focuses on population genomics of pathogens and hosts, supported by major grants including Wellcome Trust and Robertson Foundation fellowships. Current work involves developing statistical frameworks for bacterial GWAS and transmission modeling.
