SF
Seth Flaxman
University of Oxford, Imperial College London, Carnegie Mellon University, Harvard University, World Health Organization
Research areas: Medicine · Ophthalmology · COVID-19 epidemiological studies · SARS-CoV-2 and COVID-19 Research · Gaussian Processes and Bayesian Inference
0000-0002-2477-4217
Employment
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University of Oxford Associate Professor2021 - Present
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Imperial College London senior lecturer2017 - 2021
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University of Oxford postdoc2015 - 2017
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World Health Organization data scientist2010 - 2011
Education
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Carnegie Mellon University PhD in machine learning and public policy2011 - 2015
- 2004 - 2008
Projects & Funding
Projects & funding information is unavailable.
Publications (39)
- Fast approximate Bayesian inference of HIV indicators using PCA adaptive Gauss-Hermite quadrature Save
- ‘Parenting with Hope’ program among bereaved families in Colombia: A pre-post and quasi-experimental evaluation Save
- Building Hope in Crisis: A Multi-Country Cluster Randomised Controlled Trial Evaluating a Psychosocial, Parenting, and Violence Prevention Intervention During War in Ukraine Save
- Trends in Pediatric Hospital Admissions Caused or Contributed by SARS-CoV-2 Infection in England Save
- Using LLMs for R Coding and Package Development Save
- Toward optimal disease surveillance with graph-based active learning Save
- Optimal disease surveillance with graph-based Active Learning Save
- Deep Learning and MCMC with aggVAE for Shifting Administrative Boundaries: Mapping Malaria Prevalence in Kenya Save
- Semi-mechanistic Bayesian modelling of COVID-19 with renewal processes Save
- Subnational HIV incidence trends in Malawi: large, heterogeneous declines across space Save
- Statistical Machine Learning for Researchers Save
- Statistical Machine Learning for Researchers Save
- Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation Save
- Bayesian Kernel Two-Sample Testing Save
- PriorVAE: encoding spatial priors with variational autoencoders for small-area estimation Save
- Covid-19 is a leading cause of death in children and young people ages 0-19 years in the United States Save
- Comparative analysis of the risks of hospitalisation and death associated with SARS-CoV-2 omicron (B.1.1.529) and delta (B.1.617.2) variants in England: a cohort study Save
- Unrepresentative big surveys significantly overestimated US vaccine uptake Save
- Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation Save
- COVID-19–Associated Orphanhood and Caregiver Death in the United States Save